Статті в журналах з теми "Vegetation and crops"
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1
Kurbanov, R. K., and N. I. Zakharova. "Application of Vegetation Indexes to Assess the Condition of Crops." Agricultural Machinery and Technologies 14, no. 4 (December 18, 2020): 4–11. http://dx.doi.org/10.22314/2073-7599-2020-14-4-4-11.
Повний текст джерелаАнотація:
Monitoring of the state of agricultural crops and forecasting the crops development begin with aerial photography using a unmanned aerial vehicles and a multispectral camera. Vegetation indexes are selected empirically and calculated as a result of operations with values of diff erent spectral wavelengths. When assessing the state of crops, especially in breeding, it is necessary to determine the limiting factors for the use of vegetation indexes.(Research purpose) To analyze, evaluate and select vegetation indexes for conducting operational, high-quality and comprehensive monitoring of the state of crops and the formation of optimal management decisions.(Materials and Methods) The authors studied the results of scientifi c research in the fi eld of remote sensing technology using unmanned aerial vehicles and multispectral cameras, as well as the experience of using vegetation indexes to assess the condition of crops in the precision farming system. The limiting factors for the vegetation indexes research were determined: a limited number of monochrome cameras in popular multispectral cameras; key indicators for monitoring crops required by agronomists. After processing aerial photographs from an unmanned aerial vehicle, a high-precision orthophotomap, a digital fi eld model, and maps of vegetation indexes were created.(Results and discussion) More than 150 vegetation indexes were found. Not all of them were created through observation and experimentation. The authors considered broadband vegetation indexes to assess the status of crops in the fi elds. They analyzed the vegetation indexes of soybean and winter wheat crops in the main phases of vegetation.(Conclusions) The authors found that each vegetative index had its own specifi c scope, limiting factors and was used both separately and in combination with other indexes. When calculating the vegetation indexes for practical use, it was recommended to be guided by the technical characteristics of multispectral cameras and took into account the index use eff ectiveness at various vegetation stages.
2
DUBEY, R. C., S. D. GAIKWAD, V. S. NAWATHE, R. G. DEKHANE, and S. N. BIDYANTA. "Spectral radiance characteristics and vegetative indices of crops -A ground based remote sensing technique." MAUSAM 46, no. 1 (January 1, 2022): 75–80. http://dx.doi.org/10.54302/mausam.v46i1.3186.
Повний текст джерелаАнотація:
The spectral radiance characteristics and vegetation indices like simple difference, ratio vegetation, normalised vegetation perpendicular vegetation transformed vegetation and tasseled cap transformation of mung been sunflower and groundnut crops at different growth stages have been studied. The experiment was conducted in post rainy season during 1990-91 in the farm of Agricultural College. Pune using hand held multi-spectral radiometer. The significance of spectral variation of radiance and vegetative indices with respect to the phenological stages are discussed.
3
Gupta, Surya, Sara Bonetti, Peter Lehmann, and Dani Or. "Limited role of soil texture in mediating natural vegetation response to rainfall anomalies." Environmental Research Letters 17, no. 3 (February 22, 2022): 034012. http://dx.doi.org/10.1088/1748-9326/ac5206.
Повний текст джерелаАнотація:
Abstract Evidence suggests that the response of rainfed crops to dry or wet years is modulated by soil texture. This is a central tenet for certain agronomic operations in water-limited regions that rely on spatial distribution of soil texture for guiding precision agriculture. In contrast, natural vegetation in climatic equilibrium evolves to form a dynamic assemblage of traits and species adapted to local climatic conditions, primarily precipitation in water-limited regions. For undisturbed landscapes, we hypothesize that natural vegetation responds to rainfall anomalies irrespectively of local soil texture whereas rainfed crops are expected to respond to texture-mediated plant available water. Earth system models (ESMs) often quantify vegetation response to drought and water stress based on traditional agronomic concepts despite fundamental differences in composition and traits of natural vegetation and crops. We seek to test the hypothesis above at local and regional scales to differentiate natural vegetation and rainfed crops response to rainfall anomalies across soil types and better link them to water and carbon cycles. We employed field observations and remote sensing data to systematically examine the response of natural and rainfed cropped vegetation across biomes and scales. At local scales (field to ∼0.1 km), we used crop yields from literature data and natural vegetation productivity as gross primary productivity (GPP) from adjacent FLUXNET sites. At regional scales (∼102 km), we rely exclusively on remote-sensing-based GPP. Results confirm a lack of response of natural vegetation productivity to soil texture across biomes and rainfall anomalies at all scales. In contrast, crop yields at field scale exhibit correlation with soil texture in dry years (in agreement with conventional agronomic practices). These results support the hypothesis that natural vegetation is decoupled from soil texture, whereas rainfed crops retain dependency on soil texture in dry years. However, the observed correlation of crops with soil texture becomes obscured at larger scales by spatial variation of topography, rainfall, and uncertainty in soil texture and GPP values. The study provides new insights into what natural vegetation’s climatic equilibrium might mean and reveals the role of scale in expressing such sensitivities in ESMs.
4
Ha, Thuan, Yanben Shen, Hema Duddu, Eric Johnson, and Steven J. Shirtliffe. "Quantifying Hail Damage in Crops Using Sentinel-2 Imagery." Remote Sensing 14, no. 4 (February 16, 2022): 951. http://dx.doi.org/10.3390/rs14040951.
Повний текст джерелаАнотація:
Hailstorms are a frequent natural weather disaster in the Canadian Prairies that can cause catastrophic damage to field crops. Assessment of damage for insurance claims requires insurance inspectors to visit individual fields and estimate damage on individual plants. This study computes temporal profiles and estimates the severity of hail damage to crops in 54 fields through the temporal analysis of vegetation indices calculated from Sentinel-2 images. The damage estimation accuracy of eight vegetative indices in different temporal analyses of delta index (pre-and post-hail differences) or area under curve (AUC) index (time profiles of index affected by hail) was compared. Hail damage was accurately quantified by using the AUC of 32 days of Normalized Difference Vegetation Indices (NDVI), Normalized Difference Water Index (NDWI), and Plant Senescence Radiation Index (PSRI). These metrics were well correlated with ground estimates of hail damage in canola (r = −0.90, RMSE = 8.24), wheat (r = −0.86, RMSE = 12.27), and lentil (r = 0.80, RMSE = 17.41). Thus, the time-series changes in vegetation indices had a good correlation with ground estimates of hail damage which may allow for more accurate assessment of the extent and severity of hail damage to crop land.
5
Atanasov, Asparuh, Radko Mihaylov, Svilen Stoyanov, Desislava Mihaylova, and Peter Benov. "Drone-based Monitoring of Sunflower Crops." ANNUAL JOURNAL OF TECHNICAL UNIVERSITY OF VARNA, BULGARIA 6, no. 1 (May 18, 2022): 1–9. http://dx.doi.org/10.29114/ajtuv.vol6.iss1.258.
Повний текст джерелаАнотація:
Remote monitoring and utilization of digital technologies is essential for the application of the precision farming approach, which contributes significantly to the improved quality of agricultural products. The paper compares the data for six vegetation indices when observing the sunflower vegetation in South Dobrudzha in 2021. Images with RGB and digital NIR camera were obtained via a remotely piloted quadcopter. The flight plan specifies speed 8 m/s, altitude 100 m and shooting overlapping images of 80%. Six vegetation indices: NDVI, EVI2, SAVI, CVI, MGVRI and MPRI were calculated from the images obtained during the flight. The calculation of the indices takes into account the intensity of solar radiation and the parameters of the meteorological situation at the time of shooting. The findings obtained reveal a stable trend of change of the vegetation indices, thus, establishing accurate and reliable results as for the monitoring of agricultural areas with unmanned aerial vehicles.
6
Бойко, І. І. "FORMATION OF QUALITY OF VEGETATIVE MASS OF ENERGY CROPS OF DIFFERENT VEGETATION DURATION." Bulletin of Uman National University of Horticulture 1 (August 2022): 3–7. http://dx.doi.org/10.31395/2310-0478-2022-1-3-7.
