Journal articles on the topic 'Leaf level data'
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Kumar, L. "High-spectral resolution data for determining leaf water content inEucalyptusspecies: leaf level experiments." Geocarto International 22, no. 1 (March 2007): 3–16. http://dx.doi.org/10.1080/10106040701204396.
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Castro-Esau, K. "Discrimination of lianas and trees with leaf-level hyperspectral data." Remote Sensing of Environment 90, no. 3 (April 15, 2004): 353–72. http://dx.doi.org/10.1016/j.rse.2004.01.013.
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Ely, Kim S., Alistair Rogers, Deborah A. Agarwal, Elizabeth A. Ainsworth, Loren P. Albert, Ashehad Ali, Jeremiah Anderson, et al. "A reporting format for leaf-level gas exchange data and metadata." Ecological Informatics 61 (March 2021): 101232. http://dx.doi.org/10.1016/j.ecoinf.2021.101232.
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Abdel-Rahman, Elfatih M., Mike Way, Fethi Ahmed, Riyad Ismail, and Elhadi Adam. "Estimation of thrips (Fulmekiola serrataKobus) density in sugarcane using leaf-level hyperspectral data." South African Journal of Plant and Soil 30, no. 2 (June 2013): 91–96. http://dx.doi.org/10.1080/02571862.2013.803616.
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Mzinyane, Thamsanqa D., Jan van Aardt, and Fethi Ahmed. "Estimation of Merchantable Volume of Eucalyptus Clones Based on Leaf-Level Hyperspectral Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 6 (June 2015): 3095–106. http://dx.doi.org/10.1109/jstars.2015.2400573.
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Fernández, Claudio I., Brigitte Leblon, Ata Haddadi, Jinfei Wang, and Keri Wang. "Potato Late Blight Detection at the Leaf and Canopy Level Using Hyperspectral Data." Canadian Journal of Remote Sensing 46, no. 4 (June 2, 2020): 390–413. http://dx.doi.org/10.1080/07038992.2020.1769471.
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Neinavaz, Elnaz, Andrew K. Skidmore, Roshanak Darvishzadeh, and Thomas A. Groen. "LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 20, 2016): 99–105. http://dx.doi.org/10.5194/isprs-archives-xli-b7-99-2016.
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Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 < R<sup>2</sup> < 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.
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Neinavaz, Elnaz, Andrew K. Skidmore, Roshanak Darvishzadeh, and Thomas A. Groen. "LEAF AREA INDEX RETRIEVED FROM THERMAL HYPERSPECTRAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 20, 2016): 99–105. http://dx.doi.org/10.5194/isprsarchives-xli-b7-99-2016.
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Leaf area index (LAI) is an important essential biodiversity variable due to its role in many terrestrial ecosystem processes such as evapotranspiration, energy balance, and gas exchanges as well as plant growth potential. A novel approach presented here is the retrieval of LAI using thermal infrared (8–14 μm, TIR) measurements. Here, we evaluate LAI retrieval using TIR hyperspectral data. Canopy emissivity spectral measurements were recorded under controlled laboratory conditions using a MIDAC (M4401-F) illuminator Fourier Transform Infrared spectrometer for two plant species during which LAI was destructively measured. The accuracy of retrieval for LAI was then assessed using partial least square regression (PLSR) and narrow band index calculated in the form of normalized difference index from all possible combinations of wavebands. The obtained accuracy from the PLSR for LAI retrieval was relatively higher than narrow-band vegetation index (0.54 < R<sup>2</sup> < 0.74). The results demonstrated that LAI may successfully be estimated from hyperspectral thermal data. The study highlights the potential of hyperspectral thermal data for retrieval of vegetation biophysical variables at the canopy level for the first time.
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Xavier, Alexandre Cândido, and Carlos Alberto Vettorazzi. "Monitoring leaf area index at watershed level through NDVI from Landsat-7/ETM+ data." Scientia Agricola 61, no. 3 (June 2004): 243–52. http://dx.doi.org/10.1590/s0103-90162004000300001.
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Leaf area index (LAI) is an important parameter of the vegetation canopy, and is used, for instance, to estimate evapotranspiration, an important component of the hydrological cycle. This work analyzed the relationship between LAI, measured in field, and NDVI from four dates (derived from Landsat-7/ETM+ data), and with such vegetation index, to generate and analyze LAI maps of the study area for the diverse dates. LAI data were collected monthly in the field with LAI-2000 equipment in stands of sugar cane, pasture, corn, eucalypt, and riparian forest. The relationships between LAI and NDVI were adjusted by a potential model; 57% to 72% of the NDVI variance were explained by the LAI. LAI maps generated by empirical relationships between LAI and NDVI showed reasonable precision (standard error of LAI estimate ranged from 0.42 to 0.87 m² m-2). The mean LAI value of each monthly LAI map was shown to be related to the total precipitation in the three previous months.
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Ni, Zhuoya, Zhigang Liu, Hongyuan Huo, Zhao-Liang Li, Françoise Nerry, Qingshan Wang, and Xiaowen Li. "Early Water Stress Detection Using Leaf-Level Measurements of Chlorophyll Fluorescence and Temperature Data." Remote Sensing 7, no. 3 (March 20, 2015): 3232–49. http://dx.doi.org/10.3390/rs70303232.
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MIFTAHUDDIN, YUSUP, SOFIA UMAROH, and ADLEO MALIK YAMANI. "Peningkatan Random Forest dengan menerapkan GLCM (Gray Level Co-Occurence Matrix) pada Klasifikasi Leaf Blast Tumbuhan Padi." MIND Journal 7, no. 1 (June 29, 2022): 37–50. http://dx.doi.org/10.26760/mindjournal.v7i1.37-50.
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ABSTRAKPenyakit leaf blast disebabkan oleh jamur yang bernama Pyricularia Grisea yang dapat menginfeksi daun padi dan menyebabkan gejala penyakit seperti bercak yang berbentuk seperti belah ketupat yang berwarna coklat yang dapat mengakibatkan kematian pada tanaman. Tingkat penyebaran penyakit leaf blast sudah meluas hingga di Indonesia yakni pada sentra-sentra produksi padi. Penelitian dilakukan untuk mengidentifikasi Daun Padi dengan ekstraksi ciri GLCM dan klasifikasinya dengan menerapkan metode Random Forest. Jumlah data uji sebanyak 200 yang terdiri dari 100 data daun padi sehat dan 100 data daun padi berpenyakit leaf blast. Penelitian menguji keberhasilan identifikasi penyakit leaf blast dan tidak berpenyakit leaf blast. Pengujian dilakukan dengan berbagai skema yaitu 40 data uji, 80 data uji, 120 data uji, 160 data uji dan 200 data uji. Pengujian menghasilkan nilai akurasi optimal pada data uji 200 sebesar 65%, recall 65%, precision 64% dan F-measure 65% dengan rata – rata pengujian waktu klasifikasi Random Forest sebesar 0.3522s.Kata kunci: Leaf blast, Random Forest, Padi, GLCM ABSTRACTLeaf blast is a disease caused by a fungus called Pyricularia Grisea which can infect rice leaves and cause disease symptoms such as brown rhombus-shaped spots that can cause plant death. The level of spread of leaf blast disease has spread to Indonesia, namely in rice production centers. The research was conducted to identify Rice Leaf with GLCM feature extraction and classification by applying the Random Forest method. The number of test data was 200 consisting of 100 data of healthy rice leaves and 100 data of rice leaves with leaf blast disease. The study tested the success of identification of leaf blast disease and not leaf blast disease. The tests were carried out with various schemes, namely 40 test data, 80 test data, 120 test data, 160 test data and 200 test data. The test resulted in the optimal accuracy value on the 200 test data of 65%, recall 65%, precision 64% and F-measure 65% with an average testing time of Random Forest classification of 0.3522sKeywords: Leaf blast, Random Forest, Gray-level Cooncurrence Matrix, GLCM
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Gao, Jianmeng, Mingliang Ding, Qiuyu Sun, Jiayu Dong, Huanyi Wang, and Zhanhong Ma. "Classification of Southern Corn Rust Severity Based on Leaf-Level Hyperspectral Data Collected under Solar Illumination." Remote Sensing 14, no. 11 (May 26, 2022): 2551. http://dx.doi.org/10.3390/rs14112551.
