Статті в журналах з теми "Chlorophyll Content Prediction"
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1
Lv, Jie, Feng Li Deng, and Zhen Guo Yan. "Using PROSEPCT and SVM for the Estimation of Chlorophyll Concentration." Advanced Materials Research 989-994 (July 2014): 2184–87. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2184.
Повний текст джерелаАнотація:
This study focused on estimating chlorophyll concentration of rice using PROSPECT and support vector machine. The study site is located in West Lake sewage irrigation area of Changchun, Jiliin Province. Reflectance spectrual of rice were measured by ASD3 spectrometer, chlorophyll contents of rice were recorded with a portable chlorophyll meter SPAD-502. Support vector machines and PROSPECT model were adopted to construct hyperspectral models for predicting chlorophyll content. The results indicate that: the hyperspectral prediction model of rice chlorophyll content yields a maximum correlation coefficient of 0.8563, and achieves a smallest RMSE of 9.5106; and the prediction accuracy based on the first derivative spectrum is higher than on the original spectrum. Research of this paper provides a theoretical basis for large scale dynamic prediction of rice chlorophyll content in sewage irrigated area.
2
Liu, Yang, Jinfei Zhao, Yurong Tang, Xin Jiang, and Jiean Liao. "Construction of a Chlorophyll Content Prediction Model for Predicting Chlorophyll Content in the Pericarp of Korla Fragrant Pears during the Storage Period." Agriculture 12, no. 9 (August 31, 2022): 1348. http://dx.doi.org/10.3390/agriculture12091348.
Повний текст джерелаАнотація:
A chlorophyll content prediction model for predicting chlorophyll content in the pericarp of Korla fragrant pears was constructed based on harvest maturity and storage time. This model predicts chlorophyll content in the pericarp of fragrant pears after storage by using the error backpropagation neural network (BPNN), generalized regression neural network (GRNN) and adaptive neural fuzzy inference system (ANFIS). The results demonstrate that chlorophyll content in the pericarp of fragrant pears decreased gradually as the harvest time lengthened. The chlorophyll content in the pericarp of fragrant pears with different maturity levels at harvest decreased continuously with the increase in storage time. According to a comparison of the prediction performances of the BPNN and ANFIS models, it was discovered that the trained GRNN and ANFIS models could predict chlorophyll content in the pericarp of fragrant pears. The ANFIS model showed the best prediction performances when the input membership functions were gasuss2mf (RMSE = 0.006; R2 = 0.993), dsigmf (RMSE = 0.007; R2 = 0.992) and psigmf (RMSE = 0.007; R2 = 0.992). The findings of this study can serve as references for determining the delivery quality and timing of Korla fragrant pears.
3
Xu, Yanan, Keling Tu, Ying Cheng, Haonan Hou, Hailu Cao, Xuehui Dong, and Qun Sun. "Application of Digital Image Analysis to the Prediction of Chlorophyll Content in Astragalus Seeds." Applied Sciences 11, no. 18 (September 19, 2021): 8744. http://dx.doi.org/10.3390/app11188744.
Повний текст джерелаАнотація:
Chlorophyll fluorescence (CF) has been applied to measure the chlorophyll content of seeds, in order to determine seed maturity, but the high price of equipment limits its wider application. Astragalus seeds were used to explore the applicability of digital image analysis technology to the prediction of seed chlorophyll content and to supply a low cost and alternative method. Our research comprised scanning and extracting the characteristic features of Astragalus seeds, determining the chlorophyll content, and establishing a predictive model of chlorophyll content in Astragalus seeds based on characteristic features. The results showed that the R2 of the MLR prediction model established with multiple features was ≥0.947, and the R2 of the MLP model was ≥0.943. By sorting of two single features, the R and G values, the R2 reached 0.969 and 0.965, respectively. A germination result showed that the lower the chlorophyll content, the higher the quality of the seeds. Therefore, we draw a conclusion that digital image analysis technology can be used to predict effectively the chlorophyll content of Astragalus seeds, and provide a reference for the selection of mature and viable Astragalus seeds.
4
Zhao, Long, Zhao Mei Qiu, Peng Jun Mao, and Gui Yang Deng. "Research on Biological Materials for the Preferred of the Chlorophyll Content Gray GM (1,1) Prediction Models Based on the Different Light." Advanced Materials Research 910 (March 2014): 65–69. http://dx.doi.org/10.4028/www.scientific.net/amr.910.65.
Повний текст джерелаАнотація:
Light is one of the most important factor in the growth of plants, with the advent and application of biological materials such as different artificial LED source, the new agricultural technology has been rapid development. In this study, first established the gray GM (1,1) prediction model of the pepper seedlings chlorophyll changes under the different light and then compared of the chlorophyll models under the different light. Last the study found that different artificial LED have the different effect and the forecasting curve and prediction model under the blue is optimal for pepper seedling by comparing the chlorophyll curves and predictive models of pepper seedlings under different light, so blue light is the most suitable for the growth of pepper seedlings. The results turned out that accuracy test of the three gray prediction models can achieve the best grade, and the three gray prediction models have the good practical value. Grey prediction theory can be better applied to the study of the plants.
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Jin, Xiu Liang, Chang Wei Tan, Jun Chan Wang, Lu Tong, Fen Tuan Yang, Xin Kai Zhu, and Wen Shan Guo. "Estimation of Wheat Chlorophyll Content Based on HJ Satellite CCD." Advanced Materials Research 468-471 (February 2012): 1599–604. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1599.
Повний текст джерелаАнотація:
Chlorophyll content is an important indicator for assessing crop health and predicting crop yield. It is possible that chlorophyll content (CC) was quickly and non-destructively estimated by remote sensing. The objective of the experiment was to develop precision agricultural practices for predicting CC of wheat. In this study, we compared some spectral parameters (SPs) and CC with the determination coefficient (R2), and combined these SPs by stepwise regression methods. The results indicated that the 1.45SIPI-1.05PSRI, the R2 value was 0.6589 and corresponding the root mean square error (RMSE) was 1.463, and it can be used to improve the prediction accuracy of CC.
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Ali, Abebe Mohammed, Roshanak Darvishzadeh, Andrew Skidmore, Marco Heurich, Marc Paganini, Uta Heiden, and Sander Mücher. "Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes." Remote Sensing 12, no. 11 (June 1, 2020): 1788. http://dx.doi.org/10.3390/rs12111788.
Повний текст джерелаАнотація:
Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.
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P. SHANMUGAPRIYA, K. R. LATHA, S. PAZHANIVELAN, R. KUMARAPERUMAL, G. KARTHIKEYAN, and N. S. SUDARMANIAN. "Cotton yield prediction using drone derived LAI and chlorophyll content." Journal of Agrometeorology 24, no. 4 (December 2, 2022): 348–52. http://dx.doi.org/10.54386/jam.v24i4.1770.
Повний текст джерелаАнотація:
The unmanned aerial vehicles (UAV) have become a better solution for agricultural growers due to advanced features such as minimal maintenance costs, quick set-up time, low acquisition costs, and live data capturing. Near-ground remote sensing (drone) has opened up new agronomic opportunities for better crop management. This study predicted the seed cotton yield for a cotton field area located at Tamil Nadu Agricultural University, Coimbatore. Pearson correlation analysis and regression analysis were done for ground truth data and vegetation indices for validation and accuracy and also to find the best-performing indices. It was concluded that the Wide Dynamic Range Vegetation Index (WDRVI) showed a better correlation coefficient (R=0.959) with LAI ground truth data (R2=0.919). In contrast, the Modified Chlorophyll Absorption Ratio Index (MCARI) showed a better correlation coefficient (R=0.919) with SPAD chlorophyll ground truth data (R2=0.845). Then the best performing indices WDRVI and MCARI were further used for generating the yield model. High spatial resolution drone imageries for determining LAI and chlorophyll are reliable and rapid, as per the study. It helps to determine the LAI and chlorophyll at a spatial scale and their influence on yield production. This yield prediction was technical support for the widespread adoption and application of unmanned aerial vehicle (UAV) remote sensing in large-scale precision agriculture.