Повний текст джерелаАнотація:
The research results show that in the samples of switchgrass plants with different vegetation periods the dry matter content varied from 52.40% in the leaves of the 3rd year of vegetation to 77.15% in the leaves of plants of the 8th year of vegetation. In the leaves of miscanthus, the dry matter content varied from 59.35 to 62.30% depending on the duration of the growing season. This figure for energy willow was in the range of 61.23–66.12%. There is a general trend in the content of raw ash in various plant organs: more in the leaves and less in the stem in all variants of the studied plants. Thus, the ash content in switchgrass plants ranged from 1.2% in the stems of plants of the 8th year of vegetation to 4.5% in the leaves of plants of the 10th year of vegetation. The ash content in miscanthus plants ranged from 1.6% to 3.0%, and in willow samples - in the range of 1.9–3.5%. The accumulation of hemicellulose in bioenergetics plants occurs gradually during the growing season. In particular, a slightly higher content of hemicellulose was observing in the stems of bioenergetics plants, less in the leaves. Thus, in switchgrass plants the highest content of hemicellulose was in the stem – 22.65%, and in the leaves 22.75%. In miscanthus plants, the hemicellulose content in the stem ranged from 21.51 to 22.55%, and in the leaves from 21.49 to 22.30%. With regard to energy willow, the distribution of hemicellulose in the plant varied like switchgrass – less in the leaves and more in the stems. The accumulation and distribution of cellulose in bioenergetics plants was similar to hemicelluloses, the leaves were smaller comparing to the stems. In switchgrass plants, the highest cellulose content was in the stems – 42.03–45.49%, and in the leaves – 42.08–45.33%, depending on the duration of the growing season. The distribution of cellulose in switchgrass and energy willow plants changed similarly. Young plants have little lignin, but with age its amount in the tissues increases significantly. Plants accumulate the most lignin in the spring and less in the fall. The accumulation of lignin in samples of bioenergetics plants was different. Its greater content was in the leaves, and less in the stems. Thus, the plants of switchgrass had the highest amount of lignin in the leaves – 18.59% (plants of the 10th year of vegetation), the lowest in the leaves – 15.92% (plants of the 3rd year of vegetation), and in the stems, respectively, 18.02 and 15.90%.
7
Nowak, Sylwia, Arkadiusz Nowak, Marcin Nobis, and Agnieszka Nobis. "Weed vegetation of cereal crops in Tajikistan (Pamir Alai Mts., Middle Asia)." Phytocoenologia 43, no. 3-4 (June 1, 2013): 225–53. http://dx.doi.org/10.1127/0340-269x/2013/0043-0557.
Повний текст джерела
8
Barnes, Mallory Liebl, Landon Yoder, and Mahsa Khodaee. "Detecting Winter Cover Crops and Crop Residues in the Midwest US Using Machine Learning Classification of Thermal and Optical Imagery." Remote Sensing 13, no. 10 (May 20, 2021): 1998. http://dx.doi.org/10.3390/rs13101998.
Повний текст джерелаАнотація:
Cover crops are an increasingly popular practice to improve agroecosystem resilience to climate change, pests, and other stressors. Despite their importance for climate mitigation and soil health, there remains an urgent need for methods that link winter cover crops with regional-scale climate mitigation and adaptation potential. Remote sensing is ideally suited to provide these linkages, yet, cover cropping has not been analyzed extensively in remote sensing research. Methods used for remote sensing of crops from satellites traditionally leverage the difference between visible and near-infrared reflectance to isolate the signal of photosynthetically active vegetation. However, using traditional greenness indices like the Normalized Difference Vegetation Index (NDVI) for remotely sensing winter vegetation, such as winter cover crops, is challenging because vegetation reflectance signals are often confounded with reflectance of bare soil and crop residues. Here, we present new and established methods of detecting winter cover crops using remote sensing observations. We find that remote sensing methods that incorporate thermal data in addition to traditional reflectance metrics are best able to distinguish between winter farm management practices. We conclude by addressing the potential of existing and upcoming hyperspectral and thermal missions to further assess agroecosystem function in the context of global change.
9
Abou Ali, H., D. Delparte, and L. M. Griffel. "FROM PIXEL TO YIELD: FORECASTING POTATO PRODUCTIVITY IN LEBANON AND IDAHO." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W11 (February 14, 2020): 1–7. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w11-1-2020.
Повний текст джерелаАнотація:
Abstract. Idaho and Lebanon rely on potatoes as an economically important crop. NDVI (Normalized Difference Vegetation Index), GNDVI (Green Normalized Difference Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and MSAVI2 (Modified Soil Adjusted Vegetation Index 2) indices were calculated from PlanetScope satellite imagery for the 2017 growing season cloud free days. Variations in vegetation health were tracked over time and correlated to yield data provided by growers in Idaho. Based on ordinary least squares regression an Idaho yield forecast model was developed. Vegetation response during the growth stage at which potato tubers were filling out was significant in predicting yield for both the Norkotah and Russet potato variety. This corresponded to a week with high recorded temperatures that impacted the health status of the crops. The yield forecasting model was validated with a cross validation approach and then applied to potato fields in Lebanon. The Idaho model successfully displayed yield variation in crops for Lebanon. Spectral indices along with field topography allow the prediction of yield based on the crop type and variety.
10
Patel, J. H., and M. P. Oza. "Deriving crop calendar using NDVI time-series." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 869–73. http://dx.doi.org/10.5194/isprsarchives-xl-8-869-2014.
Повний текст джерелаАнотація:
Agricultural intensification is defined in terms as cropping intensity, which is the numbers of crops (single, double and triple) per year in a unit cropland area. Information about crop calendar (i.e. number of crops in a parcel of land and their planting & harvesting dates and date of peak vegetative stage) is essential for proper management of agriculture. Remote sensing sensors provide a regular, consistent and reliable measurement of vegetation response at various growth stages of crop. Therefore it is ideally suited for monitoring purpose. The spectral response of vegetation, as measured by the Normalized Difference Vegetation Index (NDVI) and its profiles, can provide a new dimension for describing vegetation growth cycle. The analysis based on values of NDVI at regular time interval provides useful information about various crop growth stages and performance of crop in a season. However, the NDVI data series has considerable amount of local fluctuation in time domain and needs to be smoothed so that dominant seasonal behavior is enhanced. Based on temporal analysis of smoothed NDVI series, it is possible to extract number of crop cycles per year and their crop calendar. <br><br> In the present study, a methodology is developed to extract key elements of crop growth cycle (i.e. number of crops per year and their planting – peak - harvesting dates). This is illustrated by analysing MODIS-NDVI data series of one agricultural year (from June 2012 to May 2013) over Gujarat. Such an analysis is very useful for analysing dynamics of kharif and rabi crops.
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Saqib, Hafiz Sohaib Ahmed, Minsheng You, and Geoff M. Gurr. "Multivariate ordination identifies vegetation types associated with spider conservation in brassica crops." PeerJ 5 (October 27, 2017): e3795. http://dx.doi.org/10.7717/peerj.3795.
Повний текст джерелаАнотація:
Conservation biological control emphasizes natural and other non-crop vegetation as a source of natural enemies to focal crops. There is an unmet need for better methods to identify the types of vegetation that are optimal to support specific natural enemies that may colonize the crops. Here we explore the commonality of the spider assemblage—considering abundance and diversity (H)—in brassica crops with that of adjacent non-crop and non-brassica crop vegetation. We employ spatial-based multivariate ordination approaches, hierarchical clustering and spatial eigenvector analysis. The small-scale mixed cropping and high disturbance frequency of southern Chinese vegetation farming offered a setting to test the role of alternate vegetation for spider conservation. Our findings indicate that spider families differ markedly in occurrence with respect to vegetation type. Grassy field margins, non-crop vegetation, taro and sweetpotato harbour spider morphospecies and functional groups that are also present in brassica crops. In contrast, pumpkin and litchi contain spiders not found in brassicas, and so may have little benefit for conservation biological control services for brassicas. Our findings also illustrate the utility of advanced statistical approaches for identifying spatial relationships between natural enemies and the land uses most likely to offer alternative habitats for conservation biological control efforts that generates testable hypotheses for future studies.
12
Luculescu, Marius Cristian, Luciana Cristea, Sorin Constantin Zamfira, and Ion Barbu. "Spectral Monitoring of the Crops Vegetation Status in Precision Agriculture." Applied Mechanics and Materials 811 (November 2015): 236–40. http://dx.doi.org/10.4028/www.scientific.net/amm.811.236.
Повний текст джерелаАнотація:
This paper proposes an analysis of spectral monitoring processes of the crop vegetation status. In terms of the extensive implementation of precision agriculture, the performant agricultural management must ensure the monitoring of crop vegetation. In this context, determination and interpretation of vegetation indices, based on spectral data, plays a very important role. The performed research revealed the characteristics of monitoring the status of vegetation in order to obtain the necessary information, ie structure, actions and performances required to achieve an efficient system of monitoring the state of vegetation resources and to obtain the application maps for a precision crop management.precision crop management. This complex system ensures data acquisition and processing, thematic and application maps realization and decision generating on obtaining large productions and quality, on optimizing the economic profits, achieving of an integrated environmental protection and increasing of the sustainability of agricultural systems.