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Maize is one of the most important crops in China, and it is under a serious, ever-increasing threat from southern corn rust (SCR). The identification of wheat rust based on hyperspectral data has been proved effective, but little research on detecting maize rust has been reported. In this study, full-range hyperspectral data (350~2500 nm) were collected under solar illumination, and spectra collected under solar illumination (SCUSI) were separated into several groups according to the disease severity, measuring height and leaf curvature (the smoothness of the leaf surface). Ten indices were selected as candidate indicators for SCR classification, and their sensitivities to the disease severity, measuring height and leaf curvature, were subjected to analysis of variance (ANOVA). The better-performing indices according to the ANOVA test were applied to a random forest classifier, and the classification results were evaluated by using a confusion matrix. The results indicate that the PRI was the optimal index for SCR classification based on the SCUSI, with an overall accuracy of 81.30% for mixed samples. The results lay the foundation for SCR detection in the incubation period and reveal potential for SCR detection based on UAV and satellite imageries, which may provide a rapid, timely and cost-effective detection method for SCR monitoring.
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Yang, Bin, Hui Lin, and Yuhao He. "Data-Driven Methods for the Estimation of Leaf Water and Dry Matter Content: Performances, Potential and Limitations." Sensors 20, no. 18 (September 21, 2020): 5394. http://dx.doi.org/10.3390/s20185394.
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Leaf equivalent water thickness (EWT) and dry matter content (expressed as leaf mass per area (LMA)) are two critical traits for vegetation function monitoring, crop yield estimation, and precise agriculture management. Data-driven methods are widely used for remote sensing of leaf EWT and LMA because of their simplicity, satisfactory accuracy, and computation efficiency, such as the vegetation indices (VI)-based and machine learning (ML)-based methods. However, most of the data-driven methods are utilized at the canopy level, comparison of the performances of the data-driven methods at the leaf level has not been well documented. Moreover, the ML-based data-driven methods generally adopt leaf optical properties directly as their inputs, which may subsequently decrease their ability in remote sensing of leaf biochemical constituents. Performances of the ML-based methods cooperating with VI are rarely evaluated. Using the independent LOPEX and ANGERS datasets, we compared the performances of three data-driven methods: VI-based, ML-reflectance-based, and ML-VI-based methods, for the estimation of leaf EWT and LMA. Three sampling strategies were also utilized for evaluation of the generalization of these data-driven methods. Our results evidenced that ML-VI-based methods were the most accurate among these data-driven methods. Compared to the ML-reflectance-based and VI-based methods, the ML-VI-based model with support vector regression overall reduced errors by 5.7% (41.5%) and 1.8% (12.4%) for the estimation of leaf EWT (LMA), respectively. The ML-VI-based model inherits advantages of vegetation indices and ML techniques, which made it sensitive to changes of leaf biochemical constituents and capable of solving nonlinear tasks. It is thus recommended for the estimation of EWT and LMA at the leaf level. Moreover, its performance can further be enhanced by improving its generalization ability, such as adopting techniques on the selection of better wavelengths and definition of new vegetation indices. These results thus provided a prior knowledge of the data-driven methods and can be helpful for future studies on the remote sensing of leaf biochemical constituents.
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Apriyani, Dwi, Rita Nurmalina, and Burhanuddin Burhanuddin. "Bullwhip Effect Study in Leaf Organic Supply Chain." AGRARIS: Journal of Agribusiness and Rural Development Research 7, no. 1 (January 15, 2021): 1–10. http://dx.doi.org/10.18196/agraris.v7i1.9842.
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The mismatch between the availability of vegetables and consumer demand is one of the causes of inefficient supply chains. This study aims to analyze the bullwhip effect on the organic leaf vegetable supply chain at PT Simply Fresh Organic (SFO). The analysis method used is a comparison between the coefficient of variation of orders created with the coefficient of variation in requests received by each supply chain institution. The data used are secondary data obtained from PT SFO. The measurement results show that the supply chain flow of organic leaf vegetables had a bullwhip effect at the PT SFO level and no bullwhip effect occurs at the retail level. The value of the BE supply chain value calculation at PT SFO shows a higher figure than at the retail level. The bullwhip effect at PT SFO occurred because of a rationing and shortage gaming policy. Therefore, each member of the supply chain must maintain transparency of data information and utilize digital technology to improve the accuracy of data forecasting requests and reservations quickly.
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Fida, Rachmadiarti, Asri Mahanani, Sahani Kandilia Sari, Nella Yulia, and Nafidiastri Farah Aisyah. "Analysis of Lead (Pb) in Leaf of Tabebuia aurea from Polluted Air." MATEC Web of Conferences 372 (2022): 07001. http://dx.doi.org/10.1051/matecconf/202237207001.
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One of the three major heavy metals which can be harmful to plants, animals, and humans is lead (Pb). Smoke from gasoline fueled motor vehicles become the sources of these pollutants. In plants, including Tabebuia aurea can be found in the roads that are often passed by vehicles, so lead exposure is unavoidable.The purpose of this research was 1) to analyze the lead levels in T. aurea leaves, 2) to analyze the chlorophyll levels in T. aurea leaves, 3) to analyze the growth (leaf area). The Pb level in plant leaves was calculated using AAS (Atomic Absorption Spectrophotometer), chlorophyll level using spectrophotometer, growth was measured with leaf meter. Data were analyzed by descriptive and Anova. Based on the research and analysis that have been carried out can be concluded that 1) Pb metal levels 0.09 – 0.187 mg/L, 2) leaf chlorophyll levels ranges from 2.719 – 7.594 mg/L, and 3) Leaf area ranges from 186.720 – 199.288 cm2.Analysis with Anova ahows that the sampling location affected the Pb and chlorophyll content in the leaves,while the location did not affect the surface area of T. aurea leaves. The results of this research indicate that T. aurea can be used as a plant to absorb Pb pollutants in the air.
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Umam, Khoirul, and Eko Heri Susanto. "Classification Of Rice Leaf Color Into Leaf Color Chart Using LAB Color Space." CCIT Journal 13, no. 2 (August 27, 2020): 168–74. http://dx.doi.org/10.33050/ccit.v13i2.1008.
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Leaf Color Chart (LCC) is a measurement tool that can be used to measure the color intensity of rice leaves. The function of these measurements is to find out how many doses of fertilizer are needed by rice plants. However, readings made by human vision have a high level of subjectivity and risk of error. Therefore we need a method that can minimize errors and the level of subjectivity. One method that can be done is to classify the green color of rice leaves using LAB color space. Rice leaf image taken using a smartphone device is then extracted in RGB format. The color is then converted to LAB color space and then compared to the standard green color in the LCC. The comparison results are then used to classify the colors. The testing results show that the method has the value of accuracy, average precision, and average recall of 54.74%, 54.44%, and 51.16% respectively. Therefore the method can only classify correctly half of the data testing.
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Majasalmi, Titta, and Ryan M. Bright. "Evaluation of leaf-level optical properties employed in land surface models." Geoscientific Model Development 12, no. 9 (September 5, 2019): 3923–38. http://dx.doi.org/10.5194/gmd-12-3923-2019.
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Abstract. Vegetation optical properties have a direct impact on canopy absorption and scattering and are thus needed for modeling surface fluxes. Although plant functional type (PFT) classification varies between different land surface models (LSMs), their optical properties must be specified. The aim of this study is to revisit the “time-invariant optical properties table” of the Simple Biosphere (SiB) model (later referred to as the “SiB table”) presented 30 years ago by Dorman and Sellers (1989), which has since been adopted by many LSMs. This revisit was needed as many of the data underlying the SiB table were not formally reviewed or published or were based on older papers or on personal communications (i.e., the validity of the optical property source data cannot be inspected due to missing data sources, outdated citation practices, and varied estimation methods). As many of today's LSMs (e.g., the Community Land Model (CLM), the Jena Scheme of Atmosphere Biosphere Coupling in Hamburg (JSBACH), and the Joint UK Land Environment Simulator (JULES)) either rely on the optical properties of the SiB table or lack references altogether for those they do employ, there is a clear need to assess (and confirm or correct) the appropriateness of those being used in today's LSMs. Here, we use various spectral databases to synthesize and harmonize the key optical property information of PFT classification shared by many leading LSMs. For forests, such classifications typically differentiate PFTs by broad geo-climatic zones (i.e., tropical, boreal, temperate) and phenology (i.e., deciduous vs. evergreen). For short-statured vegetation, such classifications typically differentiate between crops, grasses, and photosynthetic pathway. Using the PFT classification of the CLM (version 5) as an example, we found the optical properties of the visible band (VIS; 400–700 nm) to fall within the range of measured values. However, in the near-infrared and shortwave infrared bands (NIR and SWIR; e.g., 701–2500 nm, referred to as “NIR”) notable differences between CLM default and measured values were observed, thus suggesting that NIR optical properties are in need of an update. For example, for conifer PFTs, the measured mean needle single scattering albedo (SSA, i.e., the sum of reflectance and transmittance) estimates in NIR were 62 % and 78 % larger than the CLM default parameters, and for PFTs with flat leaves, the measured mean leaf SSA values in NIR were 20 %, 14 %, and 19 % larger than the CLM defaults. We also found that while the CLM5 PFT-dependent leaf angle values were sufficient for forested PFTs and grasses, for crop PFTs the default parameterization appeared too vertically oriented, thus warranting an update. In addition, we propose using separate bark reflectance values for conifer and deciduous PFTs and demonstrate how shoot-level clumping correction can be incorporated into LSMs to mitigate violations of turbid media assumption and Beer's law caused by the nonrandomness of finite-sized foliage elements.