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Shafiq Amirul Sabri, Mohd, R. Endut, C. B. M. Rashidi, A. R. Laili, S. A. Aljunid, and N. Ali. "Analysis of Near-infrared (NIR) spectroscopy for chlorophyll prediction in oil palm leaves." Bulletin of Electrical Engineering and Informatics 8, no. 2 (June 1, 2019): 506–13. http://dx.doi.org/10.11591/eei.v8i2.1412.
Повний текст джерелаАнотація:
Oil palm nutrient content is investigated with using chlorophyll as a representative factor correlated with NIR spectroscopy spectral absorbance. NIR spectroscopy method of sampling have been tested to overcome time consuming, complex chemical analysis procedure and invasive sampling method in order to identify chlorophyll content in an oil palm tree. Spectral absorbance data from range 900 nm to 1700 nm and chlorophyll data, then tested through five pre-processing methods which is Savitzky-Golay Smoothing (SGS), Multiplicative Scatter Correction (MSC), Single Normal Variation (SNV), First Derivative (1D) and also Second Derivative (2D) using Partial Least Square (PLS) regression prediction model to evaluate the correlation between both data. The overall results show, SGS has the best performance for preprocessing method with the results, the coefficient of determination (R2) values of 0.9998 and root mean square error (RMSE) values of 0.0639. In summary, correlation of NIR spectral absorbance data and chlorophyll can be achieved using a PLS regression model with SGS pre-processing technique. Thus, we can conclude that NIR spectroscopy method can be used to identify chlorophyll content in oil palm with using time saving, simple sampling and non-invasive method.
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Larson, James E., Penelope Perkins-Veazie, and Thomas M. Kon. "Apple Fruitlet Abscission Prediction. II. Characteristics of Fruitlets Predicted to Persist or Abscise by Reflectance Spectroscopy Models." HortScience 58, no. 9 (September 2023): 1095–103. http://dx.doi.org/10.21273/hortsci17245-23.
Повний текст джерелаАнотація:
Apple (Malus ×domestica L. Borkh.) growers need tools to predict the efficacy of chemical thinners that are applied to induce fruitlet abscission to aid in crop load management decisions. Recently, reflectance spectroscopy-based models to predict fruitlet abscission rates were developed. Using spectroscopy, persisting fruitlets had lower reflectance in the red-light (∼600 nm) and near infrared (∼950 nm) regions than abscising fruitlets. The goal of this study was to better understand how reflectance models distinguished between fruitlets that ultimately persisted or abscised. Individual models for the difference and ratio of each combination of wavelengths were developed to identify key wavelengths for abscission prediction from reflectance models. Accuracy for wavelength difference and ratio models was improved for all model prediction dates when reflectance (R) from R640–675 was subtracted from or divided by R675–696. This spectra region indicates differences in chlorophyll content between persisting and abscising fruitlets. Calculation of the chlorophyll concentration index (R522–579:R640–700) from nondestructively measured spectra supported this result. Chlorophyll concentration index was higher for fruitlets that ultimately persisted than abscised fruitlets (P < 0.01) for all measurement dates –1 to 9 days after thinner (DAT) in both years, except –1 DAT in 2021 (P = 0.468). Plant water index (R950–970:R890–900) was lower for persisting than abscising fruitlets for 3 to 9 DAT in 2021 (P < 0.001) and on –1 (P < 0.01) and 9 DAT (P < 0.001) in 2022. The relationship of fruit size and plant pigment (anthocyanins or chlorophyll) content in fruitlets to reflectance spectra between persisting and abscising fruitlets was also followed. Fruitlet persistence or abscission was predicted from developed models for destructively sampled fruitlets using measured reflectance spectra. Whole-fruit chlorophyll content was numerically higher in fruitlets predicted to persist than abscise for all collection dates. Higher total chlorophyll was correlated to a larger fruit size in persisting than abscising fruitlets. This higher chlorophyll content led to a lower reflectance of red light and was a key factor in model development. These results indicate that chlorophyll and water content can distinguish physiological parameters between persisting and abscising fruitlets.
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Damayanti, R., D. F. A. Riza, A. W. Putranto, and R. J. Nainggolan. "Vernonia Amygdalina Chlorophyll Content Prediction by Feature Texture Analysis of Leaf Color." IOP Conference Series: Earth and Environmental Science 757, no. 1 (May 1, 2021): 012026. http://dx.doi.org/10.1088/1755-1315/757/1/012026.
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Krishnapriya, Vengavasi, R. Arunkumar, R. Gomathi, and S. Vasantha. "PREDICTION MODELS FOR NON-DESTRUCTIVE ESTIMATION OF TOTAL CHLOROPHYLL CONTENT IN SUGARCANE." Journal of Sugarcane Research 9, no. 2 (December 31, 2019): 150. http://dx.doi.org/10.37580/jsr.2019.2.9.150-163.
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Ma, Ling, Yao Zhang, Yiyang Zhang, Jing Wang, Jianshe Li, Yanming Gao, Xiaomin Wang, and Longguo Wu. "Rapid Nondestructive Detection of Chlorophyll Content in Muskmelon Leaves under Different Light Quality Treatments." Agronomy 12, no. 12 (December 19, 2022): 3223. http://dx.doi.org/10.3390/agronomy12123223.
Повний текст джерелаАнотація:
In order to select the light quality suitable for plant growth, a quantitative detection model of chlorophyll content in muskmelon leaves was established to monitor plant growth quickly and accurately. In the paper, muskmelon “Boyang 91” was used as the experimental material, and six different light proportion treatments were set up. Through measuring plant height, stem diameter, number of leaves, nodes, and other growth indicators, in addition to leaf chlorophyll content, the response difference of muskmelon to different light qualities was explored in a plant factory. The hyperspectral imaging technology was used to establish the prediction model for the chlorophyll content of muskmelon. The original spectrum was preprocessed and optimized by five pretreatments, and then the characteristic wavelengths were extracted by six methods. Partial least squares regression (PLSR), least squares support vector machine (LSSVM), and convolutional neural network (CNN) were established for optimal feature wavelength. The results showed that the plant height and stem diameter of the T3 treatment were higher than those of other treatments, and their values were 14.48 (cm) and 5.02 (mm), respectively. The chlorophyll content of the T3 treatment was the highest, and its value was 40.16 (mg/g), which was higher than that of other treatments. Through comprehensive analysis, the T3 treatment (light ratio: 6R/1B/2W, light quantum flux: 360 μmol/(m2·s), photoperiod: 12 h) was optimal. Meanwhile, the average spectral reflectance data of 216 leaf samples were extracted, and the S-G preprocessing method was selected to preprocess the original spectral data (Rc = 0.860, RMSEC = 1.806; Rcv = 0.790, RMSECV = 2.161). By comparing and analyzing the correlation coefficients and root mean square errors of six feature wavelength extraction methods, it was concluded that the variable combination population analysis (VCPA) method had the best model effect for feature wavelength extraction (RP = 0.824, RMSEP = 1.973). Ten characteristic wavelengths ( 396, 409, 457, 518, 532, 565, 687, 691, 701, and 705 nm) extracted by the VCPA method were used to establish the chlorophyll content prediction model, and the chlorophyll content prediction model of S-G-VCPA-CNN had the best performance (Rc = 0.9151, RMSEC = 1.445; Rp = 0.811, RMSEP = 2.055). The results of this study provide data support and a theoretical basis for screening the light ratio of other crops, and also present technical support for online monitoring of crop growth in plant factories.
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Lin, Wenpeng, Xumiao Yu, Di Xu, Tengteng Sun, and Yue Sun. "Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes." Remote Sensing 13, no. 18 (September 8, 2021): 3570. http://dx.doi.org/10.3390/rs13183570.