13
Fang, Peng, Nana Yan, Panpan Wei, Yifan Zhao, and Xiwang Zhang. "Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery." Remote Sensing 13, no. 14 (July 13, 2021): 2755. http://dx.doi.org/10.3390/rs13142755.
Повний текст джерелаАнотація:
The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.
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Percival, David C., Dianne Stevens, Glen Sampson, Gary Patterson, and Klaus Jensen. "189 Vegetation Management of Lowbush Blueberries." HortScience 34, no. 3 (June 1999): 474F—474. http://dx.doi.org/10.21273/hortsci.34.3.474f.
Повний текст джерелаАнотація:
The influence of noninvasive, companion crops on lowbush blueberry production was examined at the Nova Scotia Wild Blueberry Inst. in 1998. A randomized complete-block experimental design was used with four replications and a plot size of 10 × 6 m. Treatments consisted of a control (no companion crop), sawdust, creeping red fescue, hard fescue, chewings fescue, sheeps fescue, birdsfoot trefoil (BFT), and redtop. Measurements of companion crop height, dry weight, and density, and lowbush blueberry vegetative and reproductive data were recorded. In addition, the effects of the companion crops on soil stability and weed pressures were measured at the conclusion of the growing season. Overall, the fescues and BFT established well within the blueberry canopy and in bare areas with plant densities ranging from 960 plants/m2 to 3500 plants/m2, plant dry weights of 7.2 to 11.7 mg/plant, and plant heights of 5.4 to 9.5 cm. The use of the companion crops increased yields with yields from the creeping red and hard fescue treatments being 9.0% and 13% greater, respectively, than the control. The creeping red and hard fescue treatments also significantly reduced weed pressures and increased soil stability. Therefore, using companion crops in lowbush blueberry production appears to be a viable management strategy with future research being required on herbicide use, fertility regimes, and harvestability.
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Jimenez-Sierra, David Alejandro, Edgar Steven Correa, Hernán Darío Benítez-Restrepo, Francisco Carlos Calderon, Ivan Fernando Mondragon, and Julian D. Colorado. "Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops." Sensors 21, no. 13 (June 25, 2021): 4369. http://dx.doi.org/10.3390/s21134369.
Повний текст джерелаАнотація:
Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.
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Lykhovyd, P. V. "Seasonal dynamics of normalized difference vegetation index in some winter and spring crops in the South of Ukraine." Agrology 4, no. 4 (2021): 187–93. http://dx.doi.org/10.32819/021022.
Повний текст джерелаАнотація:
Spatial crop monitoring using vegetation indices is one of the most promising technologies for crop mapping and remote phenological observations. The aim of the study was to determine the patterns of seasonal dynamics of the spatial normalized difference vegetation index for the main crops grown in the south of Ukraine and to connect it to their phenology. Remote sensing data provided by the OneSoil AI platform, which uses Sentinel-1 and Sentinel-2 imagery as a basis, was used to derive the monthly index values for the 2016–2021 growing season for nine selected crops grown in the experimental fields at the NAAS Institute of Irrigated Agriculture, Kherson, Ukraine. The fallow field was also included in the study to determine the cutoff values of the vegetation index, which are not representative of any healthy vegetation. It was determined that each crop has its unique pattern of the dynamics of the vegetation index, except for winter wheat and winter barley, which demonstrated quite similar models. The peak values of the vegetation index were observed in May for winter crops (wheat, barley, rapeseed) and early-spring crops (chickpea, peas), while the late-spring crops (grain corn, grain sorghum, soybeans, sunflower) reached the peak values in July. It is possible to suggest that the highest demand for mineral nutrition and watering will fall in the mentioned time periods of late spring and midsummer. Phenological monitoring revealed that the highest values of the spatial normalized difference vegetation index were observed in the following stages of crop growth, namely: winter wheat, winter barley – stem elongation; winter rapeseed – flowering; chickpea – branching; peas – budding and flowering; sunflower – stem growth; soybeans - pod formation; grain sorghum – panicle ejection and flowering; grain corn – panicle ejection and flowering. The results provide novel information for further implementation in the mathematical models for automation of crops recognition, mapping, and phenological observations based on the remote sensing data. Further scientific research in this direction will be aimed at increasing the spectrum of crops studied and a detailed investigation of the relationship between the value of the normalized difference vegetation index and their phenology.
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Dlamini, Mandla, George Chirima, Mbulisi Sibanda, Elhadi Adam, and Timothy Dube. "Characterizing Leaf Nutrients of Wetland Plants and Agricultural Crops with Nonparametric Approach Using Sentinel-2 Imagery Data." Remote Sensing 13, no. 21 (October 22, 2021): 4249. http://dx.doi.org/10.3390/rs13214249.
Повний текст джерелаАнотація:
In arid environments of the world, particularly in sub-Saharan Africa and Asia, floodplain wetlands are a valuable agricultural resource. However, the water reticulation role by wetlands and crop production can negatively impact wetland plants. Knowledge on the foliar biochemical elements of wetland plants enhances understanding of the impacts of agricultural practices in wetlands. This study thus used Sentinel-2 multispectral data to predict seasonal variations in the concentrations of nine foliar biochemical elements in plant leaves of key floodplain wetland vegetation types and crops in the uMfolozi floodplain system (UFS). Nutrient concentrations in different floodplain plant species were estimated using Sentinel-2 multispectral data derived vegetation indices in concert with the random forest regression. The results showed a mean R2 of 0.87 and 0.86 for the dry winter and wet summer seasons, respectively. However, copper, sulphur, and magnesium were poorly correlated (R2 ≤ 0.5) with vegetation indices during the summer season. The average % relative root mean square errors (RMSE’s) for seasonal nutrient estimation accuracies for crops and wetland vegetation were 15.2 % and 26.8%, respectively. There was a significant difference in nutrient concentrations between the two plant types, (R2 = 0.94 (crops), R2 = 0.84 (vegetation). The red-edge position 1 (REP1) and the normalised difference vegetation index (NDVI) were the best nutrient predictors. These results demonstrate the usefulness of Sentinel-2 imagery and random forests regression in predicting seasonal, nutrient concentrations as well as the accumulation of chemicals in wetland vegetation and crops.
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Мостіпан, М. І., та Н. Л. Умрихін. "Врожайність пшениці озимої залежно від погодних умов у ранньовесняний період в умовах північного Степу України". Вісник Полтавської державної аграрної академії, № 4 (28 грудня 2018): 62–69. http://dx.doi.org/10.31210/visnyk2018.04.09.
Повний текст джерелаАнотація:
Тривалими польовими дослідженнями доведено, що в північному Степу України чим пізніше відновлюється весняна веґетація озимої пшениці, тим меншою є врожайність. При цьому час відновлення веґетації має значний вплив на врожайність різновікових посівів. У разі надраннього відновлення веґетації (III декада лютого) врожайність посівів з сівбою у період з кінця серпня до початку жовтня є майже однаковою і становить від 6,44 до 6,96 т/га. У випадку пізнього відновлення веґетації (початок квітня) найбільш високу врожайність формують посіви з сівбою з 10 по 25 вересня. Їх врожайність у середньому за роки досліджень становила 3,86–3,91 т/га. Чим коротшим є період від переходу температури через 0 0С до +5 0С, тим більшою є врожайність озимої пшениці. У середньому за роки досліджень врожайність озимої пшениці за тривалості періоду від переходу температури через 0 0С до +5 0С до 10 днів становила 6,04 т/га, а в разі подовження цього періоду до 30 і більше днів зменшувалася до 3,76 т/га.
It has been established that in the Northern Steppe of Ukraine the beginning of spring vegetation of winter wheat starts at different periods of time. The earliest vegetation (February 22) was observed in 1990, and the latest vegetation (April 4) was in 2003. Therefore, it has been suggested to distinguish the very early (the third decade of February) beginning of vegetation, early beginning of vegetation (the first–second decade of March), middle-time vegetation (the third decade of March) and late beginning of vegetation (the first decade of April) of winter wheat. During the whole period of observations from 1986 to 2005, the very early beginning of vegetation was observed during 3 years (15%), early vegetation – 4 years (20%), middle-time vegetation – 8 years (40%), and late vegetation – 5 years (25%).
The analysis of winter wheat productivity shows that the later is the beginning of spring vegetation, the less productivity of winter wheat. During the very early spring vegetation in the third decade of February, productivity is twice as large as compared with the late vegetation in the first decade of April. On average, over the years of the study, these indicators were 6.74 and 3.28 t/ha respectively. In the very early vegetation (the 3rd decade of February), productivity of the mixed-age crops is almost the same and ranges from 6.44 to 6.96 t/ha. During the late vegetation (early April), the highest productivity is formed by the crops sown from the 10th to the 25th of September. Their average productivity during the years of the study was 3.86–3.91 t/ha. With this period of spring vegetation, the productivity of crops sown on September 2nd and October 2nd is almost the same and is 2.99 and 2.88 t/ha respectively, but significantly higher than the productivity of crops sown on August 25th.