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van Deventer, Heidi, Moses Azong Cho, Onisimo Mutanga, Laven Naidoo, and Nontembeko Dudeni-Tlhone. "Reducing Leaf-Level Hyperspectral Data to 22 Components of Biochemical and Biophysical Bands Optimizes Tree Species Discrimination." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 6 (June 2015): 3161–71. http://dx.doi.org/10.1109/jstars.2015.2424594.
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Chen, Tingting, Weiguang Yang, Huajian Zhang, Bingyu Zhu, Ruier Zeng, Xinyue Wang, Shuaibin Wang, et al. "Early detection of bacterial wilt in peanut plants through leaf-level hyperspectral and unmanned aerial vehicle data." Computers and Electronics in Agriculture 177 (October 2020): 105708. http://dx.doi.org/10.1016/j.compag.2020.105708.
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Savé, R., J. Peñuelas, I. Filella, and C. Olivella. "Water Relations, Hormonal Level, and Spectral Reflectance of Gerbera jamesonii BoluS Subjected to Chilling Stress." Journal of the American Society for Horticultural Science 120, no. 3 (May 1995): 515–19. http://dx.doi.org/10.21273/jashs.120.3.515.
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One-year-old gerbera plants subjected to 1 night at 5C had reduced leaf water losses and chlorophyll content and increased root hydraulic resistance, but stomatal conductance and leaf water potential did not change. After 3 nights, leaf water potential had decreased and leaf reflectance in the visible and the near-infrared had increased. Similarly, abscisic acid (ABA) in leaves had increased and cytokinins (CK) in leaves and roots had decreased, but ABA levels in roots did not change. After 4 days at 20C, root hydraulic resistance, reflectance and leaf water loss returned to their initial values, but leaf water potential and chlorophyll content remained lower. Leaf ABA levels reached values lower than the initial, while root ABA and leaf CK levels retained the initial values. These data suggest that in the gerbera plants studied, 3 nights at 5C produced a reversible strain but otherwise plants remained uninjured, so this gerbera variety could be cultured with low energetic inputs under Mediterranean conditions. The results may indicate that ABA and CK were acting as synergistic signals of the chilling stress. Spectral reflectance signals seemed to be useful as plant chilling injury indicators at ground level.
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Mehta, Jahangir Jepee, Furqaan Ahmad Wani, Aamir Ashraf Ahangar, Kanwaljeet Kaur, and Najmusher H. "Leaf Disease Remedy Using CNN Algorithm." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1148–51. http://dx.doi.org/10.22214/ijraset.2022.41468.
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Abstract: The proposed method aids in the diagnosis of plant diseases as well as the provision of medicines that may be employed as a defensive machine against them. The file collected from the web is correctly separated, and the various plant types are recognized and named again to produce a suitable record. A test file including several plant ailments is then obtained, which is used to assess the project's accuracy and confidence level. We'll next train our classifier with training data, and the result will be expected with maximum accuracy. We employ a Deep Convolutional Neuronic network (CNN), which consists of many layers for an estimate. A newly designed drone prototypical is also being developed that can be used to provide live updates of huge farming lands. The drone will be equipped with a highresolution photographic camera that will capture the image of the plants, which will be used as a contribution to the software, which will determine whether the plant is healthy or not. We reached a 78 percent accuracy level with our programming and training model. Our programmer provides us with the identity of the plant species, as well as the confidence level of the species and the medicine that may be used to treat it. Keywords: Machine Learning, Leaf Disease, Remedy, CNN
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Li, Hong, Wunian Yang, Junjie Lei, Jinxing She, and Xiangshan Zhou. "Estimation of leaf water content from hyperspectral data of different plant species by using three new spectral absorption indices." PLOS ONE 16, no. 3 (March 30, 2021): e0249351. http://dx.doi.org/10.1371/journal.pone.0249351.
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The leaf equivalent water thickness (EWT, g cm−2) and fuel moisture content (FMC, %) are key variables in ecological and environmental monitoring. Although a variety of hyperspectral vegetation indices have been developed to estimate the leaf EWT and FMC, most of these indices are defined considered two or three specific bands for a specific plant species, which limits their applicability. In this study, we proposed three new spectral absorption indices (SAI970, SAI1200, and SAI1660) for various plant types by considering the symmetry of the spectral absorption at 970 nm, 1200 nm and 1660 nm and spectral heterogeneity of different leaves. The indices were calculated considering the absorption peak and shoulder bands of each leaf instead of the same specific bands for all leaves. A pooled dataset of three tree species (camphor (VX), capricorn (VJ), and red-leaf plum (VL)) was used to test the performance of the SAIs in terms of the leaf EWT and FMC estimation. The results indicated that, first, SAI1200 was more suitable for estimating the EWT than FMC, whereas SAI970 and SAI1660 were more suitable for estimating the FMC. Second, SAI1200 achieved the most accurate estimation of the EWT with a cross-validation coefficient of determination (Rcv2) of 0.845 and relative cross-validation root mean square error (rRMSEcv) of 8.90%. Third, SAI1660 outperformed the other indices in estimating the FMC at the leaf level, with an Rcv2 of 0.637 and rRMSEcv of 8.56%. Fourth, SAI970 achieved a moderate accuracy in estimating the EWT (Rcv2 of 0.25 and rRMSEcv of 19.68%) and FMC (Rcv2 of 0.275 and rRMSEcv of 12.10%) at the leaf level. These results can enrich the application of the SAIs and demonstrate the potential of using SAI1200 to determine the leaf EWT and SAI1660 to obtain the leaf FMC among various plant types.
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Lati, Ran Nisim, Sagi Filin, and Hanan Eizenberg. "Robust Methods for Measurement of Leaf-Cover Area and Biomass from Image Data." Weed Science 59, no. 2 (June 2011): 276–84. http://dx.doi.org/10.1614/ws-d-10-00054.1.
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Leaf-cover area is a widely required plant development parameter for predictive models of weed growth and competition. Its assessment is performed either manually, which is labor intensive, or via visual inspection, which provides biased results. In contrast, digital image processing enables a high level of automation, thereby offering an attractive means for estimating vegetative leaf-cover area. Nonetheless, image-driven analysis is greatly affected by illumination conditions and camera position at the time of imaging and therefore may introduce bias into the analysis. Addressing both of these factors, this paper proposes an image-based model for leaf-cover area and biomass measurements. The proposed model transforms color images into an illumination-invariant representation, thus facilitating accurate leaf-cover detection under varying light conditions. To eliminate the need for fixed camera position, images are transformed into an object–space reference frame, enabling measurement in absolute metric units. Application of the proposed model shows stability in leaf-cover detection and measurement irrespective of camera position and external illumination conditions. When tested on purple nutsedge, one of the world's most troublesome weeds, a linear relation between measured leaf-cover area and plant biomass was obtained regardless of plant developmental stage. Data on the expansion of purple nutsedge leaf-cover area is essential for modeling its spatial growth. The proposed model offers the possibility of acquiring reliable and accurate biological data from digital images without extensive photogrammetric or image-processing expertise.
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Kaur, Sandeep, Amandeep Kaur, Gurpreet Kaur, and A. K. Dhawan. "Determination of economic threshold level for the timely management of cotton jassid Amrasca bigutulla (Ishida) on okra vegetable crop." Journal of Applied and Natural Science 9, no. 3 (September 1, 2017): 1429–33. http://dx.doi.org/10.31018/jans.v9i3.1379.