Повний текст джерелаАнотація:
Using reflectance spectroscopy to monitor vegetation pigments is a crucial method to know the nutritional status, environmental stress, and phenological phase of vegetation. Defining cities as targeted areas and common greening plants as research objects, the pigment concentrations and dust deposition amounts of the urban plants were classified to explore the spectral difference, respectively. Furthermore, according to different dust deposition levels, this study compared and discussed the prediction models of chlorophyll concentration by correlation analysis and linear regression analysis. The results showed: (1) Dust deposition had interference effects on pigment concentration, leaf reflectance, and their correlations. Dust was an essential factor that must be considered. (2) The influence of dust deposition on chlorophyll—a concentration estimation was related to the selected vegetation indexes. Different modeling indicators had different sensitivity to dust. The SR705 and CIrededge vegetation indexes based on the red edge band were more suitable for establishing chlorophyll-a prediction models. (3) The leaf chlorophyll concentration prediction can be achieved by using reflectance spectroscopy data. The effect of the chlorophyll estimation model under the levels of “Medium dust” and “Heavy dust” was worse than that of “Less dust”, which meant the accumulation of dust had interference to the estimation of chlorophyll concentration. The quantitative analysis of vegetation spectrum by reflectance spectroscopy shows excellent advantages in the research and application of vegetation remote sensing, which provides an important theoretical basis and technical support for the practical application of plant chlorophyll content prediction.
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Song, Yufei, Shiwu Li, Zhiguo Liu, Yuekui Zhang, and Nan Shen. "Analysis on Chlorophyll Diagnosis of Wheat Leaves Based on Digital Image Processing and Feature Selection." Traitement du Signal 39, no. 1 (February 28, 2022): 381–87. http://dx.doi.org/10.18280/ts.390140.
Повний текст джерелаАнотація:
Crop nutrition measurement is of great significance in agricultural practice, especially in variable rate fertilization. The chlorophyll content, an important indicator of nitrogen nutrition in crops, largely depends on crop growth and development, photosynthesis, and crop yield, and plays an important role in the monitoring of crop growth. This paper tries to detect the chlorophyll content of wheat quickly, using the digital image processing technology. Specifically, a feature selection method was developed based on wrapper and light gradient boosting machine (LGBM), and combined with logistic regression (LR) to predict the chlorophyll content of wheat. The results show that: the optimal model is the combination between the 17 image evaluation indices screened by LGBM and the LR prediction model; the optimal results were coefficient of determination (R2) of 0.728, and root mean square error (RMSE) of 4.979. The optimal model can predict the chlorophyll content of wheat accurately based on digital images in field prototype.
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Shi, Hongzhao, Jinjin Guo, Jiaqi An, Zijun Tang, Xin Wang, Wangyang Li, Xiao Zhao, et al. "Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index." Agronomy 13, no. 3 (February 24, 2023): 663. http://dx.doi.org/10.3390/agronomy13030663.
Повний текст джерелаАнотація:
Chlorophyll is an important component of crop photosynthesis as it is necessary for the material exchange between crops and the atmosphere. The amount of chlorophyll present reflects the growth and health status of crops. Spectral technology is a feasible method for obtaining crop chlorophyll content. The first-order differential spectral index contains sufficient spectral information related to the chlorophyll content and has a high chlorophyll prediction ability. Therefore, in this study, the hyperspectral index data and chlorophyll content of soybean canopy leaves at different growth stages were obtained. The first-order differential transformation of soybean canopy hyperspectral reflectance data was performed, and five indices, highly correlated with soybean chlorophyll content at each growth stage, were selected as the optimal spectral index input. Four groups of model input variables were divided according to the following four growth stages: four-node (V4), full-bloom (R2), full-fruit (R4), and seed-filling stage (R6). Three machine learning methods, support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were used to establish an inversion model of chlorophyll content at different soybean growth stages. The model was then verified. The results showed that the correlation coefficient between the optimal spectral index and chlorophyll content of soybean was above 0.5, the R2 period correlation coefficient was above 0.7, and the R4 period correlation coefficient was above 0.8. The optimal estimation model of soybean and chlorophyll content is established through the combination of the first-order differential spectral index and RF during the R4 period. The optimal estimation model validation set determination coefficient (R2) was 0.854, the root mean square error (RMSE) was 2.627, and the mean relative error (MRE) was 4.669, demonstrating high model accuracy. The results of this study can provide a theoretical basis for monitoring the growth and health of soybean crops at different growth stages.
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An, Gangqiang, Minfeng Xing, Binbin He, Chunhua Liao, Xiaodong Huang, Jiali Shang, and Haiqi Kang. "Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data." Remote Sensing 12, no. 18 (September 22, 2020): 3104. http://dx.doi.org/10.3390/rs12183104.
Повний текст джерелаАнотація:
Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths ‘a’ and ‘b’ (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil–plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0–565.6, RCRW739.5–743.5, RCRW684.4–687.1 and RCRW667.9–672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice.
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Lv, Gang, and Hai Qing Yang. "Nondestructive Measurement of Grape Leaf Chlorophyll Content Using Multi-Spectral Imaging Technology and Calibration Models." Advanced Engineering Forum 1 (September 2011): 365–69. http://dx.doi.org/10.4028/www.scientific.net/aef.1.365.
Повний текст джерелаАнотація:
Nondestructive measurement of grape leaf chlorophyll content is essential for precision vineyard management. Multi-spectral imaging technology was adopted for image acquisition of grape leave. For each leaf, a color (R-G-B) image and a near-infrared (NIR) image were taken. These images were then transformed into three vegetation indices, e.g. RVI, NDVI and GNDVI. Calibration models were established, by single-variable linear regression, multi-variable linear regression and BP-ANN. Three color space systems, e.g. R-G-B, CIE XYZ and HIS, were examined with the purpose of model optimization. A total of 112 leave were divided into a calibration set(62) and an independent validation set(50). A SPAD-502 chlorophyll meter was used for reference measurement. The single-variable linear regression result shows that the NDVI index is most significant for the measurement of leaf chlorophyll content with coefficient of determination (r2) of 0.70 for calibration set and 0.69 for independent validation set. It is found that the model for R-index produces higher accuracy than those for G- and B-index, which confirms that chlorophyll content can be correlated with R-grayscale values. By comparison, the multi-variable linear regression models based on R-G-B-NIR achieves higher prediction accuracy with r2 of 0.8174. To further improve the prediction accuracy, several BP-ANN models were developed. The best result was achieved for R-G-B-NIR with r2 of 0.99 for independent validation set. It is concluded that multi-spectral imaging technology coupled with BP-ANN calibration model of R-G-B-NIR grayscales is promising for nondestructive measurement of grape leaf chlorophyll content. This method proposed in the study is worthy of being further examined for in situ determination of nutrition diagnose of grape plant.
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Eswari, Jujjavarapu Satya, Manwendra Kumar Tripathi, Swasti Dhagat, and Santosh Kr Karn. "Five Objective Optimization Using Naïve & Sorting Genetic Algorithm (NSGA) for Green Microalgae Culture Conditions for Biodiesel Production." Recent Innovations in Chemical Engineering (Formerly Recent Patents on Chemical Engineering) 12, no. 2 (September 26, 2019): 110–21. http://dx.doi.org/10.2174/2405520412666190124163629.
Повний текст джерелаАнотація:
Background: Renewable sources of energy like biodiesel are substitute energy fuel which are made from renewable bio sources or biomasses. Due to many advantages of using algae (Chlorella sp), we performed design of experiments in terms of functional and biochemical factors such as biomass, chlorophyll content, protein moiety and carbohydrate and lipid contents. Objective: Our objective is maximization of lipid accumulation (y1) and chlorophyll content (y2) and minimization of carbohydrate consumption (y3), protein (y4) and biomass (y5) contents. By using the experimental data, the regression model has been developed in order to obtain the desired response (biomass, chlorophyll, protein, carbohydrate and lipid) therefore it is necessary to optimize input conditions. The pre-optimization stage is an important part and useful for the production of biodiesel as biomass which is renewable energy to improve the quality. Methodology: The corresponding input and output conditions with multi-objective optimisation using naïve & sorting genetic algorithm (NSGA) is X1=0.99, X2=0.001, X3=-1.111, X4=0.01 and Lipid= 42.34, Chlorophyll=1.1212 (µgmL-1), Carbohydrate= 24.54%, Protein= 0.0742 (mgmL-1), Biomass=0.999 (gL-1). Conclusion: The multi-objective optimization NSGA prediction is compared with the response surface model combined with a genetic algorithm (RSM-GA) and we observed better productivity with NSGA.