For the formation of winter wheat harvest, the change of the average daily temperature above 0 °C is important, as well as the duration of the period from that time to the beginning of spring vegetation. That is the steady increase in average daily air temperature to more than +5 °С. The increase in the period of time from the date of the change of the average daily air temperature above 0 °C to the beginning of spring vegetation causes the decrease in the productivity of winter wheat. During the years when the duration of this period was up to 10 days, the productivity of winter wheat averaged 6.04 t/ha, and during the years with this period of more than 30 days, the productivity decreased to 3.76 t/ha.
The shorter period from the change of the average daily air temperature above 0 °C to the time of the beginning of spring vegetation, the higher the dependence of the level of winter wheat productivity on the sowing terms. If this period is longer than 30 days, the highest productivity was formed by crops sown on September 17th and September 25th, and during the years when this period lasted from 10 to 20 days, higher productivity was provided by the crops sown from September 10th to September 25th.
With the duration of the period from the date of the change of the average daily air temperature above 0 °C to the beginning of spring vegetation to 20 days, the productivity of winter wheat crops with early sowing on September 2nd and October 2nd is almost the same. The crops sown on September 2nd with the duration of this period up to 10 days formed productivity of 5.44 t/ha, and the crops sown on October 2nd – 5.56 t/ha. At the same time, if the duration of this period exceeds 20 days, the crops sown on October 2nd form a considerably higher productivity than the crops sown on September 2nd.
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Lykhovyd, Pavlo V., Raisa A. Vozhehova, Sergiy O. Lavrenko, and Nataliya M. Lavrenko. "The Study on the Relationship between Normalized Difference Vegetation Index and Fractional Green Canopy Cover in Five Selected Crops." Scientific World Journal 2022 (March 21, 2022): 1–6. http://dx.doi.org/10.1155/2022/8479424.
Повний текст джерелаАнотація:
Crop models are of great use and importance in modern agriculture. Most models imply spatial vegetation indices, such as NDVI, or canopy cover characteristics, such as FGCC, to provide estimation of crops conditions and forecast productivity. The purpose of the study was to (1) determine the possibility of mutual conversion between spatial NDVI and Canopeo-derived FGCC in five crops (grain corn, sunflower, tomato, millet, and winter wheat) and (2) estimate the precision of such a conversion. The data set of the study was formed by the OneSoil AI derived satellite imagery on NDVI for the studied crops in different stages of their growing season combined with Canopeo-processed photographs of vegetating crops in the field with FGCC percentage calculation. The sets of NDVI and FGCC values were paired up and then statistically processed to obtain polynomial equations of NDVI into FGCC and inverse conversion for each crop. The results of the study revealed that mutual conversion between spatial NDVI and Canopeo-derived FGCC is possible. There is a strong direct correlation (R2 within 0.6779–0.9000 depending on the crop) between the studied indices for all crops. Close-growing crops, especially winter wheat, showed the highest correlation, while row crops and especially tomatoes had a less strong relationship between vegetation indices. The models for mutual conversion between FGCC and NDVI could be incorporated into the yield simulation models to improve the forecasting capacities.
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Gómez Gómez, Robin, María Isabel González Lutz, Renán Agüero Alvarado, Ramón Mexzón Vargas, Franklin Herrera Murillo, and Ana María Rodríguez Ruiz. "Conocimiento sobre coberturas vivas y disposición a utilizarlas por productores de varios cultivos." Agronomía Mesoamericana 28, no. 2 (April 30, 2017): 489. http://dx.doi.org/10.15517/ma.v28i2.23403.
Повний текст джерелаАнотація:
The objective of this study was to assess the knowledge on cover crops and native vegetation mulches and the willingness to implement them by papaya, oil palm, and banana producers in Costa Rica. An evaluation instrument with twenty eight questions to be answered as true or false was developed, and it was used to yield a knowledge indicator. Seven additional questions with responses on a scale from 0 to 5 were included to explore producers’ willingness to implement cover crops or native vegetation mulches on their farms. The evaluation was completed in 2014, and was filled out by 36 papaya producers, 30 oil palm producers, and 57 banana producers. Item analyses to determine reliability produced Cronbach’s alpha values above 90%. For this study a factors analysis was performed in order to determine the measurement of one single variable, knowledge on cover crops and native vegetation mulches. Global knowledge scores varied signi cantly between producer groups. Banana producers assessments yielded the highest mean with the lowest variability, whereas papaya producers had the lower mean and the highest variability. Likewise, answers to each of the questions differed importantly between producer groups. It was also determined that producers of these crops are willing to implement and get training on cover crops and native vegetation mulches.
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Sharma, Shashank, and F. M. Prasad. "Accumulation of Lead and Cadmium in Soil and Vegetable Crops along Major Highways in Agra (India)." E-Journal of Chemistry 7, no. 4 (2010): 1174–83. http://dx.doi.org/10.1155/2010/678589.
Повний текст джерелаАнотація:
Environmental pollution of heavy metals from automobiles has attained much attention in the recent past. The present research was conducted to study Pb and Cd level concentrations in soil and vegetations along a major highway with high traffic density. Soil and vegetable samples along highway were collected from 10 sites in Agra district (India) and analyzed for two heavy metals (lead and cadmium) using flame atomic absorption spectrophotometer (AAS). The soil physicochemical properties were also determined. The general decrease in concentrations of these metals with distance from the highway indicates their relation to traffic. Higher accumulations of metals have been observed on vegetation and soil samples near to the highway (0-5 m) than on vegetation and soil samples from sites a little farther away ( at 5-10 m & 10-15 m). This is attributed mainly to aerial deposition of the metal particulates from motor vehicles. The values of heavy metals were compared with results found by other investigators in various countries worldwide.
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Shurlaeva, E. S., K. E. Tokarev, and B. Kh Sanzhapov. "Satellite monitoring and visualization of vegetation indices for assessing crop productivity." Journal of Physics: Conference Series 2060, no. 1 (October 1, 2021): 012018. http://dx.doi.org/10.1088/1742-6596/2060/1/012018.
Повний текст джерелаАнотація:
Abstract The article discusses approaches to the use of remote sensing and satellite monitoring tools for assessing the productivity of agricultural crops based on the recognition of high-resolution aerial photographs, followed by numerical calculation of vegetation indices and visualization in cross-platform geoinformation systems.
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Emelyanov, Dmitriy, Irina Botvich, and Anatoly Shevyrnogov. "Crop and Soil Temperature Difference an Additional Factor for Analysis of the Condition of Crops." E3S Web of Conferences 333 (2021): 01004. http://dx.doi.org/10.1051/e3sconf/202133301004.
Повний текст джерелаАнотація:
The study aims to study changes in land surface temperature (LST) of soil and vegetation on agricultural land planted with barley based on unmanned LST data. Simultaneously with the LST data, the spectral characteristics (NDVI) of crops were measured using the DJI P4 Multispectral. The paper shows the variability of vegetation indices and radiation temperature during the growing season. A significant relationship was found between the dynamics of NDVI and the dynamics of radiation temperature. The features of the variability of the spatial distribution of temperatures depending on precipitation are shown. The paper gives an example of a temperature map of the studied areas in the middle of the growing season, which shows the features of the spatial distribution of temperatures.
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Wijesingha, Jayan, Supriya Dayananda, Michael Wachendorf, and Thomas Astor. "Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters." Sensors 21, no. 8 (April 20, 2021): 2886. http://dx.doi.org/10.3390/s21082886.
Повний текст джерелаАнотація:
Various remote sensing data have been successfully applied to monitor crop vegetation parameters for different crop types. Those successful applications mostly focused on one sensor system or a single crop type. This study compares how two different sensor data (spaceborne multispectral vs unmanned aerial vehicle borne hyperspectral) can estimate crop vegetation parameters from three monsoon crops in tropical regions: finger millet, maize, and lablab. The study was conducted in two experimental field layouts (irrigated and rainfed) in Bengaluru, India, over the primary agricultural season in 2018. Each experiment contained n = 4 replicates of three crops with three different nitrogen fertiliser treatments. Two regression algorithms were employed to estimate three crop vegetation parameters: leaf area index, leaf chlorophyll concentration, and canopy water content. Overall, no clear pattern emerged of whether multispectral or hyperspectral data is superior for crop vegetation parameter estimation: hyperspectral data showed better estimation accuracy for finger millet vegetation parameters, while multispectral data indicated better results for maize and lablab vegetation parameter estimation. This study’s outcome revealed the potential of two remote sensing platforms and spectral data for monitoring monsoon crops also provide insight for future studies in selecting the optimal remote sensing spectral data for monsoon crop parameter estimation.