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A field study was carried out at Vegetable Research Farm PAU, Ludhiana to determine the economic threshold level for the timely management of cotton jassid Amrasca bigutulla (Ishida) on okra vegetable crop. It was observed that significantly lowest jassid nymphal count per leaf in the pooled data (0.96 nymphs/leaf) were registered in the treatment where spray against jassid were given at 2 nymphs/leaf stage and first injury grade that is curling and yellowing of leaf margin as compared to others spray stages (1.54-1.72 nymphs/leaf) and unsprayed control (1.75 nymphs/leaf) significantly lowest jassid injury grade was also observed when spray stared at 2 nymphs/ leaf (0.58) and second spray stage curling and yellowing of leaf margin (0.65 nymphs/leaf) as against other spray stages (1.13 – 1.60 nymphs/leaf) and unsprayed control (1.63 nymphs/leaf). Maximum plant (110.33-110.44 cm) was also recorded as against other treatment (90.80 – 108.46 cm) and control (90.13cm). Total highest fruit yield (120.40-120.75) quintal /ha was also registered in these two treatments. Economic threshold level estimated for the management of cotton jassid on okra crop will help to develop an ecologically safe pest management practices against this pest.
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Su, Huadong. "Data Research on Tobacco Leaf Image Collection Based on Computer Vision Sensor." Journal of Sensors 2021 (October 11, 2021): 1–11. http://dx.doi.org/10.1155/2021/4920212.
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In the process of tobacco silk making, how to better improve the quality of stem and leaf separation has become an issue of concern. His research mainly discusses the data collected from tobacco leaf images based on computer vision sensors. In LM (Levenberg-Marquarelt) as a training function, the algorithm uses threshing effect samples for training and learning. This paper is aimed at extracting the shape characteristic parameters of tobacco leaves and obtains the shape parameters of the length, width, area, circumference, and roundness of the tobacco leaves. In this paper, boundary tracking is used to obtain the coordinate and direction information of the tobacco leaf boundary pixels in the image, which provides a basis for obtaining the subsequent extraction of tobacco leaf characteristic parameters. In the tobacco leaf grading system, the tobacco leaf feature parameter extraction module displays the geometric characteristics of tobacco leaf, such as length, width, area, aspect ratio, rectangularity, and color characteristic, hue H , saturation S , A channel, and B channel in detail through the computer vision sensor. Finally, the subjective and objective combination weighting method is used to combine and weight the indicators of the threshing effect of the first-level threshing machine, which not only considers the quantity of information provided by the indicators but also takes into account the subjective view of the experts, which increases the weight of the indicators, accuracy, and scientificity. The approximation accuracy of the training samples of the threshing effect prediction model based on the BP neural network LM algorithm is 99.495%, the approximation accuracy of the validation set is 96.535%, and the approximation accuracy of the test set is 98.392%. This research will greatly improve production efficiency and meet the enterprise’s requirements for high efficiency and low cost.
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Patel, Jitali, Ruhi Patel, Saumya Shah, and Jigna Ashish Patel. "Big Data analytics for Advanced Viticulture." Scalable Computing: Practice and Experience 22, no. 3 (November 20, 2021): 303–12. http://dx.doi.org/10.12694/scpe.v22i3.1856.
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Big data analytics involve systematic approach to find hidden patterns to help the organization grow from large volume and variety of data. In recent years big data analytics is widely used in the agricultural domain to improve yield. Viticulture (the cultivation of grapes) is one of the most lucrative farming in India. It is a subdivision of horticulture and is the study of wine growing. The demand for Indian Wine is increasing at about 27% each year since the 21st century and thus more and more ways are being developed to improve the quality and quantity of the wine products. In this paper, we focus on a specific agricultural practice as viticulture. Weather forecasting and disease detection are the two main research areas in precision viticulture. Leaf disease detection as a part of plant pathology is the key research area in this paper. It can be applied on vineyards of India where farmers are bereft of the latest technologies. Proposed system architecture comprises four modules: Data collection, data preprocessing, classification and visualization. Database module involve grape leaf dataset, consists of healthy images combined with disease leaves such as Black measles, Black rot, and Leaf blight. Models have been implemented on Apache Hadoop using map reduce programming framework. It apply feature extraction to extract various features of the live images and classification algorithm with reduced computational complexity. Gray Level Co-occurrence Matrix (GLCM) followed by K-Nearest Neighborhood (KNN) algorithm. System also recommends the necessary steps and remedies that the viticulturists can take to assure that the grapes can be salvaged at the right time and in the right manner based on classification results. Overall system will help Indian viticulturists to improve the harvesting process. Accuracy of the model is 72% and it can be increased as a future work by including deep learning with time series grape leaf images.
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Bush, Emma R., Edward T. A. Mitchard, Thiago S. F. Silva, Edmond Dimoto, Pacôme Dimbonda, Loïc Makaga, and Katharine Abernethy. "Monitoring Mega-Crown Leaf Turnover from Space." Remote Sensing 12, no. 3 (January 29, 2020): 429. http://dx.doi.org/10.3390/rs12030429.
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Spatial and temporal patterns of tropical leaf renewal are poorly understood and poorly parameterized in modern Earth System Models due to lack of data. Remote sensing has great potential for sampling leaf phenology across tropical landscapes but until now has been impeded by lack of ground-truthing, cloudiness, poor spatial resolution, and the cryptic nature of incremental leaf turnover in many tropical plants. To our knowledge, satellite data have never been used to monitor individual crown leaf phenology in the tropics, an innovation that would be a major breakthrough for individual and species-level ecology and improve climate change predictions for the tropics. In this paper, we assessed whether satellite data can detect leaf turnover for individual trees using ground observations of a candidate tropical tree species, Moabi (Baillonella toxisperma), which has a mega-crown visible from space. We identified and delineated Moabi crowns at Lopé NP, Gabon from satellite imagery using ground coordinates and extracted high spatial and temporal resolution, optical, and synthetic-aperture radar (SAR) timeseries data for each tree. We normalized these data relative to the surrounding forest canopy and combined them with concurrent monthly crown observations of new, mature, and senescent leaves recorded from the ground. We analyzed the relationship between satellite and ground observations using generalized linear mixed models (GLMMs). Ground observations of leaf turnover were significantly correlated with optical indices derived from Sentinel-2 optical data (the normalized difference vegetation index and the green leaf index), but not with SAR data derived from Sentinel-1. We demonstrate, perhaps for the first time, how the leaf phenology of individual large-canopied tropical trees can directly influence the spectral signature of satellite pixels through time. Additionally, while the level of uncertainty in our model predictions is still very high, we believe this study shows that we are near the threshold for orbital monitoring of individual crowns within tropical forests, even in challenging locations, such as cloudy Gabon. Further technical advances in remote sensing instruments into the spatial and temporal scales relevant to organismal biological processes will unlock great potential to improve our understanding of the Earth system.
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Wocher, Matthias, Katja Berger, Martin Danner, Wolfram Mauser, and Tobias Hank. "Physically-Based Retrieval of Canopy Equivalent Water Thickness Using Hyperspectral Data." Remote Sensing 10, no. 12 (November 30, 2018): 1924. http://dx.doi.org/10.3390/rs10121924.
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Quantitative equivalent water thickness on canopy level (EWTcanopy) is an important land surface variable and retrieving EWTcanopy from remote sensing has been targeted by many studies. However, the effect of radiative penetration into the canopy has not been fully understood. Therefore, in this study the Beer-Lambert law is applied to inversely determine water content information in the 930 to 1060 nm range of canopy reflectance from measured winter wheat and corn spectra collected in 2015, 2017, and 2018. The spectral model was calibrated using a look-up-table (LUT) of 50,000 PROSPECT spectra. Internal model validation was performed using two leaf optical properties datasets (LOPEX93 and ANGERS). Destructive in-situ measurements of water content were collected separately for leaves, stalks, and fruits. Correlation between measured and modelled water content was most promising for leaves and ears in case of wheat, reaching coefficients of determination (R2) up to 0.72 and relative RMSE (rRMSE) of 26% and in case of corn for the leaf fraction only (R2 = 0.86, rRMSE = 23%). These findings indicate that, depending on the crop type and its structure, different parts of the canopy are observed by optical sensors. The results from the Munich-North-Isar test sites indicated that plant compartment specific EWTcanopy allows us to deduce more information about the physical meaning of model results than from equivalent water thickness on leaf level (EWT) which is upscaled to canopy water content (CWC) by multiplication of the leaf area index (LAI). Therefore, it is suggested to collect EWTcanopy data and corresponding reflectance for different crop types over the entire growing cycle. Nevertheless, the calibrated model proved to be transferable in time and space and thus can be applied for fast and effective retrieval of EWTcanopy in the scope of future hyperspectral satellite missions.