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Lin, W. C., J. W. Hall, and A. Klieber. "Video Imaging for Quantifying Cucumber Fruit Color." HortTechnology 3, no. 4 (October 1993): 436–39. http://dx.doi.org/10.21273/horttech.3.4.436.
Повний текст джерелаАнотація:
A video-imaging technique, using commercial software to process images obtained at 550 nm, was established to estimate chlorophyll content of cucumber fruit disks. The chlorophyll content of excised disks was extracted, determined, and regressed on the video-image grey level. They were linearly related. The change in grey level of the whole visible image accurately indicated the change of green color during fruit development on the vine and the loss of green color after 1 week of storage at 13C. The relationship of the chlorophyll content on grey level was quadratic for three imaging methods: 1) average grey level of the five disks; 2) average grey level of the whole cucumber image; and 3) average grey level of central one-third of the whole cucumber image. Chlorophyll content was most highly correlated to the grey level of the disks themselves (residual SD = 6.74 μg·cm-2), but this sampling technique was destructive. Both one-third of the fruit image (SD = 9.25 μg·cm-2) and the whole image (SD = 9.36 μg·cm-2) provided satisfactory precision. For simplicity, whole-fruit imaging is suitable for estimating fruit chlorophyll content and for quantifying fruit green color intensity. Potential use of this technique in product sorting and shelf life prediction of long English cucumbers is discussed.
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Florence, Anna, Andrew Revill, Stephen Hoad, Robert Rees, and Mathew Williams. "The Effect of Antecedence on Empirical Model Forecasts of Crop Yield from Observations of Canopy Properties." Agriculture 11, no. 3 (March 18, 2021): 258. http://dx.doi.org/10.3390/agriculture11030258.
Повний текст джерелаАнотація:
Identification of yield deficits early in the growing season for cereal crops (e.g., Triticum aestivum) could help to identify more precise agronomic strategies for intervention to manage production. We investigated how effective crop canopy properties, including leaf area index (LAI), leaf chlorophyll content, and canopy height, are as predictors of winter wheat yield over various lead times. Models were calibrated and validated on fertiliser trials over two years in fields in the UK. Correlations of LAI and plant height with yield were stronger than for yield and chlorophyll content. Yield prediction models calibrated in one year and tested on another suggested that LAI and height provided the most robust outcomes. Linear models had equal or smaller validation errors than machine learning. The information content of data for yield prediction degraded strongly with time before harvest, and in application to years not included in the calibration. Thus, impact of soil and weather variation between years on crop phenotypes was critical in changing the interactions between crop variables and yield (i.e., slopes and intercepts of regression models) and was a key contributor to predictive error. These results show that canopy property data provide valuable information on crop status for yield assessment, but with important limitations.
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Kang, Yeseong, Jinwoo Nam, Younggwang Kim, Seongtae Lee, Deokgyeong Seong, Sihyeong Jang, and Chanseok Ryu. "Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery." Remote Sensing 13, no. 8 (April 14, 2021): 1508. http://dx.doi.org/10.3390/rs13081508.
Повний текст джерелаАнотація:
Unmanned aerial vehicle-based multispectral imagery including five spectral bands (blue, green, red, red-edge, and near-infrared) for a rice field in the ripening stage was used to develop regression models for predicting the rice yield and protein content and to select the most suitable regression analysis method for the year-invariant model: partial least squares regression, ridge regression, and artificial neural network (ANN). The regression models developed with six vegetation indices (green normalization difference vegetation index (GNDVI), normalization difference red-edge index (NDRE), chlorophyll index red edge (CIrededge), difference NIR/Green green difference vegetation index (GDVI), green-red NDVI (GRNDVI), and medium resolution imaging spectrometer terrestrial chlorophyll index (MTCI)), calculated from the spectral bands, were applied to single years (2018, 2019, and 2020) and multiple years (2018 + 2019, 2018 + 2020, 2019 + 2020, and all years). The regression models were cross-validated through mutual prediction against the vegetation indices in nonoverlapping years, and the prediction errors were evaluated via root mean squared error of prediction (RMSEP). The ANN model was reproducible, with low and sustained prediction errors of 24.2 kg/1000 m2 ≤ RMSEP ≤ 59.1 kg/1000 m2 in rice yield and 0.14% ≤ RMSEP ≤ 0.28% in rice-protein content in all single-year and multiple-year analyses. When the importance of each vegetation index of the regression models was evaluated, only the ANN model showed the same ranking in the vegetation index of the first (MTCI in both rice yield and protein content) and second importance (CIrededge in rice yield and GRNDVI in rice-protein content). Overall, this means that the ANN model has the highest potential for developing a year-invariant model with stable RMSEP and consistent variable ranking.
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Ji, Jiangtao, Nana Li, Hongwei Cui, Yuchao Li, Xinbo Zhao, Haolei Zhang, and Hao Ma. "Study on Monitoring SPAD Values for Multispatial Spatial Vertical Scales of Summer Maize Based on UAV Multispectral Remote Sensing." Agriculture 13, no. 5 (May 2, 2023): 1004. http://dx.doi.org/10.3390/agriculture13051004.
Повний текст джерелаАнотація:
Rapid acquisition of chlorophyll content in maize leaves is of great significance for timely monitoring of maize plant health and guiding field management. In order to accurately detect the relative chlorophyll content of summer maize and study the responsiveness of vegetation indices to SPAD (soil and plant analyzer development) values of summer maize at different spatial vertical scales, this paper established a prediction model for SPAD values of summer maize leaves at different spatial scales based on UAV multispectral images. The experiment collected multispectral image data from summer maize at the jointing stage and selected eight vegetation indices. By using the sparrow search optimized kernel limit learning machine (SSA-KELM), the prediction models for canopy leaf (CL) SPADCL and ear leaf (EL) SPADEL were established, and a linear fitting analysis was conducted combining the measured SPADCL values and SPADEL values on the ground. The results showed that for SPADCL, the R2 of the linear fitting between the predicted values and measured values was 0.899, and the RMSE was 1.068. For SPADEL, the R2 of linear fitting between the predicted values and the measured values was 0.837, and the RMSE was 0.89. Compared with the model established by the partial least squares method (PLSR), it is found that the sparrow search optimized kernel limit learning machine (SSA-KELM) has more precise prediction results with better stability and adaptability for small sample prediction. The research results can provide technical support for remote sensing monitoring of the chlorophyll content of summer maize at different spatial scales.
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Kamenova, Ilina, Petar Dimitrov, and Rusina Yordanova. "Evaluation of RapidEye vegetation indices for prediction of biophysical/biochemical variables of winter wheat." Aerospace Research in Bulgaria 30 (2018): 63–74. http://dx.doi.org/10.3897/arb.v30.e06.
Повний текст джерелаАнотація:
The aim of the study is to evaluate the possibility for using RapidEye data for prediction of Leaf Area Index (LAI), fraction of Absorbed Photosynthetically Active Radiation (fAPAR), fraction of vegetation Cover (fCover), leaf Chlorophyll Concentration (CC) and Canopy Chlorophyll Content (CCC) of winter wheat. The relation of a number of vegetation indices (VIs) with these crop variables are accessed based on a regression analysis. Indices, which make use of the red edge band, such as Chlorophyll Index red edge (CIre) and red edge Normalized Difference Vegetation Index (reNDVI), were found most useful, resulting in linear models with R2 of 0.67, 0.71, 0.72, and 0.76 for fCover, LAI, CCC, and fAPAR respectively. CC was not related with any of the VIs.
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Ta, Na, Qingrui Chang, and Youming Zhang. "Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods." Remote Sensing 13, no. 19 (September 29, 2021): 3902. http://dx.doi.org/10.3390/rs13193902.