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GORYANINA, Tatiana A. "POTENTIAL PRODUCTIVITY OF WINTER CROPS IN THE MIDDLE VOLGA REGION." Periódico Tchê Química 17, no. 36 (December 20, 2020): 1004–15. http://dx.doi.org/10.52571/ptq.v17.n36.2020.1019_periodico36_pgs_1004_1015.pdf.
Повний текст джерелаАнотація:
The study of winter crop cultivars was carried out in the breeding fields of the Samara Agricultural Research Institute, located in the steppe zone of the Middle Volga region, in the nursery of competitive testing in 2002-2019. For calculations, 5 varieties of winter rye, 6 varieties of winter triticale, and 2 varieties of winter wheat were taken. For scientific justification, the authors calculated the potential productivity (Yp), the actual possible potential yield (Ypp a), the maximum possible potential yield (Ypp m), the bioclimatic potential (BCP), and correlation analysis. The study aims to calculate the possible yield of winter crops to substantiate the data obtained scientifically. In the dry conditions of Bezenchuk, the maximum yield of triticale was obtained in 2017 – 7.48 t/ha, rye – 5.88 t/ha, and in 2016 for wheat – 4.65 t/ha. Potential productivity, taking into account ΣT>10 °C for the vegetation period of the crop, for triticale in 2017, 3.02 t/ha, for winter rye in 2005, 6.83 t/ha, for winter wheat in 2005-2.79 t/ha. The variation of the indicator (BCP) over the years reached significantly higher values from 0.62 to 1.16 points for winter rye, from 0.30 to 0.60 for winter triticale and winter wheat. The trend of the interrelations between yield is observed with the length of the vegetation period, with a set of climatic conditions for the springsummer period. The triticale vegetation duration depends on the precipitation in May and on the set of conditions in June. The winter rye vegetation duration depends on the temperatures during the sowing-germination period and on the sum of active temperatures during vegetation.
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Jagadeeswaran, R., A. Poornima, and R. Kumaraperumal. "Mapping and classification of crops using high resolution satellite image." Journal of Applied and Natural Science 10, no. 3 (August 8, 2018): 818–25. http://dx.doi.org/10.31018/jans.v10i3.1710.
Повний текст джерелаАнотація:
In the present study an attempt was made to perform land use land cover classification at Level-III in order to discriminate and map individual crops. IRS Resources at 2 LISS IV sensor imagery (5.0 m spatial resolution) of September 2014 was utilized for the study. A hybrid classification approach of unsupervised classification followed by supervised classification was adopted to identify and map the crop area in Kodumudi block, Erode district of Tamil Nadu. Signature evaluation was carried out to study the class separability and through cross tabulation and the accuracy was assessed by error matrix. The signature separability analysis to classify various land cover classes indicated that the class viz., waterbody, settlement, sandy area and fallow land were better and for vegetation sub-classes viz., individual crops were poor, which means classification of individual crops was a challenge. The overall accuracy with three different algorithms varied from 56 to 65 per cent and this low accuracy was due to the problem in discriminating the tonal variation and spectral pattern of individual crops in the study area. Thus, classification of vegetation categories into individual crops using LISS IV data resulted in moderate classification accuracy in areas with multiple cropping.
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Carreño-Conde, Francisco, Ana Elizabeth Sipols, Clara Simón de Blas, and David Mostaza-Colado. "A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)." Applied Sciences 11, no. 4 (February 20, 2021): 1859. http://dx.doi.org/10.3390/app11041859.
Повний текст джерелаАнотація:
Vegetation dynamics is very sensitive to environmental changes, particularly in arid zones where climate change is more prominent. Therefore, it is very important to investigate the response of this dynamics to those changes and understand its evolution according to different climatic factors. Remote sensing techniques provide an effective system to monitor vegetation dynamics on multiple scales using vegetation indices (VI), calculated from remote sensing reflectance measurements in the visible and infrared regions of the electromagnetic spectrum. In this study, we use the normalized difference vegetation index (NDVI), provided from the MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor. NDVI is frequent in studies related to vegetation mapping, crop state indicator, biomass estimator, drought monitoring and evapotranspiration. In this paper, we use a combination of forecasts to perform time series models and predict NDVI time series derived from optical remote sensing data. The proposed ensemble is constructed using forecasting models based on time series analysis, such as Double Exponential Smoothing and autoregressive integrated moving average with explanatory variables for a better prediction performance. The method is validated using different maize plots and one olive plot. The results after combining different models show the positive influence of several weather measures, namely, temperature, precipitation, humidity and radiation.
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Choroś, T., T. Oberski, and T. Kogut. "UAV IMAGING AT RGB FOR CROP CONDITION MONITORING." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1521–25. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1521-2020.
Повний текст джерелаАнотація:
Abstract. Modern techniques such as precision agriculture tasks are provided to intentional fertilization, pesticide dosing or simply watering the crops. These tasks need to be continuously monitored. One of known method for analyzing the crops conditions is calculating the vegetation indexes. This paper focuses on purpose of using images made with UAV equipped with ordinary non-metric digital RGB camera. The methods had been taken revealed easy to use and cost effective. We present an experiment which attend to distinguish different crops conditions on two test fields sowed with wheat and rape. For this purpose, two different RGB based vegetation indexes were analyzed. The results of calculated indexes shown how crops differs in each stage of vegetation. During the first stage (germinating) the plants are green and average TGI is low. It increases at second stage (flowering) because of plant flowers, which partly cover the leaves. At last stage (ripening) TGI decreases, so plants are still green but starting to dry and change their color.
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Ma, Xu, Lei Lu, Jianli Ding, Fei Zhang, and Baozhong He. "Estimating Fractional Vegetation Cover of Row Crops from High Spatial Resolution Image." Remote Sensing 13, no. 19 (September 28, 2021): 3874. http://dx.doi.org/10.3390/rs13193874.
Повний текст джерелаАнотація:
With high spatial resolution remote sensing images being increasingly used in precision agriculture, more details of the row structure of row crops are captured in the corresponding images. This phenomenon is a challenge for the estimation of the fractional vegetation cover (FVC) of row crops. Previous studies have found that there is an overestimation of FVC for the early growth stage of vegetation in the current algorithms. When the row crops are a form in the early stage of vegetation, their FVC may also have overestimation. Therefore, developing an algorithm to address this problem is necessary. This study used World-View 3 images as data sources and attempted to use the canopy reflectance model of row crops, coupling backward propagation neural networks (BPNNs) to estimate the FVC of row crops. Compared to the prevailing algorithms, i.e., empirical method, spectral mixture analysis, and continuous crop model coupling BPNNs, the results showed that the calculated accuracy of the canopy reflectance model of row crops coupling with BPNNs is the highest performing (RMSE = 0.0305). Moreover, when the structure is obvious, we found that the FVC of row crops was about 0.5–0.6, and the relationship between estimated FVC of row crops and NDVI presented a strong exponential relationship. The results reinforced the conclusion that the canopy reflectance model of row crops coupled with BPNNs is more suitable for estimating the FVC of row crops in high-resolution images.
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Kolomiets, S. S., O. M. Nechaj, O. V. Turaieva, and O. V. Hnelytsia. "THE METHOD OF STUDYING WATER CONSUMPTION OF CROPS IN FIELD VEGETATION EXPERIMENTS." Міжвідомчий тематичний науковий збірник "Меліорація і водне господарство", no. 2 (December 12, 2019): 96–104. http://dx.doi.org/10.31073/mivg201902-186.