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Zhang, Yongqin, Jing M. Chen, and Sean C. Thomas. "Retrieving seasonal variation in chlorophyll content of overstory and understory sugar maple leaves from leaf-level hyperspectral data." Canadian Journal of Remote Sensing 33, no. 5 (October 2007): 406–15. http://dx.doi.org/10.5589/m07-037.
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Parent, Jason R., and John C. Volin. "Assessing species-level biases in tree heights estimated from terrain-optimized leaf-off airborne laser scanner (ALS) data." International Journal of Remote Sensing 36, no. 10 (May 18, 2015): 2697–712. http://dx.doi.org/10.1080/01431161.2015.1047047.
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Bhadra, Sourav, Vasit Sagan, Maitiniyazi Maimaitijiang, Matthew Maimaitiyiming, Maria Newcomb, Nadia Shakoor, and Todd C. Mockler. "Quantifying Leaf Chlorophyll Concentration of Sorghum from Hyperspectral Data Using Derivative Calculus and Machine Learning." Remote Sensing 12, no. 13 (June 29, 2020): 2082. http://dx.doi.org/10.3390/rs12132082.
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Leaf chlorophyll concentration (LCC) is an important indicator of plant health, vigor, physiological status, productivity, and nutrient deficiencies. Hyperspectral spectroscopy at leaf level has been widely used to estimate LCC accurately and non-destructively. This study utilized leaf-level hyperspectral data with derivative calculus and machine learning to estimate LCC of sorghum. We calculated fractional derivative (FD) orders starting from 0.2 to 2.0 with 0.2 order increments. Additionally, 43 common vegetation indices (VIs) were calculated from leaf spectral reflectance factor to make comparisons with reflectance-based data. Within the modeling pipeline, three feature selection methods were assessed: Pearson’s correlation coefficient (PCC), partial least squares based variable importance in the projection (VIP), and random forest-based mean decrease impurity (MDI). Finally, we used partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) to estimate the LCC of sorghum. Results showed that: (1) increasing derivative order can show improved model performance until certain order for reflectance-based analysis; however, it is inconclusive to state that a particular order is optimal for estimating LCC of sorghum; (2) VI-based modeling outperformed derivative augmented reflectance factor-based modeling; (3) mean decrease impurity was found effective in selecting sensitive features from large feature space (reflectance-based analysis), whereas simple Pearson’s correlation coefficient worked better with smaller feature space (VI-based analysis); and (4) SVR outperformed all other models within reflectance-based analysis; alternatively, ELR with VIs from original reflectance yielded slightly better results compared to all other models.
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Bi, Kaiyi, Zheng Niu, Shunfu Xiao, Jie Bai, Gang Sun, Ji Wang, Zeying Han, and Shuai Gao. "Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level." Remote Sensing 13, no. 24 (December 10, 2021): 5025. http://dx.doi.org/10.3390/rs13245025.
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Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R2 > 0.6) than the single spectral index (R2 > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R2 = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.
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Syarifahnur, Fatin, Roslizawaty Roslizawaty, Amiruddin Amiruddin, Muhammad Hasan, T. Fadrial Karmil, and Hamdani Budiman. "6. The Effect of Celery Leaves Infusa (Apium graveolens L) on Reducing Level of Blood Glucose on Rat (Rattus norvegicus) Induced by Alloxan." Jurnal Medika Veterinaria 12, no. 1 (March 14, 2018): 36–39. http://dx.doi.org/10.21157/j.med.vet..v12i1.4335.
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This study aims to investigate the effect of celery leaf infusa on decreasing level of blood glucose in rat (Rattus norvegicus). Twenty five rats were divided into 5 groups namely first treatment group (P1) as negative control. Second treatment group (P2) as positive control, rats were induced with alloxan. Third treatment group (P3) rats were induced with alloxan and given 5% celery leaf infusa. Fourth treatment group (P4) rats were induced with alloxan and given 10% celery leaf infusa. Fifth treatment group (P5) rats were induced alloxan and given 15% celery leaf infusa for 14 days. The level of blood glucose of the rat was determined before treatment, after given aloksan and after given celery leaf infusa. Data were analyzed using Analysis of Varians (ANOVA). Results showed that administration of celery leaf infusa for 14 days, show significant effect to decreasing level of glucose in rat (P0.01). It can be concluded that the administration of celery leaf infusa concentrated 5%, 10% and 15% for 14 days show significant effect to decreased level of glucose in rats.
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Kokubu, Yutaka, Seiichi Hara, and Akira Tani. "Mapping Seasonal Tree Canopy Cover and Leaf Area Using Worldview-2/3 Satellite Imagery: A Megacity-Scale Case Study in Tokyo Urban Area." Remote Sensing 12, no. 9 (May 9, 2020): 1505. http://dx.doi.org/10.3390/rs12091505.
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This study presents a methodology for developing a high-resolution (2 m) urban tree canopy leaf area inventory in different tree phenological seasons and a subsequent application of the methodology to a 625 km2 urban area in Tokyo. Satellite remote sensing has the advantage of imaging large areas simultaneously. However, mapping the tree canopy cover and leaf area accurately is still difficult in a highly heterogeneous urban landscape. The WorldView-2/3 satellite imagery at the individual tree level (2 m resolution) was used to map urban trees based on a simple pixel-based classification method. The comparison of our mapping results with the tree canopy cover derived from aerial photography shows that the error margin is within an acceptable range of 5.5% at the 3.0 km2 small district level, 5.0% at the 60.9 km2 municipality level, and 1.2% at the 625 km2 city level. Furthermore, we investigated the relationship between the satellite data (vegetation index) and in situ tree-measurement data (leaf area index) to develop a simple model to directly map the tree leaf area from the WorldView-2/3 imagery. The estimated total leaf area in Tokyo urban area in the leaf-on season (633 km2) was twice that of the leaf-off season (319 km2). Our results also showed that the estimated total leaf area in Tokyo urban area was 1.9–6.2 times higher than the results of the moderate-resolution (30 m) satellite imagery.
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Chen, Qingda, Tian Gao, Jiaojun Zhu, Fayun Wu, Xiufen Li, Deliang Lu, and Fengyuan Yu. "Individual Tree Segmentation and Tree Height Estimation Using Leaf-Off and Leaf-On UAV-LiDAR Data in Dense Deciduous Forests." Remote Sensing 14, no. 12 (June 10, 2022): 2787. http://dx.doi.org/10.3390/rs14122787.
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Accurate individual tree segmentation (ITS) is fundamental to forest management and to the studies of forest ecosystem. Unmanned Aerial Vehicle Light Detection and Ranging (UAV-LiDAR) shows advantages for ITS and tree height estimation at stand and landscape scale. However, dense deciduous forests with tightly interlocked tree crowns challenge the performance for ITS. Available LiDAR points through tree crown and appropriate algorithm are expected to attack the problem. In this study, a new UAV-LiDAR dataset that fused leaf-off and leaf-on point cloud (FULD) was introduced to assess the synergetic benefits for ITS and tree height estimation by comparing different types of segmentation algorithms (i.e., watershed segmentation, point cloud segmentation and layer stacking segmentation) in the dense deciduous forests of Northeast China. Field validation was conducted in the four typical stands, including mixed broadleaved forest (MBF), Mongolian oak forest (MOF), mixed broadleaf-conifer forest (MBCF) and larch plantation forest (LPF). The results showed that the combination of FULD and the layer stacking segmentation (LSS) algorithm produced the highest accuracies across all forest types (F-score: 0.70 to 0.85). The FULD also showed a better performance on tree height estimation, with a root mean square error (RMSE) of 1.54 m at individual level. Compared with using the leaf-on dataset solely, the RMSE of tree height estimation was reduced by 0.22 to 0.27 m, and 12.3% more trees were correctly segmented by the FULD, which are mainly contributed by improved detection rate at nearly all DBH levels and by improved detection accuracy at low DBH levels. The improvements are attributed to abundant points from the bole to the treetop of FULD, as well as each layer point being included for segmentation by LSS algorithm. These findings provide useful insights to guide the application of FULD when more multi-temporal LiDAR data are available in future.