Повний текст джерелаАнотація:
Leaf chlorophyll content (LCC) is one of the most important factors affecting photosynthetic capacity and nitrogen status, both of which influence crop harvest. However, the development of rapid and nondestructive methods for leaf chlorophyll estimation is a topic of much interest. Hence, this study explored the use of the machine learning approach to enhance the estimation of leaf chlorophyll from spectral reflectance data. The objective of this study was to evaluate four different approaches for estimating the LCC of apple tree leaves at five growth stages (the 1st, 2nd, 3rd, 4th and 5th growth stages): (1) univariate linear regression (ULR); (2) multivariate linear regression (MLR); (3) support vector regression (SVR); and (4) random forest (RF) regression. Samples were collected from the leaves on the eastern, western, southern and northern sides of apple trees five times (1st, 2nd, 3rd, 4th and 5th growth stages) over three consecutive years (2016–2018), and experiments were conducted in 10–20-year-old apple tree orchards. Correlation analysis results showed that LCC and ST, LCC and vegetation indices (VIs), and LCC and three edge parameters (TEP) had high correlations with the first-order differential spectrum (FODS) (0.86), leaf chlorophyll index (LCI) (0.87), and (SDr − SDb)/ (SDr + SDb) (0.88) at the 3rd, 3rd, and 4th growth stages, respectively. The prediction models of different growth stages were relatively good. The MLR and SVR models in the LCC assessment of different growth stages only reached the highest R2 values of 0.79 and 0.82, and the lowest RMSEs were 2.27 and 2.02, respectively. However, the RF model evaluation was significantly better than above models. The R2 value was greater than 0.94 and RMSE was less than 1.37 at different growth stages. The prediction accuracy of the 1st growth stage (R2 = 0.96, RMSE = 0.95) was best with the RF model. This result could provide a theoretical basis for orchard management. In the future, more models based on machine learning techniques should be developed using the growth information and physiological parameters of orchards that provide technical support for intelligent orchard management.
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Zhang, Ying, Xiao Hu Zhao, and Cai Juan Li. "Soft Sensing for Algae Blooms Based on Physical-Chemical Factors of Marine Environment." Applied Mechanics and Materials 58-60 (June 2011): 630–35. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.630.
Повний текст джерелаАнотація:
Soft sensing can solve the problem of on-line measuring for some variables which are difficult to measure with common instruments commendably. Chlorophyll-a is an important index of water quality for seawater, which can indicate the state of algae reproduction, further more it can predict the disaster of red tide by prediction model. The content of chlorophyll-a of seawater is affected by many physical-chemical factors, this complex relationship among them is difficult to be described by ordinary mechanism expression. In this paper, we use Fuzzy BP model to describe this complex nonlinear system, and detect the content of chlorophyll-a by the method of soft sensing. The PCA(Principal Component Analysis) method had been used to reduce the dimension of the sample data, simplify the complexity of the model system, this method can make the model has a faster convergence rate and a relative low dimension. The experiment illustrates that the result of soft sensing for algae blooms can match the real changes of the content of chlorophyll-a in seawater basically.
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Zillmann, E., M. Schönert, H. Lilienthal, B. Siegmann, T. Jarmer, P. Rosso, and T. Weichelt. "Crop Ground Cover Fraction and Canopy Chlorophyll Content Mapping using RapidEye imagery." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-7/W3 (April 28, 2015): 149–55. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-149-2015.
Повний текст джерелаАнотація:
Remote sensing is a suitable tool for estimating the spatial variability of crop canopy characteristics, such as canopy chlorophyll content (CCC) and green ground cover (GGC%), which are often used for crop productivity analysis and site-specific crop management. Empirical relationships exist between different vegetation indices (VI) and CCC and GGC% that allow spatial estimation of canopy characteristics from remote sensing imagery. However, the use of VIs is not suitable for an operational production of CCC and GGC% maps due to the limited transferability of derived empirical relationships to other regions. Thus, the operational value of crop status maps derived from remotely sensed data would be much higher if there was no need for reparametrization of the approach for different situations. <br><br> This paper reports on the suitability of high-resolution RapidEye data for estimating crop development status of winter wheat over the growing season, and demonstrates two different approaches for mapping CCC and GGC%, which do not rely on empirical relationships. The final CCC map represents relative differences in CCC, which can be quickly calibrated to field specific conditions using SPAD chlorophyll meter readings at a few points. The prediction model is capable of predicting SPAD readings with an average accuracy of 77%. The GGC% map provides absolute values at any point in the field. A high R² value of 80% was obtained for the relationship between estimated and observed GGC%. The mean absolute error for each of the two acquisition dates was 5.3% and 8.7%, respectively.
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Abdel-Sattar, Mahmoud, Adel Al-Saif, Abdulwahed Aboukarima, Dalia Eshra, and Lidia Sas-Paszt. "Quality Attributes Prediction of Flame Seedless Grape Clusters Based on Nutritional Status Employing Multiple Linear Regression Technique." Agriculture 12, no. 9 (August 25, 2022): 1303. http://dx.doi.org/10.3390/agriculture12091303.
Повний текст джерелаАнотація:
Flame Seedless grape is considered one of the most popular and favorite grapes for consumers, since it ripens early, and has good cluster quality. Flame seedless grape marketing value depends upon its desirable appearance, berry, cluster size, and shape. Therefore, it is imperative that the cluster yield and quality are enhanced to ensure profitability. In this study, the prediction of physical characteristics of clusters and berries’ color attributes of Flame Seedless grape grown under different culture practices, in particular fertilization treatments, was carried out using nutritional status concentration (leaf mineral elements, total chlorophyll content, total carotenoids content) and multiple linear regression (MLR). The method was based on the development of two indices: the first is called index 1 (%) and was formulated by combing the mineral elements of N, P, K, Ca, and Mg concentrations; and the second is called index 2 (ppm) and was formulated by combing the elements of Fe, Cu, Mn, Zn, and B concentrations in leaf petioles. The results indicated that the established MLR models can obtain variation accuracy, based on values of coefficients of determination (R2) using the test set. The R2 values were in the range of 0.9286 to 0.9972 for cluster weight, cluster length, shoulder length, berries’ color attributes (L*, a*, b*, chroma, hue, and color index for red grapes (CIRG)). This study highlighted that during a grown season, leaf mineral elements, total chlorophyll content, and total carotenoids coupled with a MLR model can be used successfully to evaluate the physical characteristics of the cluster and berries’ color attributes of Flame seedless grape. This method is easy, fast and reliable as it retains the physical appearance of the fruits by adjusting the concentration of mineral elements, total chlorophyll content, and total carotenoids in leaves. Moreover, total chlorophyll had the greatest weight of all the predicted quality attributes.
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Yu, Siyao, Haoran Bu, Xue Hu, Wancheng Dong, and Lixin Zhang. "Establishment and Accuracy Evaluation of Cotton Leaf Chlorophyll Content Prediction Model Combined with Hyperspectral Image and Feature Variable Selection." Agronomy 13, no. 8 (August 13, 2023): 2120. http://dx.doi.org/10.3390/agronomy13082120.
Повний текст джерелаАнотація:
In order to explore the feasibility of rapid non-destructive detection of cotton leaf chlorophyll content during the growth stage, this study utilized hyperspectral technology combined with a feature variable selection method to conduct quantitative detection research. Through correlation spectroscopy (COS), a total of 882 representative samples from the seedling stage, bud stage, and flowering and boll stage were used for feature wavelength screening, resulting in 213 selected feature wavelengths. Based on all wavelengths and selected feature wavelengths, a backpropagation neural network (BPNN), a backpropagation neural network optimized by genetic algorithm (GA-BPNN), a backpropagation neural network optimized by particle swarm optimization (PSO-BPNN), and a backpropagation neural network optimized by sparrow search algorithm (SSA-BPNN) prediction models were established for cotton leaf chlorophyll content, and model performance comparisons were conducted. The research results indicate that the GA-BPNN, PSO-BPNN, and SSA-BPNN models established based on all wavelengths and selected feature wavelengths outperform the BPNN model in terms of performance. Among them, the SSA-BPNN model (referred to as COS-SSA-BPNN model) established using 213 feature wavelengths extracted through correlation analysis showed the best performance. Its determination coefficient and root-mean-square error for the prediction set were 0.920 and 3.26% respectively, with a relative analysis error of 3.524. In addition, the innovative introduction of orthogonal experiments validated the performance of the model, and the results indicated that the optimal solution for achieving the best model performance was the SSA-BPNN model built with 213 feature wavelengths extracted using the COS method. These findings indicate that the combination of hyperspectral data with the COS-SSA-BPNN model can effectively achieve quantitative detection of cotton leaf chlorophyll content. The results of this study provide technical support and reference for the development of low-cost cotton leaf chlorophyll content detection systems.
