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Journal articles on the topic 'Chlorophyll Content Prediction'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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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.

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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

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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 (2022): 1348. http://dx.doi.org/10.3390/agriculture12091348.

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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 fragr

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3

Zhao, Tengbo. "Prediction Method for Pomegranate Chlorophyll Content Based on Multi-feature Fusion of Unmanned Aerial Vehicle." Advances in Computer and Engineering Technology Research 1, no. 4 (2024): 106. https://doi.org/10.61935/acetr.4.1.2024.p106.

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The aim of this study was to obtain RGB and multispectral images of fruit tree canopy during the flowering period of pomegranate by multispectral unmanned aerial vehicle (UAV) to quickly and accurately predict the chlorophyll content in order to improve the monitoring efficiency of the orchard. A handheld chlorophyll meter was used to obtain the actual chlorophyll values, and image processing techniques were combined to extract parameters such as color features and texture features of the RGB images as well as vegetation index of the multispectral images. A chlorophyll content prediction metho

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4

Jin, Xiu Liang, Chang Wei Tan, Jun Chan Wang, et al. "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.

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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

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5

Zhang, Xuehui, Huijiao Yu, Jun Yan, and Xianyong Meng. "Study on the Detection of Chlorophyll Content in Tomato Leaves Based on RGB Images." Horticulturae 11, no. 6 (2025): 593. https://doi.org/10.3390/horticulturae11060593.

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Chlorophyll is a key substance in plant photosynthesis, and its content detection methods are of great significance in the field of agricultural AI. These methods provide important technical support for crop growth monitoring, pest and disease identification, and yield prediction, playing a crucial role in improving agricultural productivity and the level of intelligence in farming. This paper aims to explore an efficient and low-cost non-destructive method for detecting chlorophyll content (SPAD) and investigate the feasibility of smartphone image analysis technology in predicting chlorophyll

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Xu, Yanan, Keling Tu, Ying Cheng, et al. "Application of Digital Image Analysis to the Prediction of Chlorophyll Content in Astragalus Seeds." Applied Sciences 11, no. 18 (2021): 8744. http://dx.doi.org/10.3390/app11188744.

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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

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7

Liu, Xiaochi, Zhijun Li, Youzhen Xiang, et al. "Estimation of Winter Wheat Chlorophyll Content Based on Wavelet Transform and the Optimal Spectral Index." Agronomy 14, no. 6 (2024): 1309. http://dx.doi.org/10.3390/agronomy14061309.

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Hyperspectral remote sensing technology plays a vital role in advancing modern precision agriculture due to its non-destructive and efficient nature. To achieve accurate monitoring of winter wheat chlorophyll content, this study utilized 68 sets of chlorophyll content data and hyperspectral measurements collected during the jointing stage of winter wheat over two consecutive years (2019–2020), under various fertilization types and nitrogen application levels. Continuous wavelet transform was applied to transform the original reflectance, ranging from 21 to 210, and the correlation matrix metho

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8

Ali, Abebe Mohammed, Roshanak Darvishzadeh, Andrew Skidmore, et al. "Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes." Remote Sensing 12, no. 11 (2020): 1788. http://dx.doi.org/10.3390/rs12111788.

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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 robu

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9

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 (2022): 348–52. http://dx.doi.org/10.54386/jam.v24i4.1770.

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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

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10

Taha, Mohamed Farag, Hanping Mao, Yafei Wang, et al. "High-Throughput Analysis of Leaf Chlorophyll Content in Aquaponically Grown Lettuce Using Hyperspectral Reflectance and RGB Images." Plants 13, no. 3 (2024): 392. http://dx.doi.org/10.3390/plants13030392.

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Chlorophyll content reflects plants’ photosynthetic capacity, growth stage, and nitrogen status and is, therefore, of significant importance in precision agriculture. This study aims to develop a spectral and color vegetation indices-based model to estimate the chlorophyll content in aquaponically grown lettuce. A completely open-source automated machine learning (AutoML) framework (EvalML) was employed to develop the prediction models. The performance of AutoML along with four other standard machine learning models (back-propagation neural network (BPNN), partial least squares regression (PLS

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11

Mohd., Shafiq Amirul Sabri, Endut R., B. M. Rashidi C., R. Laili A., A. Aljunid S., and Ali N. "Analysis of Near-infrared (NIR) spectroscopy for chlorophyll prediction in oil palm leaves." Bulletin of Electrical Engineering and Informatics 8, no. 2 (2019): 506–13. https://doi.org/10.11591/eei.v8i2.1412.

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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 Der

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12

Zeng, Minglu, Xinghui Zhu, Ling Wan, Jian Xu, and Luming Shen. "Data-Driven Prediction of Grape Leaf Chlorophyll Content Using Hyperspectral Imaging and Convolutional Neural Networks." Applied Sciences 15, no. 10 (2025): 5696. https://doi.org/10.3390/app15105696.