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Khan, Muhammad Attique, Abdullah Alqahtani, Aimal Khan, Shtwai Alsubai, Adel Binbusayyis, M. Munawwar Iqbal Ch, Hwan-Seung Yong, and Jaehyuk Cha. "Cucumber Leaf Diseases Recognition Using Multi Level Deep Entropy-ELM Feature Selection." Applied Sciences 12, no. 2 (January 7, 2022): 593. http://dx.doi.org/10.3390/app12020593.
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Agriculture has becomes an immense area of research and is ascertained as a key element in the area of computer vision. In the agriculture field, image processing acts as a primary part. Cucumber is an important vegetable and its production in Pakistan is higher as compared to the other vegetables because of its use in salads. However, the diseases of cucumber such as Angular leaf spot, Anthracnose, blight, Downy mildew, and powdery mildew widely decrease the quality and quantity. Lately, numerous methods have been proposed for the identification and classification of diseases. Early detection and then treatment of the diseases in plants is important to prevent the crop from a disastrous decrease in yields. Many classification techniques have been proposed but still, they are facing some challenges such as noise, redundant features, and extraction of relevant features. In this work, an automated framework is proposed using deep learning and best feature selection for cucumber leaf diseases classification. In the proposed framework, initially, an augmentation technique is applied to the original images by creating more training data from existing samples and handling the problem of the imbalanced dataset. Then two different phases are utilized. In the first phase, fine-tuned four pre-trained models and select the best of them based on the accuracy. Features are extracted from the selected fine-tuned model and refined through the Entropy-ELM technique. In the second phase, fused the features of all four fine-tuned models and apply the Entropy-ELM technique, and finally fused with phase 1 selected feature. Finally, the fused features are recognized using machine learning classifiers for the final classification. The experimental process is conducted on five different datasets. On these datasets, the best-achieved accuracy is 98.4%. The proposed framework is evaluated on each step and also compared with some recent techniques. The comparison with some recent techniques showed that the proposed method obtained an improved performance.
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Sandi Syahputra, Rusli Rustam, and Desita Salbiah. "UJI BEBERAPA KONSENTRASI EKSTRAK TEPUNG DAUN PAITAN (Tithonia diversifolia A. Gray) TERHADAP MORTALITAS LARVA PENGGEREK TONGKOL JAGUNG Helicoverpa armigera Hubner." DINAMIKA PERTANIAN 38, no. 3 (January 18, 2023): 277–84. http://dx.doi.org/10.25299/dp.2022.vol38(3).11906.
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Borer pest of corn cobs (Helicovepa armigera Hubner) is a major pest of corn. Pest control can be done using plant-based insecticides from paitan leaf extract (Tithonia diversifolia A. Gray). This study aims to obtain the best concentration of paitan leaf extract in controlling corn cob borer Helicoverpa armigera. The study was conducted at the Plant Pest Laboratory of the Faculty of Agriculture, University of Riau. The research was carried out using a completely randomized design (CRD), consisting of 5 treatments and 4 replications. The treatments used were several concentrations of paitan leaf extract, namely 0 gl-1, 25 gl-1, 50 gl-1, 75 gl-1and 100 gl-1. Parameters observed were initial death, lethal time 50, lethal concentration (LC50 and LC95), daily mortality, and total mortality. data. Data were collected from daily mortality and then descriptively analyzed and displayed graphically. Data lethal concentration (LC50 and LC95) were analyzed probit using the POLO-PC program, while other data such as initial death, total mortality, and lethal time (LT50) were analyzed with analysis of variance. Data from the variance analysis with significant effect will be continued using the smallest significant difference (LSD) test at the 5% level. Results showed that the application of paitan leaf extract with a concentration of 100 gl-1 water was able to control H. armigera with mortality of 55.00%. The proper concentration to lead mortality of 50% of larvae H. armigera was 10.3% or equal to 103 gl-1 of water of paitan leaf extract. Meanwhile, the proper concentration to lead mortality of 95% of larvae population H. armigera was 44.7% or equivalent to 447 gl-1 water of paitan leaf extract.
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Jayathunga, Sadeepa, Toshiaki Owari, and Satoshi Tsuyuki. "Digital Aerial Photogrammetry for Uneven-Aged Forest Management: Assessing the Potential to Reconstruct Canopy Structure and Estimate Living Biomass." Remote Sensing 11, no. 3 (February 8, 2019): 338. http://dx.doi.org/10.3390/rs11030338.
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Scientifically robust yet economical and efficient methods are required to gather information about larger areas of uneven-aged forest resources, particularly at the landscape level, to reduce deforestation and forest degradation and to support the sustainable management of forest resources. In this study, we examined the potential of digital aerial photogrammetry (DAP) for assessing uneven-aged forest resources. Specifically, we tested the performance of biomass estimation by varying the conditions of several factors, e.g., image downscaling, vegetation metric extraction (point cloud- and canopy height model (CHM)-derived), modeling method ((simple linear regression (SLR), multiple linear regression (MLR), and random forest (RF)), and season (leaf-on and leaf-off). We built dense point clouds and CHMs using high-resolution aerial imagery collected in leaf-on and leaf-off conditions of an uneven-aged mixed conifer–broadleaf forest. DAP-derived vegetation metrics were then used to predict the dominant height and living biomass (total, conifer, and broadleaf) at the plot level. Our results demonstrated that image downscaling had a negative impact on the accuracy of the dominant height and biomass estimation in leaf-on conditions. In comparison to CHM-derived vegetation metrics, point cloud-derived metrics performed better in dominant height and biomass (total and conifer) estimations. Although the SLR (%RMSE = 21.1) and MLR (%RMSE = 18.1) modeling methods produced acceptable results for total biomass estimations, RF modeling significantly improved the plot-level total biomass estimation accuracy (%RMSE of 12.0 for leaf-on data). Overall, leaf-on DAP performed better in total biomass estimation compared to leaf-off DAP (%RMSE of 15.0 using RF modeling). Nevertheless, conifer biomass estimation accuracy improved when leaf-off data were used (from a %RMSE of 32.1 leaf-on to 23.8 leaf-off using RF modeling). Leaf-off DAP had a negative impact on the broadleaf biomass estimation (%RMSE > 35% for SLR, MLR, and RF modeling). Our results demonstrated that the performance of forest biomass estimation for uneven-aged forests varied with statistical representations as well as data sources. Thus, it would be appropriate to explore different statistical approaches (e.g., parametric and nonparametric) and data sources (e.g., different image resolutions, vegetation metrics, and leaf-on and leaf-off data) to inform the interpretation of remotely sensed data for biomass estimation for uneven-aged forest resources.
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Mitrović, Suzana, Milorad Veselinović, Nevena Čule, Goran Češljar, Ljiljana Brašanac-Bosanac, Saša Eremija, and Uroš Petrović. "Determination of leaf area index (LAI) at Level II Sample plots according ICP manual." Sustainable Forestry: Collection, no. 83-84 (2021): 65–77. http://dx.doi.org/10.5937/sustfor2183065m.
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The paper describes the methodology for determining LAI according to the ICP forest methodology, where hemispherical photographs were taken on a network of fixed points placed on the surfaces of three Sample plots Level II. Hemispherical photographs were processed by the Hemisfer software package. The data obtained by image processing were entered into the ICP Forests database. The obtained LAI values represent the response to the state of vegetation under the influence of different ecological conditions as well as anthropogenic influences, and will be the part of future annual monitoring at Sample plots of the Level II points.
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Wang, Hui, Feng Qin, Qi Liu, Liu Ruan, Rui Wang, Zhanhong Ma, Xiaolong Li, Pei Cheng, and Haiguang Wang. "Identification and Disease Index Inversion of Wheat Stripe Rust and Wheat Leaf Rust Based on Hyperspectral Data at Canopy Level." Journal of Spectroscopy 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/651810.
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Stripe rust and leaf rust with similar symptoms are two important wheat diseases. In this study, to investigate a method to identify and assess the two diseases, the canopy hyperspectral data of healthy wheat, wheat in incubation period, and wheat in diseased period of the diseases were collected, respectively. After data preprocessing, three support vector machine (SVM) models for disease identification and six support vector regression (SVR) models for disease index (DI) inversion were built. The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models. To improve the identification accuracy, it was suggested that a combination model could be constructed with one SVM model and two models built usingK-nearest neighbors (KNN) method. Using the DI inversion SVR models, the satisfactory results were obtained for the two diseases. The results demonstrated that identification and DI inversion of stripe rust and leaf rust can be implemented based on hyperspectral data at the canopy level.