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Qian, Ji, Juan Zhou, and Yang Liu. "Labview-based Study on the Modeling Method of Chlorophyll Content Prediction in Tomato Leaves." Advances in Modelling and Analysis B 60, no. 2 (June 30, 2017): 416–28. http://dx.doi.org/10.18280/ama_b.600211.
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Paul, Subir, Vinayaraj Poliyapram, Nevrez Imamoglu, Kuniaki Uto, Ryosuke Nakamura, and D. Nagesh Kumar. "Canopy Averaged Chlorophyll Content Prediction of Pear Trees Using Convolutional Autoencoder on Hyperspectral Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13 (2020): 1426–37. http://dx.doi.org/10.1109/jstars.2020.2983000.
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Yuan, Jianqing, Zhongbin Su, Qingming Kong, Li Kang, Qi Zhang, and Yu Zhang. "Hyperspectral Response of Rice Canopy and Prediction of Its Chlorophyll Content in Cold Regions." International Journal of u- and e-Service, Science and Technology 8, no. 10 (October 31, 2015): 75–82. http://dx.doi.org/10.14257/ijunesst.2015.8.10.08.
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Hoel, Bernt Olav. "Chlorophyll Meter Readings in Winter Wheat: Cultivar Differences and Prediction of Grain Protein Content." Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 52, no. 4 (January 2002): 147–57. http://dx.doi.org/10.1080/090647103100004843.
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Shanmugapriya, P., K. R. Latha, S. Pazhanivelan, R. Kumaraperumal, G. Karthikeyan, and N. S. Sudarmanian. "Spatial prediction of leaf chlorophyll content in cotton crop using drone-derived spectral indices." Current Science 123, no. 12 (December 25, 2022): 1473. http://dx.doi.org/10.18520/cs/v123/i12/1473-1480.
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Wang, Jinghua, Xiang Li, Wancheng Wang, Fan Wang, Quancheng Liu, and Lei Yan. "Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves." Sensors 23, no. 2 (January 4, 2023): 571. http://dx.doi.org/10.3390/s23020571.
Повний текст джерелаАнотація:
Tea polyphenols, amino acids, soluble sugars, and other ingredients in fresh tea leaves are the key parameters of tea quality. In this research, a tea leaf ingredient estimation sensor was developed based on a multi-channel spectral sensor. The experiment showed that the device could effectively acquire 700 nm–1000 nm spectral data of tea tree leaves and could display the ingredients of leaf samples in real time through the visual interactive interface. The spectral data of Fuding white tea tree leaves acquired by the detection device were used to build an ingredient content prediction model based on the ridge regression model and random forest algorithm. As a result, the prediction model based on the random forest algorithm with better prediction performance was loaded into the ingredient detection device. Verification experiment showed that the root mean square error (RMSE) and determination coefficient (R2) in the prediction were, respectively, as follows: moisture content (1.61 and 0.35), free amino acid content (0.16 and 0.79), tea polyphenol content (1.35 and 0.28), sugar content (0.14 and 0.33), nitrogen content (1.15 and 0.91), and chlorophyll content (0.02 and 0.97). As a result, the device can predict some parameters with high accuracy (nitrogen, chlorophyll, free amino acid) but some of them with lower accuracy (moisture, polyphenol, sugar) based on the R2 values. The tea leaf ingredient estimation sensor could realize rapid non-destructive detection of key ingredients affecting tea quality, which is conducive to real-time monitoring of the current quality of tea leaves, evaluating the status during tea tree growth, and improving the quality of tea production. The application of this research will be helpful for the automatic management of tea plantations.
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Eze, Elias, Sam Kirby, John Attridge, and Tahmina Ajmal. "Time Series Chlorophyll-A Concentration Data Analysis: A Novel Forecasting Model for Aquaculture Industry." Engineering Proceedings 5, no. 1 (June 29, 2021): 27. http://dx.doi.org/10.3390/engproc2021005027.
Повний текст джерелаАнотація:
Eutrophication in fresh water has become a critical challenge worldwide and chlorophyll-a content is a key water quality parameter that indicates the extent of eutrophication and algae concentration in a body of water. In this paper, a forecasting model for the high accuracy prediction of chlorophyll-a content is proposed to enable aquafarm managers to take remediation actions against the occurrence of toxic algal blooms in the aquaculture industry. The proposed model combines the ensemble empirical mode decomposition (EEMD) technique and a deep learning (DL) long short-term memory (LSTM) neural network (NN). With this hybrid approach, the time-series data are firstly decomposed with the aid of the EEMD algorithm into manifold intrinsic mode functions (IMFs). Secondly, a multi-attribute selection process is employed to select the group of IMFs with strong correlations with the measured real chlorophyll-a dataset and integrate them as inputs for the DL LSTM NN. The model is built on water quality sensor data collected from the Loch Duart salmon aquafarm in Scotland. The performance of the proposed novel hybrid predictive model is validated by comparing the results against the dataset. To measure the overall accuracy of the proposed novel hybrid predictive model, the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) were used.
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Liu, Chuang, Yi Liu, Yanhong Lu, Yulin Liao, Jun Nie, Xiaoliang Yuan, and Fang Chen. "Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity." PeerJ 6 (January 11, 2019): e6240. http://dx.doi.org/10.7717/peerj.6240.
Повний текст джерелаАнотація:
Improving the accuracy of predicting plant productivity is a key element in planning nutrient management strategies to ensure a balance between nutrient supply and demand under climate change. A calculation based on intercepted photosynthetically active radiation is an effective and relatively reliable way to determine the climate impact on a crop above-ground biomass (AGB). This research shows that using variations in a chlorophyll content index (CCI) in a mathematical function could effectively obtain good statistical diagnostic results between simulated and observed crop biomass. In this study, the leaf CCI, which is used as a biochemical photosynthetic component and calibration parameter, increased simulation accuracy across the growing stages during 2016–2017. This calculation improves the accuracy of prediction and modelling of crops under specific agroecosystems, and it may also improve projections of AGB for a variety of other crops.
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Qiao, Lang, Dehua Gao, Junyi Zhang, Minzan Li, Hong Sun, and Junyong Ma. "Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery." Remote Sensing 12, no. 16 (August 17, 2020): 2650. http://dx.doi.org/10.3390/rs12162650.
Повний текст джерелаАнотація:
In order to improve the diagnosis accuracy of chlorophyll content in maize canopy, the remote sensing image of maize canopy with multiple growth stages was acquired by using an unmanned aerial vehicle (UAV) equipped with a spectral camera. The dynamic influencing factors of the canopy multispectral images of maize were removed by using different image segmentation methods. The chlorophyll content of maize in the field was diagnosed. The crop canopy spectral reflectance, coverage, and texture information are combined to discuss the different segmentation methods. A full-grown maize canopy chlorophyll content diagnostic model was created on the basis of the different segmentation methods. Results showed that different segmentation methods have variations in the extraction of maize canopy parameters. The wavelet segmentation method demonstrated better advantages than threshold and ExG index segmentation methods. This method segments the soil background, reduces the texture complexity of the image, and achieves satisfactory results. The maize canopy multispectral band reflectance and vegetation index were extracted on the basis of the different segmentation methods. A partial least square regression algorithm was used to construct a full-grown maize canopy chlorophyll content diagnostic model. The result showed that the model accuracy was low when the image background was not removed (Rc2 (the determination coefficient of calibration set) = 0.5431, RMSEF (the root mean squared error of forecast) = 4.2184, MAE (the mean absolute error) = 3.24; Rv2 (the determination coefficient of validation set) = 0.5894, RMSEP (the root mean squared error of prediction) = 4.6947, and MAE = 3.36). The diagnostic accuracy of the chlorophyll content could be improved by extracting the maize canopy through the segmentation method, which was based on the wavelet segmentation method. The maize canopy chlorophyll content diagnostic model had the highest accuracy (Rc2 = 0.6638, RMSEF = 3.6211, MAE = 2.89; Rv2 = 0.6923, RMSEP = 3.9067, and MAE = 3.19). The research can provide a feasible method for crop growth and nutrition monitoring on the basis of the UAV platform and has a guiding significance for crop cultivation management.