Повний текст джерелаАнотація:
Facing the global climate change, the study of the water consumption of new varieties and hybrids of crops becomes an urgent problem because of the need for economical use of available water resources in the production process and adaptation of agro-technologies to climate change. The purpose of the research is to study the patterns of water consumption of new varieties of crops on the basis of instrumental daily measurement of the dynamics of components of the total water consumption of crops – evaporation and transpiration under different systems of their fertilizers and different levels of soil moisture. The developed method of instrumental measurement of the components of total water consumption is based on the use of ceramic membranes to automatically maintain different levels of soil moisture in the field vegetation experience under the influence of natural climatic factors. A two-factor vegetation experiment allows a comparative analysis of the structure of water consumption of crops at different levels of soil moisture and different fertilizer systems. The constructive decisions and history of creation and formation of field vegetation experiments with controlled level of soil moisture supply, their advantages and disadvantages are presented. Since 2017, the field of vegetation field experiments has functioned on a permanent basis in the village of Gora, Boryspil district, Kiev region. The advantage of these experiments is the high reliability of the obtained patterns, which provides a sufficient number of replicates in each variant; instrumental measurement of constituents of water consumption – evaporation from soil, total water consumption and, by difference, transpiration of plants that can be monitored on a daily basis, and even on an hourly basis; the dual action of the moisture supply device allows both to supply water to the soil and to divert excess water after precipitation to the storage tanks, which prevents anaerobiosis in the soil. According to the results of the conducted experiments, regularities of fundamental character were established: the ratio of evaporation and transpiration during the period of vegetation of crops was quantified; the influence of different fertilizer systems on the components of total water consumption of crops has been reliably established, in particular the influence of microbiological preparations and the participation of soil biota in the water consumption have been proved; factor analysis proved the equivalence of moisture supply factors (38 %) and fertilizer (36 %) on buckwheat grain productivity; under conditions of guaranteed moisture supply, soil biota improves soil fertility parameters. Conclusions. The fundamental result of vegetation experiments is the reliable establishment of the influence of different fertilizer systems on the components of water consumption of crops and in particular microbiological preparations, the participation of soil biota in soil moisture consumption, as well as its positive effect on the growth of the parameters of soil fertility substance, most likely due to the development of micro- and mesobiota (algae, moss, etc.) under conditions of guaranteed soil moisture. The method of conducting field vegetation experiments with regulation of soil moisture level developed at the Institute of Water Problems and Land Reclamation is indispensable for instrumental study of the patterns of evaporation and transpiration during the growing season of agricultural crops, which are further used in the scenario modeling of agrotechnological technologies. also rainfed agriculture for long-term forecasts of security and the growing season, which is aimed at the economical use of moisture in the production process.The possibility of a direct comparative assessment of water consumption of different new varieties and hybrids of crops at different levels of soil moisture in the field vegetation experiment remains unrealized.Field vegetation experiment has a high demonstration and educational potential for teaching undergraduate and graduate students.
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Вершинина, Oksana Vershinina, Васин, and Vasiliy Vasin. "THE PRODUCTIVITY OF PEAS BY GROWTH STIMULATORS FERTIGRAIN APPLICATION." Bulletin Samara State Agricultural Academy 1, no. 3 (July 28, 2016): 3–10. http://dx.doi.org/10.12737/20326.
Повний текст джерелаАнотація:
The purpose of research is the development of peas productivity increasing ways in the conditions of Middle Volga forest steppe. Results of researches during 2013-2015 with an assessment of photosynthetic activity indexes, structure of harvest, productivity and fodder advantages of peas by different methods of crops preseeding processing and are given by biogrowth factors Noktin and Fertigrain. The largest square of leaves 45.0-47.4 thousand sq.m/ha is formed in a phase of blossoming peas on options at an inoculation of seeds by Rizotorfin and Rizotorfin + Fertigrain Start with after-treatment of crops with biostimulator Fertigrain Foliar in a phase of 4-6 leaves. Photosynthetic potential of crops for vegetation was made without processing of seeds and crops on vegetation of 1.275 million sq.m/ha in a days, when processing seeds preparations Fertigrain Start it raises to 1.305 million sq.m/ha in a days. Net productivity of a photosynthesis reaches maximum in options with processing of seeds Noktin + Fertigrain Start and Rizotorfin + Fertigrain Start and processing of crops on vegetation with an index 4.00-4.09 g/m2 days. The conducted researches showed that all options of processings of seeds and crops increase the efficiency of peas. The greatest productivity of peas 2.04 t/ha and 2.12 t/ha is reached on the crops processed with preparation Fertigrain Foliar in budding phase against processing of seeds preparations Rizotorfin + Fertigrain Start and Noktin + Fertigrain Start. These options differ also in the best fodder advantages with collecting nonvolatile solid 1.82-1.90 t/ha, the feed protein units 2.32-2.41 thousand/ha and an exit of an exchange energy 23.35 - 24.27 GDzh/ha. Results of the conducted researches Noktin and Fertigrain allow todraw the conclusion for application effectiveness of preseeding inoculation of seeds and processing of crops vegetation by preparations.
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Aliyev, Elchin, and Fuad Salmanov. "FUZZY APPROACH TO FORECASTING THE DYNAMICS OF VEGETATION INDICES." International Scientific Technical Journal "Problems of Control and Informatics" 2, no. 4 (March 1, 2022): 53–68. http://dx.doi.org/10.34229/2786-6505-2022-2-4.
Повний текст джерелаАнотація:
Modern technologies for satellite monitoring of the Earthʼs surface provide agricultural producers with useful information about the health status of crops. The remote sensorʼs ability to detect subtle differences in vegetation makes it a useful tool for quantifying variability within a given field, estimating crop growth, and managing land based on current conditions. Remote sensing data, collected on a regular basis, allows producers and agronomists to draw up a current vegetation map that reflects the condition and strength of crops, analyze the dynamics of changes in plant condition, and predict yields in a particular area under crops. To interpret these data, the most effective means are various vegetation indices calculated empirically, that is, by operations with different spectral ranges of satellite monitoring multispectral data. Based on the time series of one of these vegetation indices, the paper considers the annual dynamics of the development of a plant culture in a particular field. The possibility of predicting the yield of the given crop is considered based on fuzzy modeling of time series for the corresponding spectral ranges of vegetation reflection obtained from satellite monitoring images. The proposed fuzzy models of time series are investigated for adequacy and suitability in terms of analyzing the features of the intra-annual of average long-term dynamics of the vegetation index, typical for the given area under crop.
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Susantoro, Tri Muji, Ketut Wikantika, Agung Budi Harto, and Deni Suwardi. "Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)." HAYATI Journal of Biosciences 26, no. 3 (December 2, 2019): 117. http://dx.doi.org/10.4308/hjb.26.3.117.
Повний текст джерелаАнотація:
This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport.
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Wang, Dong, Dongxia Yue, Yanyan Zhou, Feibiao Huo, Qiong Bao, and Kai Li. "Drought Resistance of Vegetation and Its Change Characteristics before and after the Implementation of the Grain for Green Program on the Loess Plateau, China." Remote Sensing 14, no. 20 (October 14, 2022): 5142. http://dx.doi.org/10.3390/rs14205142.
Повний текст джерелаАнотація:
Drought affects the growth and productivity of vegetation, and the analysis of drought resistance of vegetation can help ecological and environmental protection and sustainable development in drought-prone areas. The Loess Plateau (LP) is a drought-prone area in China with an extremely fragile ecological environment. This study analyzed the drought resistance of vegetation across different climate regions and vegetation biotypes, explored the characteristics of changes in vegetation drought resistance before and after the implementation of the Grain for Green Program (GGP), and evaluated the relative contribution of climatic factors and human activities to the change in drought resistance of vegetation. The following conclusions are obtained. (1) The drought resistance of vegetation on the LP basically showed a spatial pattern of increasing from northwest to southeast with the degree of aridity. The vegetation in the semi-humid and arid regions showed the strongest and weakest drought resistance, respectively. (2) The drought resistance of vegetation on the LP mainly showed an increasing trend since the GGP was implemented, but there were differences in different climatic zones. In semi-humid regions, the drought resistance of vegetation mainly showed a weakening trend, while in arid and semi-arid regions, it mainly showed an increasing trend. There were differences between vegetation biotypes as well; the drought resistance of forest and grassland showed a different trend in different climatic zones, while that of crops in all climatic zones showed an increasing trend. In the area with cropland returned, the drought resistance tended to increase where crops turned to forests, but the area where crops turned to grassland showed a weakening trend. (3) The positive contribution of climate change and human activities leads to the enhancement of drought resistance of vegetation in most areas of the LP, and the weakening of drought resistance of vegetation in semi-humid regions is dominated by the negative contribution of climate change. The negative contribution of human activities is the main reason for the decrease in drought resistance of vegetation in the area of returning cropland to grassland. This study can provide a reference for ecological protection and high-quality development of the LP.
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Tyšer, Luděk, Michaela Kolářová, Ondřej Tulačka, and Pavel Hamouz. "Weed vegetation in conventional and organic farming in West Bohemia (Czech Republic)." Plant, Soil and Environment 67, No. 7 (July 13, 2021): 376–82. http://dx.doi.org/10.17221/6/2021-pse.
Повний текст джерелаАнотація:
The paper presents species richness and composition of arable weed vegetation in the region of West Bohemia (Czech Republic) in different types of farming (conventional and organic) and grown crops (winter and spring cereals, wide-row crops). During the field survey in the years 2007 to 2017, 105 phytocoenological relevés were recorded. The average species richness in one relevé was significantly higher in organic farming, as well as total weed cover. The lowest species richness was found in wide-row crops. Recently widespread species belonged to the most frequent species in our study. Based on multivariate statistics, the effects of variables on the occurrence of weed species were found as statistically significant. Most of the variability in data was explained by crop, following by type of farming. Weed species of Fabaceae Lindl. family (especially Vicia L.) and many perennial species positively correlated with the organic type of farming. Endangered species were found mainly in organic farming and cereals. Less intensive cultivation with a higher weed cover is beneficial for the promotion of biodiversity.