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Chen, Yi, Jun Bin, Congming Zou, and Mengjiao Ding. "Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning." Journal of Analytical Methods in Chemistry 2021 (June 7, 2021): 1–11. http://dx.doi.org/10.1155/2021/9912589.
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The maturity affects the yield, quality, and economic value of tobacco leaves. Leaf maturity level discrimination is an important step in manual harvesting. However, the maturity judgment of fresh tobacco leaves by grower visual evaluation is subjective, which may lead to quality loss and low prices. Therefore, an objective and reliable discriminant technique for tobacco leaf maturity level based on near-infrared (NIR) spectroscopy combined with a deep learning approach of convolutional neural networks (CNNs) is proposed in this study. To assess the performance of the proposed maturity discriminant model, four conventional multiclass classification approaches—K-nearest neighbor (KNN), backpropagation neural network (BPNN), support vector machine (SVM), and extreme learning machine (ELM)—were employed for a comparative analysis of three categories (upper, middle, and lower position) of tobacco leaves. Experimental results showed that the CNN discriminant models were able to precisely classify the maturity level of tobacco leaves for the above three data sets with accuracies of 96.18%, 95.2%, and 97.31%, respectively. Moreover, the CNN models with strong feature extraction and learning ability were superior to the KNN, BPNN, SVM, and ELM models. Thus, NIR spectroscopy combined with CNN is a promising alternative to overcome the limitations of sensory assessment for tobacco leaf maturity level recognition. The development of a maturity-distinguishing model can provide an accurate, reliable, and scientific auxiliary means for tobacco leaf harvesting.
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Neuffer, Barbara, and Silke Eschner. "Life-history traits and ploidy levels in the genus Capsella (Brassicaceae)." Canadian Journal of Botany 73, no. 9 (September 1, 1995): 1354–65. http://dx.doi.org/10.1139/b95-147.
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In a random block field experiment, life-history traits associated with colonizing ability of diploid and tetraploid cytotypes of Capsella (Brassicaceae) were compared. These were germination, flowering, growth-form parameters, and leaf shape. Data are not in favour of differences in germination behaviour between the diploid and tetraploid Capsella species, as germination rate and capacity are highly influenced by inception and release of seed dormancy. Although our data at first glance seem to suggest that diploid C. rubella start flowering later than tetraploid C. bursa-pastoris, considerable ecotypic variation for flowering in both species makes it difficult to assign an effect specifically to ploidy level. We also conclude that plant height, rosette diameter, and branching number are not directly determined by ploidy level. In contrast however, leaf shape is clearly determined by ploidy level. In the light of all available data including data of previous experiments, we suggest that gene duplication by polyploidization may have been a key element that provided C. bursa-pastoris with additional genetic flexibility. It is not primarily the gain of colonization ability, as both species are weeds and colonizers. Rather, the greater genetic flexibility enabled C. bursa-pastoris to extend its range beyond that of C. rubella. Key words: ploidy level, germination, flowering, growth form, leaf morphology, Capsella.
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Červená, L., G. Pinlová, Z. Lhotáková, E. Neuwirthová, L. Kupková, M. Potůčková, J. Lysák, P. Campbell, and J. Albrechtová. "DETERMINATION OF CHLOROPHYLL CONTENT IN SELECTED GRASS COMMUNITIES OF KRKONOŠE MTS. TUNDRA BASED ON LABORATORY SPECTROSCOPY AND AERIAL HYPERSPECTRAL DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 30, 2022): 381–88. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-381-2022.
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Abstract. The study focuses on the determination of chlorophyll content in four prevailing grasses in the relict arctic-alpine tundra located in the Krkonoše Mountains National Park, Czech Republic. We compared two methods for determination of leaf chlorophyll content (LCC) – spectrophotometric determination in the laboratory, and the LCC assessed by fluorescence portable chlorophyll meter CCM-300. Relationships were established between the LCCs and vegetation indices calculated from leaf spectra acquired with contact probe coupled with an ASD FieldSpec4 Wide-Res spectroradiometer. Canopy chlorophyll contents (CCC) were computed from the LCCs and green leaf area index (LAI), and modelled based on the field spectra measured by the spectroradiometer and the hyperspectral images acquired by Headwall Nano-Hyperspec® mounted on the DJI Matrice 600 Pro drone. The calculations are performed on datasets acquired in June, July and August 2020 together and separately for species and months. In general, the correlations based on June datasets work the best at both levels: median R2 for all indices was 0.52 for all species together at leaf level and median R2 = 0.47 at the canopy level (vegetation indices computed from field spectra). Canopy chlorophyll content map was created based on the results of stepwise multiple linear regression. The R2 was 0.42 when using four wavelengths from the red and red edge spectral region. We attribute the weak model performance to a combination of several factors: leaf structure may bias LCC from laboratory measurements, effects of LAI variability on CCC, and the sampling design, probably not covering the whole phenology equally for all studied species.
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Zahera, Rika, Junita Purwanti, and Dwierra Evvyernie. "Populasi Mikroba Rumen, Fermentabilitas, dan Kecernaan Suplementasi Daun Kelor dalam Ransum Sapi Perah secara In Vitro." Jurnal Ilmu Nutrisi dan Teknologi Pakan 20, no. 3 (December 31, 2022): 117–22. http://dx.doi.org/10.29244/jintp.20.3.117-122.
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This study aimed to evaluate the microbial rumen population, fermentability, and digestibility of Moringa leaf supplementation in dairy cow ration using in vitro and to determine the optimal level of supplementation. The experiment consist of two steps with the first step was microbiology measurement used a Randomized Block Design with 5 treatments level of Moringa leaf extract (P0= control; P1= 5%, P2 = 10%, P3 =15%, P4 =20%) and the second step was in vitro fermentability and digestibility measurement used Randomized Block Design with 7 treatments level of Moringa leaf in dairy cow ration (R0 = control, R1 = R0 + 2.5% Moringa leaf, R2= R0 +5% Moringa leaf, R3 = R0 + 7.5% Moringa leaf, R4= R0+10% Moringa leaf, R5=R0+12.5% Moringa leaf, R6=R0+15% Moringa leaf) which grouped by rumen fluids. Data analysis used analysis of variance and continued with Duncan’s Multiple Range Test. The measured variable were microbial rumen population (bacteria and protozoa), fermentability (N-NH3, VFA), microbial protein synthesis, dry matter digestibility (DMD), and organic matter digestibility (DMO). The results showed Moringa leaf extract significantly decreased bacterial population (p<0.05), but there was no effect on the protozoa population. Moringa leaf supplementation did not affect N-NH3, DMD, and DMO, but significantly influenced VFA concentration and microbial protein synthesis (p<0.01). The higher Moringa leaf supplementation showed decreasing total VFA concentration, but was still within the normal range for rumen fermentation (102.29-126.69 mM). Moringa leaf supplementation showed a quadratic effect on microbial protein synthesis with an optimal supplementation level of 5%, but decreasing at a level of 7.5% still within in normal range. It can be concluded Moringa leaf can be supplemented up to 7.5% in dairy cow ration.
Key words: digestibility, fermentability, in vitro, moringa leaf, dairy cow
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Rosmaidar, Rosmaidar, Meutia Handayani, Fadillah Fadillah, Teuku Armansyah, Tongku Nizwan Siregar, Hafizuddin Hafizuddin, and Husnurrizal Husnurrizal. "The Effect of Red Betel Leaf (Piper crocatum) and Moringa Leaf Extracts on Endometritis Levels in Aceh Cows." Majalah Obat Tradisional 26, no. 3 (December 21, 2021): 161. http://dx.doi.org/10.22146/mot.64626.
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This study aims to determine the ability of red betel leaf (Piper crocatum) or Moringa leaf extracts to reduce the endometritis level in Aceh cattle. In this study, six Aceh cows aged 3-5 years, weighing 150-250 kg from the Experimental Animal Technical Implementation Unit of Faculty of Veterinary Medicine of Syiah Kuala University were used. The cows were divided into two treatment groups, namely cows with endometritis that were given red betel leaf extract (T1) and cows with endometritis that were given Moringa leaf extract (T2). Examination of the endometritis levels was carried out before and after treatment using the White Side Test (WST) method. The collection of estrus cervical mucus was needed for the WST examination, and heat induction was performed with prostaglandin F2 alpha (PGF2α) at a dose of 25 μg. Collection of cervical mucus was performed 8-12 hours after the initiation of heat. All cows with endometritis were given intrauterine extracts of red betel leaves or Moringa leaves at a concentration of 20% every 24 hours for a week at a solution volume of 20 ml. The data were then analyzed using a paired t test. The mean endometritis levels before and after treatment on K1 vs. K2 were 3.0 and 1.7 vs. 2.7 and 2.7, respectively (P<0.05). It was concluded that red betel leaf extract at a concentration of 20% was more effective in reducing the endometritis level of Aceh cows than Moringa leaf extract.