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Georgiopoulou, Ioulia, Soultana Tzima, Georgia D. Pappa, Vasiliki Louli, Epaminondas Voutsas, and Kostis Magoulas. "Experimental Design and Optimization of Recovering Bioactive Compounds from Chlorella vulgaris through Conventional Extraction." Molecules 27, no. 1 (December 22, 2021): 29. http://dx.doi.org/10.3390/molecules27010029.
Повний текст джерелаАнотація:
Microalgae contain an abundance of valuable bioactive compounds such as chlorophylls, carotenoids, and phenolics and, consequently, present great commercial interest. The aim of this work is the study and optimization of recovering the aforementioned components from the microalgae species Chlorella vulgaris through conventional extraction in a laboratory-scale apparatus using a “green” mixture of ethanol/water 90/10 v/v. The effect of three operational conditions—namely, temperature (30–60 °C), duration (6–24 h) and solvent-to-biomass ratio (20–90 mLsolv/gbiom), was examined regarding the extracts’ yield (gravimetrically), antioxidant activity, phenolic, chlorophyll, and carotenoid contents (spectrophotometric assays), as well as concentration in key carotenoids, i.e., astaxanthin, lutein, and β-carotene (reversed-phase–high-performance liquid chromatography (RP–HPLC)). For this purpose, a face-centered central composite design (FC-CCD) was employed. Data analysis resulted in the optimal extraction conditions of 30 °C, for 24 h with 37 mLsolv/gbiom and validation of the predicted models led to 15.39% w/w yield, 52.58 mgextr/mgDPPH (IC50) antioxidant activity, total phenolic, chlorophyll, and carotenoid content of 18.23, 53.47 and 9.92 mg/gextr, respectively, and the total sum of key carotenoids equal to 4.12 mg/gextr. The experimental data and predicted results were considered comparable, and consequently, the corresponding regression models were sufficiently reliable for prediction.
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Nemeskéri, Eszter, and Lajos Helyes. "Physiological Responses of Selected Vegetable Crop Species to Water Stress." Agronomy 9, no. 8 (August 13, 2019): 447. http://dx.doi.org/10.3390/agronomy9080447.
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The frequency of drought periods influences the yield potential of crops under field conditions. The change in morphology and anatomy of plants has been tested during drought stress under controlled conditions but the change in physiological processes has not been adequately studied in separate studies but needs to be reviewed collectively. This review presents the responses of green peas, snap beans, tomatoes and sweet corn to water stress based on their stomatal behaviour, canopy temperature, chlorophyll fluorescence and the chlorophyll content of leaves. These stress markers can be used for screening the drought tolerance of genotypes, the irrigation schedules or prediction of yield.
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Zhu, Jiyou, Weijun He, Jiangming Yao, Qiang Yu, Chengyang Xu, Huaguo Huang, and Catherine Mhae B. Jandug. "Spectral Reflectance Characteristics and Chlorophyll Content Estimation Model of Quercus aquifolioides Leaves at Different Altitudes in Sejila Mountain." Applied Sciences 10, no. 10 (May 24, 2020): 3636. http://dx.doi.org/10.3390/app10103636.
Повний текст джерелаАнотація:
Quercus aquifolioides is one of the most representative broad-leaved plants in Qinghai-Tibet Plateau with important ecological status. So far, understanding how to quickly estimate the chlorophyll content of plants in plateau areas is still an urgent problem. Field Spec 3 spectrometer was used to measure hyperspectral reflectance data of Quercus aquifolioides leaves at different altitudes, and CCI (chlorophyll relative content) of corresponding leaves was measured by a chlorophyll meter. The correlation and univariate linear fitting analysis techniques were used to establish their relationship models. The results showed that: (1) Chlorophyll relative content of Quercus aquifolioides, under different altitude gradients, were significantly different. From 2905 m to 3500 m, chlorophyll relative content increased first and then decreased. Altitude 3300 m was the most suitable growth area. (2) In 350~550 nm, the spectral reflectance was 3500 m > 3300 m > 2905 m. In 750~1100 nm, the spectral reflectivity was 2905 m > 3500 m > 3300 m. (3) There were 4 main reflection peaks and 5 main absorption valleys in the leaf surface spectral reflection curve. While, 750~1400 nm was the sensitive range of leaf spectral response of Quercus aquifolioides. (4) The red edge position and red valley position moved to short wave direction with the increase of altitude, while the yellow edge position and green peak position moved to long wave direction first and then to short wave direction. (5) The correlation curve between the original spectrum and the CCI value was the best between the wavelengths 509~650 nm. The correlation between the first derivative spectrum and CCI value was the best and most stable at 450~500 nm. The green peak reflectance was most sensitive to the relative chlorophyll content of Quercus aquifolioides. The estimation model R2 of green peak reflectance was the highest (y = 206.98e−10.85x, R2 = 0.8523), and the prediction accuracy was 95.85%. The research results can provide some technical and theoretical support for the protection of natural Quercus aquifolioides forests in Tibet.
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Kumar, Chandan, Partson Mubvumba, Yanbo Huang, Jagman Dhillon, and Krishna Reddy. "Multi-Stage Corn Yield Prediction Using High-Resolution UAV Multispectral Data and Machine Learning Models." Agronomy 13, no. 5 (April 28, 2023): 1277. http://dx.doi.org/10.3390/agronomy13051277.
Повний текст джерелаАнотація:
Timely and cost-effective crop yield prediction is vital in crop management decision-making. This study evaluates the efficacy of Unmanned Aerial Vehicle (UAV)-based Vegetation Indices (VIs) coupled with Machine Learning (ML) models for corn (Zea mays) yield prediction at vegetative (V6) and reproductive (R5) growth stages using a limited number of training samples at the farm scale. Four agronomic treatments, namely Austrian Winter Peas (AWP) (Pisum sativum L.) cover crop, biochar, gypsum, and fallow with sixteen replications were applied during the non-growing corn season to assess their impact on the following corn yield. Thirty different variables (i.e., four spectral bands: green, red, red edge, and near-infrared and twenty-six VIs) were derived from UAV multispectral data collected at the V6 and R5 stages to assess their utility in yield prediction. Five different ML algorithms including Linear Regression (LR), k-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Regression (SVR), and Deep Neural Network (DNN) were evaluated in yield prediction. One-year experimental results of different treatments indicated a negligible impact on overall corn yield. Red edge, canopy chlorophyll content index, red edge chlorophyll index, chlorophyll absorption ratio index, green normalized difference vegetation index, green spectral band, and chlorophyll vegetation index were among the most suitable variables in predicting corn yield. The SVR predicted yield for the fallow with a Coefficient of Determination (R2) and Root Mean Square Error (RMSE) of 0.84 and 0.69 Mg/ha at V6 and 0.83 and 1.05 Mg/ha at the R5 stage, respectively. The KNN achieved a higher prediction accuracy for AWP (R2 = 0.69 and RMSE = 1.05 Mg/ha at V6 and 0.64 and 1.13 Mg/ha at R5) and gypsum treatment (R2 = 0.61 and RMSE = 1.49 Mg/ha at V6 and 0.80 and 1.35 Mg/ha at R5). The DNN achieved a higher prediction accuracy for biochar treatment (R2 = 0.71 and RMSE = 1.08 Mg/ha at V6 and 0.74 and 1.27 Mg/ha at R5). For the combined (AWP, biochar, gypsum, and fallow) treatment, the SVR produced the most accurate yield prediction with an R2 and RMSE of 0.36 and 1.48 Mg/ha at V6 and 0.41 and 1.43 Mg/ha at the R5. Overall, the treatment-specific yield prediction was more accurate than the combined treatment. Yield was most accurately predicted for fallow than other treatments regardless of the ML model used. SVR and KNN outperformed other ML models in yield prediction. Yields were predicted with similar accuracy at both growth stages. Thus, this study demonstrated that VIs coupled with ML models can be used in multi-stage corn yield prediction at the farm scale, even with a limited number of training data.