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Ali, Abid, Roberta Martelli, Flavio Lupia, and Lorenzo Barbanti. "Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices." Remote Sensing 11, no. 20 (October 15, 2019): 2384. http://dx.doi.org/10.3390/rs11202384.
Повний текст джерелаАнотація:
Assessing crop yield trends over years is a key step in site specific management, in view of improving the economic and environmental profile of agriculture. This study was conducted in a 11.07 ha area under Mediterranean climate in Northern Italy to evaluate the spatial variability and the relationships between six remotely sensed vegetation indices (VIs) and grain yield (GY) in five consecutive years. A total of 25 satellite (Landsat 5, 7, and 8) images were downloaded during crop growth to obtain the following VIs: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Green Normalized Difference Vegetation Index (GNDVI), Green Chlorophyll Index (GCI), and Simple Ratio (SR). The surveyed crops were durum wheat in 2010, sunflower in 2011, bread wheat in 2012 and 2014, and coriander in 2013. Geo-referenced GY and VI data were used to generate spatial trend maps across the experimental field through geostatistical analysis. Crop stages featuring the best correlations between VIs and GY at the same spatial resolution (30 m) were acknowledged as the best periods for GY prediction. Based on this, 2–4 VIs were selected each year, totalling 15 VIs in the five years with r values with GY between 0.729** and 0.935**. SR and NDVI were most frequently chosen (six and four times, respectively) across stages from mid vegetative to mid reproductive growth. Conversely, SAVI never had correlations high enough to be selected. Correspondence analysis between remote VIs and GY based on quantile ranking in the 126 (30 m size) pixels exhibited a final agreement between 64% and 86%. Therefore, Landsat imagery with its spatial and temporal resolution proved a good potential for estimating final GY over different crops in a rotation, at a relatively small field scale.
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Rodimtsev, S. A., N. E. Pavlovskaya, S. V. Vershinin, I. V. Gorkova, and I. N. Gagarina. "The use of the vegetative index NDVI to predict grain crop yields." Bulletin of NSAU (Novosibirsk State Agrarian University), no. 4 (January 12, 2023): 56–67. http://dx.doi.org/10.31677/2072-6724-2022-65-4-56-67.
Повний текст джерелаАнотація:
The need for unified approaches to determining the phenological phase of a reliable indicator of the vegetative index is one of the critical problems of early forecasting of crop yields using satellite monitoring. Several works of domestic and foreign researchers formulate different estimates of the correlation relationship between NDVI and yield. This study aimed to obtain predictive models for the product of winter wheat and spring barley using indicators that are adequate for formalizing the tasks of predicting the trend section of the vegetative index NDVI of crops within the experimental farm of the Oryol State Agrarian University. Based on the analysis of the dynamics of the vegetation index NDVI, based on multi-year studies, the maximum mean annual values of the vegetation index, 0.72 for winter wheat and 0.56 for spring barley, were determined.The maximum NDVI values of the 2021 season for these crops are 0.78 and 0.58. It was found that the peaks of NDVI values correspond to the earing phase of crops with possible variation from 1 to 13 days. The correlation coefficients between the maximum values of NDVI and productivity of crops were 0.79 and 0.75 for winter wheat and spring barley, respectively, which suggests the possibility of reliable prediction of crop yield based on the data of their peak NDVI values. The authors obtained predictive crop yield models based on polynomial (second-degree) functions. A reliable yield forecast expands the scope of reasonable estimates and the implementation of plans aimed at the progressive development of the individual farm. Furthermore, it contributes to the food security of Russia as a whole.
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Troiachenko, R. M. "Control of segetal vegetation of potato crops under herbicides application." Taurian Scientific Herald 2, no. 116 (2020): 74–78. http://dx.doi.org/10.32851/2226-0099.2020.116.2.11.
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Luculescu, Marius Cristian, Luciana Cristea, Sorin Constantin Zamfira, and Ion Barbu. "Mechatronic System for Spectral Monitoring of the Crops Vegetation Status." Applied Mechanics and Materials 823 (January 2016): 405–10. http://dx.doi.org/10.4028/www.scientific.net/amm.823.405.
Повний текст джерелаАнотація:
This paper presents a mechatronic solution for monitoring the crops vegetation status. A data acquisition system containing different types of sensors (multispectral, temperature, plant height, GPS) is placed on a terrestrial mechatronic platform that is carried by a tractor or on an UAV (Unmanned Aerial Vehicle). The multispectral sensor offers information about the reflectance, for discrete wavelengths, necessary to compute the so called vegetation indices. They are correlated with the degree of development and plant health, thermal and water stress, pests, fertilizer need and so on. Knowing these information, a timely intervention is possible, allowing to supply water, pesticide and fertilizer in the proper quantity, at the proper time and in the precise place, leading to major economic impact and significant environmental protection. Geographical information are used for geo-referencing the acquired data, so that the thematic maps to be generated.
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Bagrikova, Natalia. "Syntaxonomy of weed vegetation of crops cultures of the Crimea." Chornomorski Botanical Journal 1, no. 2 (October 1, 2005): 47–58. http://dx.doi.org/10.14255/2308-9628/05.12/4.
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Vozhehova, Raisa, Mykola Maliarchuk, Iryna Biliaieva, Pavlo Lykhovyd, Anastasiia Maliarchuk, and Anatoliy Tomnytskyi. "Spring Row Crops Productivity Prediction Using Normalized Difference Vegetation Index." Journal of Ecological Engineering 21, no. 6 (August 1, 2020): 176–82. http://dx.doi.org/10.12911/22998993/123473.
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Leemans, R., and G. J. van den Born. "Determining the potential distribution of vegetation, crops and agricultural productivity." Water, Air, and Soil Pollution 76, no. 1-2 (July 1994): 133–61. http://dx.doi.org/10.1007/bf00478338.
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Bell, Jordan R., Esayas Gebremichael, Andrew L. Molthan, Lori A. Schultz, Franz J. Meyer, Christopher R. Hain, Suravi Shrestha, and K. Cole Payne. "Complementing Optical Remote Sensing with Synthetic Aperture Radar Observations of Hail Damage Swaths to Agricultural Crops in the Central United States." Journal of Applied Meteorology and Climatology 59, no. 4 (April 2020): 665–85. http://dx.doi.org/10.1175/jamc-d-19-0124.1.
Повний текст джерелаАнотація:
AbstractThe normalized difference vegetation index (NDVI) has been frequently used to map hail damage to vegetation, especially in agricultural areas, but observations can be blocked by cloud cover during the growing season. Here, the European Space Agency’s Sentinel-1A/1B C-band synthetic aperture radar (SAR) imagery in co- and cross polarization is used to identify changes in backscatter of corn and soybeans damaged by hail during intense thunderstorm events in the early and late growing season. Following a June event, hail-damaged areas produced a lower mean backscatter when compared with surrounding, unaffected pixels [vertical–vertical (VV): −1.1 dB; vertical–horizontal (VH): −1.5 dB]. Later, another event in August produced an increase in co- and cross-polarized backscatter (VV: 0.7 dB; VH: 1.7 dB) that is hypothesized to result from the combined effects of crop growth, change in structure of damaged crops, and soil moisture conditions. Hail damage regions inferred from changes in backscatter were further assessed through coherence change detections to support changes in the structure of crops damaged within the hail swath. While studies using NDVI have routinely concluded a decrease in NDVI is associated with damage, the cause of change with respect to the damaged areas in SAR backscatter values is more complex. Influences of environmental variables, such as vegetation structure, vegetation maturity, and soil moisture conditions, need to be considered when interpreting SAR backscatter and will vary throughout the growing season.
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Moraes, Victor H., Pedro R. Giongo, Marcio Mesquita, Thomas J. Cavalcante, Matheus V. A. Ventura, Estevam M. Costa, and Bruno H. T. Arantes. "Analysis of the Impact of Land Use and Occupation on the Biophysical Variables of the Cerrado Biome in Southwest Goiano, Brazil." Journal of Agricultural Science 11, no. 1 (December 15, 2018): 399. http://dx.doi.org/10.5539/jas.v11n1p399.