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Arza, Putri Aulia, and Suci Purnama Sari. "PENGARUH PENAMBAHAN EKSTRAK DAUN ALPUKAT (Persea americana, mill) TERHADAP MUTU ORGANOLEPTIK DAN KADAR KALIUM PUDING PISANG." JURNAL PENDIDIKAN DAN KELUARGA 9, no. 2 (June 29, 2018): 58. http://dx.doi.org/10.24036/jpk/vol9-iss2/55.
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Leaf avocado (Persea americana, mill) and Banana Mas (Moses sp) is a plant that has high potassium content and low levels of sodium. The purpose of this research is to analyze the effect of addition of avocado leaf extract to organoleptic quality and potassium banana pudding level. Type of research is true experimental with completely randomized design method. The data used was obtained directly from 25 semi trained panelists. Data analyzed by using ANOVA, if different real continued with Test Duncan. Addition of Leaf avocado and Banana Mas affect the levels of acceptance in terms of color, aroma and flavor but does not affect the texture. Addition of eggs also affects the level of potassium. Best assessment results obtained on treated banana with the addition of avocado leaf extract as much 250 ml ©. Potassium content of pudding with adding leaf avocado 0, 200ml dan 250ml respectively 0,0365 %, 0,0577 %, 0,0790 %
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Yuan, Shaoxiong, Guangman Song, Guangqing Huang, and Quan Wang. "Reshaping Hyperspectral Data into a Two-Dimensional Image for a CNN Model to Classify Plant Species from Reflectance." Remote Sensing 14, no. 16 (August 16, 2022): 3972. http://dx.doi.org/10.3390/rs14163972.
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Leaf-level hyperspectral-based species identification has a long research history. However, unlike hyperspectral image-based species classification models, convolutional neural network (CNN) models are rarely used for the one-dimensional (1D) structured leaf-level spectrum. Our research focuses on hyperspectral data from five laboratories worldwide to test the general use of effective identification of the CNN model by reshaping 1D structure hyperspectral data into two-dimensional greyscale images without principal component analysis (PCA) or downscaling. We compared the performance of two-dimensional CNNs with the deep cross neural network (DCN), support vector machine, random forest, gradient boosting machine, and decision tree in individual tree species classification from leaf-level hyperspectral data. We tested the general performance of the models by simulating an application phase using data from different labs or years as the unseen data for prediction. The best-performing CNN model had validation accuracy of 98.6%, prediction accuracy of 91.6%, and precision of 74.9%, compared to the support vector machine, with 98.6%, 88.8%, and 66.4%, respectively, and DCN, with 94.0%, 85.7%, and 57.1%, respectively. Compared with the reference models, CNNs more efficiently recognized Fagus crenata, and had high accuracy in Quercus rubra identification. Our results provide a template for a species classification method based on hyperspectral data and point to a new way of reshaping 1D data into a two-dimensional image, as the key to better species prediction. This method may also be helpful for foliar trait estimation.
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Hoan, Tran Thi, Tu Quang Hien, Mai Anh Khoa, and Tu Quang Trung. "Determination of the appropriate level of manure fertilisation for Moringa oleifera grown for animal feed." Ministry of Science and Technology, Vietnam 63, no. 2 (June 1, 2021): 58–63. http://dx.doi.org/10.31276/vjste.63(2).58-63.
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The purpose of this study is to determine the appropriate level of chicken manure for the green fodder M. oleifera grown for leaf meal production as a supplement into poultry diet to improve poultry product quality (i.e., meat and egg). The experiment was conducted at Thai Nguyen University of Agriculture and Forestry, Vietnam, for two years from 2018 to 2019. The experiment consisted of four treatments (NT) represented by four different levels of chicken manure, namely, 0 tons (NT1), 10 tons (NT2), 20 tons (NT3) and 30 tons/ha/yr (NT4). Each treatment was carried out over an area of 24 m2 with 5 replicates. The experiment was the complete randomised block design. Other factors such as plantation density, nitrogen, phosphate, potassium fertiliser levels, cutting height, and cutting intervals, etc., were similar among treatments. The results showed that the leaf dry matter yield of NT1 through NT4 was 6.919, 8.131, 8.975, and 9.494 tons/ha/yr, respectively. That of the leaf crude protein was 2.244, 2.694, 3.073, and 3.357 tons/ha/yr, respectively. Increasing manure levels from 0 to 30 tons/ha/yr decreased the dry matter content in the leaves by 1.43%, increased the crude protein in leaf dry matter basic by 2.93%, and decreased crude fibre in the leaf dry matter basic by 2.24%. Based on these results and data from statistical analysis, the most appropriate level of chicken manure application for M. oleifera was at 20 tons/ha/yr.
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Rusminah, Nunung, Yanti Rusyanti, Agus Susanto, and Tisye Chandra Rini. "The Effect of Green Betle Leaf Gel (Piper Betle Leaf) to Total Antioxidant Capacity (TAC) Level after Scaling and Root Planing (SRP) Treatment." Key Engineering Materials 829 (December 2019): 220–25. http://dx.doi.org/10.4028/www.scientific.net/kem.829.220.
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Abstract. Green betel leaf gel (Piper betle leaf) has an antimicrobial, antiseptic and antioxidant activity. An imbalance between antioxidants and ROS in the oral cavity have been implicated as one of the progressive or pathogenic factor for periodontal disease. Antimicrobial topical agent delivery may be provided as a supportive therapy for periodontal treatment after scaling and root planing. The purpose of this research is to analyze the effect of green betle leaf gel (Piper betle leaf) to Total Antioxidant Capacity (TAC) level gingival crevicular fluids after scaling and root planing treatment in chronic periodontitis patients. This research is a quasi experimental with pretest and posttest, split mouth. A total of 14 subjects followed this study. The parameters measured were TAC levels gingival crevicular fluids before and after treatment with ELISA examination. Green betel leaf gel was applied to the pocket on the test side after scaling and root planing. Data analysis using Wilcoxon test with p <0.05, and Mann-Whitney test with p <0.05. TAC levels of gingival crevicular fluids increased on day 14 on both sides, on the test side there was a significant increase (p = 0.002) while on the control side the increase was not significant. Green betel leaf gel has an effect to increase TAC level gingival crevicular fluids after scaling and root planing in chronic periodontitis patients.
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Alan, Fatma, Aydin Uzun, and Hasan Pinar. "CHARACTERIZATION OF LEAF FEATURES IN SOME BLACKBERRY GENOTYPES COLLECTED FROM THE BLACK SEA REGION." Current Trends in Natural Sciences 10, no. 19 (July 31, 2021): 375–80. http://dx.doi.org/10.47068/ctns.2021.v10i19.049.
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In this study, morphological characterization studies regarding to leaf characteristics (leaf length, leaf width, leaf cross section shape, leaf edge waviness, swelling between leaf veins, leaf tooth depth, dominant leaflet number, leaf shape, green color intensity on the upper part of the leaf and brightness on the upper part of the leaf) were carried out in some blackberry genotypes collected from the Black Sea Region of Turkey, according to the definition lists of UPOV (International Association for the Protection of New Plant Varieties). The measurements and observations made were transformed into feature scores, and using these scores, dendrogram and principal component analysis (PCA) related to morphological features were obtained. In the dendrogram obtained according to the morphological characterization data, the similarity level between the materials was determined between 0.50-1.00 and the average similarity coefficient was found as 0.75. The dendrogram consists of 2 main clusters (A and B). According to the dendrogram, genotypes obtained from Samsun (Çarşamba 1), Samsun (Çarşamba 2) and Ordu (Gülyalı), Bartın (Center) and Rize (Küçükçayır) regions were found to be the closest genotypes with 1.00 similarity level. In addition, as a result of the principal component analysis (PCA), the similarities and differences within the blackberry genotypes were clearly revealed. It was determined that they consisted of three main groups and two outer groups in their distribution on the two-dimensional plot.As a result of the study, it was determined that there is a certain level of variation between blackberry genotypes. The data obtained from this study will help to protect the diversity and to evaluate it with different studies.