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Evans, Marlene S., Richard D. Robarts, and Michael T. Arts. "Predicted versus actual determinations of algal production, algal biomass, and zooplankton biomass in a hypereutrophic, hyposaline prairie lake." Canadian Journal of Fisheries and Aquatic Sciences 52, no. 5 (May 1, 1995): 1037–49. http://dx.doi.org/10.1139/f95-102.
Повний текст джерелаАнотація:
We compared the accuracy of various regression models in predicting algal production, algal biomass and composition, and zooplankton biomass in a hypereutrophic, hyposaline prairie lake. The total phosphorus (TP) models investigated underestimated mean summer algal biomass and inedible biomass: the models overestimated mean summer edible algae biomass and annual primary production in the euphotic zone. Differences between predicted and actual biomass values are attributed to intense zooplankton grazing on the edible algal community and to the gradual accumulation of slow-growing, inedible algae. The TP model investigated provided an accurate prediction of zooplankton biomass. The algal biomass model overestimated zooplankton biomass, possibly because edible algae accounted for a very small fraction of algal biomass in Humboldt Lake during the ice-free season. The chlorophyll model investigated underestimated zooplankton biomass, apparently because Humboldt Lake algae have a relatively low chlorophyll content. The use of a 0.01 conversion factor to estimate algal biomass on the basis of chlorophyll appears to be inadequate and requires further study. There was no evidence that hyposaline Humboldt Lake has a relatively high zooplankton to phytoplankton biomass ratio when compared with freshwater lakes.
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王, 博. "Prediction of Chlorophyll Content in Rice under Arsenic Stress Based on Dynamic Fuzzy Neural Network Model." Advances in Environmental Protection 07, no. 05 (2017): 404–13. http://dx.doi.org/10.12677/aep.2017.75054.
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Matsunaka, Teruo, Yuji Watanabe, Tadashi Miyawaki, and Nobuo Ichikawa. "Prediction of grain protein content in winter wheat through leaf color measurements using a chlorophyll meter." Soil Science and Plant Nutrition 43, no. 1 (March 1997): 127–34. http://dx.doi.org/10.1080/00380768.1997.10414721.
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Haboudane, Driss, John R. Miller, Nicolas Tremblay, Pablo J. Zarco-Tejada, and Louise Dextraze. "Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture." Remote Sensing of Environment 81, no. 2-3 (August 2002): 416–26. http://dx.doi.org/10.1016/s0034-4257(02)00018-4.
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Croft, H., J. M. Chen, Y. Zhang, A. Simic, T. L. Noland, N. Nesbitt, and J. Arabian. "Evaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework." ISPRS Journal of Photogrammetry and Remote Sensing 102 (April 2015): 85–95. http://dx.doi.org/10.1016/j.isprsjprs.2015.01.008.
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Wibowo, Fachrina. "Prediction of gene action content of Na, K, and Chlorophyll for Soybean Crop Adaptation to Salinity." JERAMI Indonesian Journal of Crop Science 2, no. 1 (September 1, 2019): 21–28. http://dx.doi.org/10.25077/jijcs.2.1.21-28.2019.
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Salinity area experienced an expansion that is the caused contamination of irrigation water, seawater intrusion, drought stress and excessive uses of fertilizers.varieties is one of the plant breeding programs to resolve the salinity problem, before that, however, the breeder must know plant adaptation mechanisms in morphology, physiology and biochemical so that the plant can be categorized adapt and as having potential for the tolerant varieties. This writing aims to know the action of genes through skewness and kurtosis estimation pattern Na, K, and chlorophyll content, so it is known if plant-able to adapt with salinity. This research used a destructive analysis. (A) Anjasmoro varieties, (G) Grobogan varieties, (N) Grobogan varieties that have been through repeated selection as a comparison. Research result shows the tolerant varieties having high K + ions.
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Putra, Kielvien Lourensius Eka Setia, Fabian Surya Pramudya, Alexander Agung Santoso Gunawan, and Prasetyo Mimboro. "Predicting Nitrogen Content in Oil Palms through Machine Learning and RGB Aerial Imagery." International Journal of Emerging Technology and Advanced Engineering 13, no. 6 (June 25, 2023): 19–27. http://dx.doi.org/10.46338/ijetae0623_03.
Повний текст джерелаАнотація:
—Nitrogen is a crucial nutrient for the sustainable health and productivity of oil palm plantations. Accurate fertilization for Nitrogencan optimize production while reducing maintenance costs. This study investigates the relationship between various vegetation indices and oil palm Nitrogen content using aerial images. We employ and compare different machine learning algorithms to predict Nitrogen content in oil palms, utilizing RGB aerial images obtained from PT. Perkebunan Nusantara IV (PTPN IV) in North Sumatra. Twelve vegetation indices are assessed, considering the limited spectral information available from the aerial images. Our findings reveal that the random forest algorithm, when applied to Hue, Green Leaf Index, and Coloration Index, yields the highest prediction accuracy of 90.13%. Furthermore, the results demonstrate that machine learning algorithms can effectively overcome the limitations of near-infrared channel availability, allowing for the prediction of Nitrogen content using RGB aerial images as a proxy for chlorophyll absorption.
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Zeng, Linglin, Guozhang Peng, Ran Meng, Jianguo Man, Weibo Li, Binyuan Xu, Zhengang Lv, and Rui Sun. "Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery." Remote Sensing 13, no. 15 (July 26, 2021): 2937. http://dx.doi.org/10.3390/rs13152937.
Повний текст джерелаАнотація:
Unmanned aerial vehicles-collected (UAVs) digital red–green–blue (RGB) images provided a cost-effective method for precision agriculture applications regarding yield prediction. This study aims to fully explore the potential of UAV-collected RGB images in yield prediction of winter wheat by comparing it to multi-source observations, including thermal, structure, volumetric metrics, and ground-observed leaf area index (LAI) and chlorophyll content under the same level or across different levels of nitrogen fertilization. Color indices are vegetation indices calculated by the vegetation reflectance at visible bands (i.e., red, green, and blue) derived from RGB images. The results showed that some of the color indices collected at the jointing, flowering, and early maturity stages had high correlation (R2 = 0.76–0.93) with wheat grain yield. They gave the highest prediction power (R2 = 0.92–0.93) under four levels of nitrogen fertilization at the flowering stage. In contrast, the other measurements including canopy temperature, volumetric metrics, and ground-observed chlorophyll content showed lower correlation (R2 = 0.52–0.85) to grain yield. In addition, thermal information as well as volumetric metrics generally had little contribution to the improvement of grain yield prediction when combining them with color indices derived from digital images. Especially, LAI had inferior performance to color indices in grain yield prediction within the same level of nitrogen fertilization at the flowering stage (R2 = 0.00–0.40 and R2 = 0.55–0.68), and color indices provided slightly better prediction of yield than LAI at the flowering stage (R2 = 0.93, RMSE = 32.18 g/m2 and R2 = 0.89, RMSE = 39.82 g/m2) under all levels of nitrogen fertilization. This study highlights the capabilities of color indices in wheat yield prediction across genotypes, which also indicates the potential of precision agriculture application using many other flexible, affordable, and easy-to-handle devices such as mobile phones and near surface digital cameras in the future.
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Mineeva, N. M. "Evaluation of Nutrient-Chlorophyll Relationships in the Rybinsk Reservoir." Water Science and Technology 28, no. 6 (September 1, 1993): 25–28. http://dx.doi.org/10.2166/wst.1993.0125.
Повний текст джерелаАнотація:
Our earlier study on the phytoplankton abundance and nutrient content in the Rybinsk Reservoir showed a weak correlation between chlorophyll a and nutrients. CHL:TP and CHL:TN ratios were used to quantitatively estimate the chlorophyll dependence on nitrogen and phosphorus. CHL:TP changed from 0.01 to 0.57 mg/mg in May-October 1981, 1982 and reached 1.46 in July 1989, CHL:TN ranged from 1-2 to 47 and to 56 mg/g, respectively. Variations of both indices through the ranges of CHL, TP, TN and TN:TP are given and their increment in eutrophic waters in comparison with mesotrophic ones is discussed. The present approach seems to be useful for the prediction and control of the eutrophication processes under the similar ecological conditions.