Повний текст джерелаАнотація:
The change in the use of natural vegetation by annual or perennial crops, sugarcane and fast-growing forests causes changes in the biophysical variables, and these changes can be monitored by remote sensing. The objective of this work was to evaluate, on a temporal scale, the impacts of land use changes on biophysical variables in the county of Santa Helena de Goias-Goias/Brazil. Between the years of 2000 to 2015 areas were identified for agricultural crops 1 (annual crops), water, agricultural crops 2 (sugarcane), natural vegetation, pasture and urban areas. The MODIS (Moderate Resolution Spectroradiometer) sensor products were selected for study: MOD11A2-Surface temperature; MOD16A2-Real evapotranspiration, MOD13Q1-Enhanced Vegetation Index and rainfall data from TRMM (Tropical Rainfall Measuring Mission). The geographic coordinates referring to the land uses were inserted in the LAPIG platform, searching the information of the biophysical variables referring to the selected pixel. The impact of land use change was evaluated by calculating the weighted average through the quantitative classification of the areas. It is concluded for the period of study that the index of average vegetation of the county had increase. There was an increase in the evapotranspiration volume of the county from 28% from 2000 to 2013 and the average surface temperature of the county showed a reduction of 2 °C in the period from 2000 to 2015.
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Sánchez, N., J. M. Lopez-Sanchez, B. Arias-Pérez, R. Valcarce-Diñeiro, J. Martínez-Fernández, J. M. Calvo-Heras, A. Camps, A. González-Zamora, and F. Vicente-Guijalba. "NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 101–7. http://dx.doi.org/10.5194/isprsarchives-xli-b1-101-2016.
Повний текст джерелаАнотація:
A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.
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Sánchez, N., J. M. Lopez-Sanchez, B. Arias-Pérez, R. Valcarce-Diñeiro, J. Martínez-Fernández, J. M. Calvo-Heras, A. Camps, A. González-Zamora, and F. Vicente-Guijalba. "NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B1 (June 2, 2016): 101–7. http://dx.doi.org/10.5194/isprs-archives-xli-b1-101-2016.
Повний текст джерелаАнотація:
A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.
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Martyniuk, I. V., Ya S. Tsymbal, M. M. Ptashnik, R. V. Ilchuk, and N. I. Martyniuk. "Efficiency of control of segetal vegetation in oats in organic agriculture." Agriculture and plant sciences: theory and practice, no. 1 (May 17, 2022): 17–23. http://dx.doi.org/10.54651/agri.2022.01.02.
Повний текст джерелаАнотація:
The article analyzes the results of studies conducted in the stationary experiment of NSC «Institute of Agriculture NAAS» to determine the impact of different systems of basic tillage on weed agrocenosis of oats in single and mixed crops with legumes (oats + diaper). The aim of the research is to establish the influence of different systems of basic tillage on the level of weediness of crops. The technology of growing crops in the experiment is generally accepted and recommended for the research area. The obtained research results convincingly show that from the replacement of fallow plowing by 20–22 cm by autumn disc cultivation by 10–12 cm weediness of oat crops at the time of its tillering increased from 15 to 25 pieces/m2 on average during the research or 40%. It was found that sowing oat-diaper mixture with 25% bean component, regardless of the system of main tillage, is an effective phytocoenotic agro-measure of control of segetal vegetation and reduces by 9–20% weed binary crops compared to monocotyledons. The priority of application of additional agrotechnical measure of control of segetal vegetation in agrocenosis of oats by carrying out spring pre-emergence loosening of soil by non-shelf implements by 6–8 cm was proved, which allowed to significantly reduce weed infestation. In particular, the spring pre-emergence loosening against the background of fallow plowing and autumn shallow disc cultivation weed reduction of oat crops decreased by 18 and 25% and 20 and 26%, respectively.
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Ermitão, Tiago, Célia M. Gouveia, Ana Bastos, and Ana C. Russo. "Vegetation Productivity Losses Linked to Mediterranean Hot and Dry Events." Remote Sensing 13, no. 19 (October 6, 2021): 4010. http://dx.doi.org/10.3390/rs13194010.
Повний текст джерелаАнотація:
Persistent hot and dry conditions play an important role in vegetation dynamics, being generally associated with reduced activity. In the Mediterranean region, ecosystems are adapted to such conditions. However, prolonged and intense heat and drought or the occurrence of compound hot and dry events may still have a negative impact on vegetation activity. This work aims to study how the productivity of Mediterranean vegetation is affected by hot and dry events, examining a set of severe episodes that occurred in three different regions (Iberian Peninsula, Eastern Mediterranean and Western Europe) between 2001 and 2019. The analysis relies on remote sensing products, namely Gross Primary Production from MODIS to detect and monitor vegetative stress and LST from MODIS and SM from ESA CCI to evaluate the influence of temperature and soil water availability on stressed vegetation. Of all events, the 2005 episode in the Iberian Peninsula was the most significant, affecting large sectors of low tree cover areas and crops and leading to reductions of annual plant productivity in affected vegetation of ~47 TgC/year. The obtained results highlight the influence of land-atmosphere coupling on vegetation productivity and clarified the role of warm springs on vegetation activity and soil moisture that may amplify summer temperatures. The functional recovery of affected vegetation productivity after these episodes varied across events, ranging from months to years. This work highlights the influence of hot and dry events on vegetation productivity in the Mediterranean basin and the usefulness of remote-sensing products to assess the response of different land covers to such episodes.
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García, Pedro M. Garc�, Gilberto F. Castro, Inelda A. Martillo, and Maikel Y. L. V� Vázquez. "Redesign of a drone (UAV) to obtain high flight autonomy, used in the analysis of Pitahaya crops based on neutrosophic control." International Journal of Neutrosophic Science 19, no. 3 (2022): 53–62. http://dx.doi.org/10.54216/ijns.190306.
Повний текст джерелаАнотація:
The use of drones stands out in precision agriculture for the analysis of vegetation and soil indices, the present work contemplates a redesign, construction and implementation of a drone using computer tools based on software engineering and technologies of info-communications, which allows optimizing one of the existing platforms in the drone market (SKYWALKER (X8)) for the evaluation of vegetation indices, as estimators of changes in different types of vegetation cover in Pitahaya crops in the province del Guayas, also carry out precise monitoring of large extensions of crops, minimizing human presence, controlling soil conditions through special systems, such as hydration, temperature or plant growth rate, chlorophyll level, among others, and the appearance of plagues that could affect the Pitahaya crops located prematurely, as well as the bases for a neutrosophic control system in designing platforms by using simulators. For the neutrosophic control, neutrosophic uninorms were used for the aggregation of the measurement results by regions.
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Марюшко, Максим В’ячеславович, Руслан Едуардович Пащенко та Наталія Сергіївна Коблюк. "МОНІТОРИНГ СІЛЬСЬКОГОСПОДАРСЬКИХ КУЛЬТУР ІЗ ЗАСТОСУВАННЯМ КОСМІЧНИХ ЗНІМКІВ SENTINEL-2". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 1 (23 березня 2019): 99–108. http://dx.doi.org/10.32620/reks.2019.1.11.
Повний текст джерелаАнотація:
The subject of the study in the article is the growing need for the use of spatial information for efficient agricultural production, due to the growing tendency of Earth remote sensing data accessibility, which, due to the spatial and temporal resolution improvement, can be used in the land cover analysis and other related jobs. The goal is to review the obtaining process of satellite multispectral space imagery from Sentinel-2 and to consider the possibility of their use for monitoring crops during the entire vegetation phase. The tasks: to study the modern needs of agricultural producers in the field of analysis of land cover occupied by agricultural crops; the analysis of the European Space Agency programs and the global land program Copernicus, which uses spatial information from Sentinel-2 for use in the agricultural sector; estimation of the constellation characteristics of Sentinel-2, imaging equipment and remote sensing data processing results by ground services received from Internet services; the use of Sentinel-2 multispectral space imagery for monitoring crops during the entire vegetation phase. The following results were obtained. After analyzing agricultural producers needs and the European Space Agency program, the feasibility of using multispectral space images taken by the Multispectral Instrument installed on satellites Sentinel-2 was established. Free access to the space imagery database is provided through the Copernicus Open Access Hub Internet Service. For the researched territory, Poltava region, Chutov district, the village of Vilkhovatka, various time space images were obtained and the normalized difference vegetation index (NDVI) was calculated. Histogram analysis of the obtained vegetation index values distribution within a single field (corn to grain) allows to reveal a quantitative and qualitative change in biomass, indicating a change in the vegetative phase. Conclusions. The approach described in this paper allows to conduct monitoring of the cropping state during the vegetation phase using both qualitative – visual analysis and quantitative – NDVI index, criteria. The change in the values of the normalized difference vegetation index can reveal a change in the biomass state. However, for calculating the NDVI index, data from near-infrared and red channels is needed, which complicates the acquisition of the original image. Therefore, in order to obtain the quantitative criteria in subsequent jobs, it is expedient to consider the possibility of using fractal dimension, which will reduce the amount of input data required for calculations.