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Grapes, highly nutritious and flavorful fruits, require adequate chlorophyll to ensure normal growth and development. Consequently, the rapid, accurate, and efficient detection of chlorophyll content is essential. This study develops a data-driven integrated framework that combines hyperspectral imaging (HSI) and convolutional neural networks (CNNs) to predict the chlorophyll content in grape leaves, employing hyperspectral images and chlorophyll a + b content data. Initially, the VGG16-U-Net model was employed to segment the hyperspectral images of grape leaves for leaf area extraction. Subse

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13

Li, Longjie, Junxian Guo, Qian Wang, Jun Wang, Ya Liu, and Yong Shi. "Design and Experiment of a Portable Near-Infrared Spectroscopy Device for Convenient Prediction of Leaf Chlorophyll Content." Sensors 23, no. 20 (2023): 8585. http://dx.doi.org/10.3390/s23208585.

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This study designs a spectrum data collection device and system based on the Internet of Things technology, aiming to solve the tedious process of chlorophyll collection and provide a more convenient and accurate method for predicting chlorophyll content. The device has the advantages of integrated design, portability, ease of operation, low power consumption, low cost, and low maintenance requirements, making it suitable for outdoor spectrum data collection and analysis in fields such as agriculture, environment, and geology. The core processor of the device uses the ESP8266-12F microcontroll

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14

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.

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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 seedli

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15

Zhou, Ju, Feiyi Li, Xinwu Wang, et al. "Hyperspectral and Fluorescence Imaging Approaches for Nondestructive Detection of Rice Chlorophyll." Plants 13, no. 9 (2024): 1270. http://dx.doi.org/10.3390/plants13091270.

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Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China

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16

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 (2019): 506–13. http://dx.doi.org/10.11591/eei.v8i2.1412.

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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 Der

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17

Wang, Linlin, Tingting Song, Yuejiao Du, Que Zheng, and Guangyu Zou. "The change of SPAD value and the establishment of correlation model of lettuce in intercropping mode." BIO Web of Conferences 111 (2024): 03003. http://dx.doi.org/10.1051/bioconf/202411103003.

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In order to find out the change rule of SPAD value in lettuce leaves and the rapid, non-destructive and more accurate detection method of chlorophyll content in lettuce leaves under intercropping mode, the effect of intercropping on SPAD value in lettuce leaves was studied and the correlation model between chlorophyll content and SPAD value was established. The results show that the SPAD value of lettuce leaves increases at first and then decreases slowly with the development of the days of aerosol culture, and intercropping has obvious advantage in the middle and late stages of aerosol cultur

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18

Li, Xiaoyu, Yongmei Liu, Huaiyu Wang, Xingzhi Dong, Lei Wang, and Yongqing Long. "Comparing Stacking Ensemble Learning and 1D-CNN Models for Predicting Leaf Chlorophyll Content in Stellera chamaejasme from Hyperspectral Reflectance Measurements." Agriculture 15, no. 3 (2025): 288. https://doi.org/10.3390/agriculture15030288.

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Stellera chamaejasme, a toxic invasive species widespread in degraded alpine grasslands, Qinghai Province, causes a significant threat to the local ecological balance. Accurate monitoring of the leaf chlorophyll content is essential for preventing its expansion over large areas. This study presents an optimal approach by integrating hierarchical dimensionality reduction, stacking ensemble learning, and 1D-CNN models to estimate leaf chlorophyll content in S. chamaejasme using hyperspectral reflectance data. Field spectrometry analysis demonstrates that the combination of Pearson correlation, f

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19

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 (2023): 1095–103. http://dx.doi.org/10.21273/hortsci17245-23.

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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

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20

Ma, Ling, Yao Zhang, Yiyang Zhang, et al. "Rapid Nondestructive Detection of Chlorophyll Content in Muskmelon Leaves under Different Light Quality Treatments." Agronomy 12, no. 12 (2022): 3223. http://dx.doi.org/10.3390/agronomy12123223.

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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 imagi

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21

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 (2021): 3570. http://dx.doi.org/10.3390/rs13183570.

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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. Th

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22

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 (2022): 381–87. http://dx.doi.org/10.18280/ts.390140.

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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 logisti

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23

Shi, Hongzhao, Jinjin Guo, Jiaqi An, et al. "Estimation of Chlorophyll Content in Soybean Crop at Different Growth Stages Based on Optimal Spectral Index." Agronomy 13, no. 3 (2023): 663. http://dx.doi.org/10.3390/agronomy13030663.

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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

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An, Gangqiang, Minfeng Xing, Binbin He, et al. "Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data." Remote Sensing 12, no. 18 (2020): 3104. http://dx.doi.org/10.3390/rs12183104.

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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 grad

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25

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 (2021): 012026. http://dx.doi.org/10.1088/1755-1315/757/1/012026.

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26

Zhang, Yamei, Guangxin Ru, Zhenli Zhao, and Decai Wang. "Hyperspectral Prediction Models of Chlorophyll Content in Paulownia Leaves under Drought Stress." Sensors 24, no. 19 (2024): 6309. http://dx.doi.org/10.3390/s24196309.

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This study explored the quantitative inversion of the chlorophyll content in Paulownia seedling leaves under drought stress and analyzed the factors influencing the chlorophyll content from multiple perspectives to obtain the optimal model. Paulownia seedlings were selected as the experimental materials for the potted water control experiments. Four drought stress treatments were set up to obtain four types of Paulownia seedlings: one pair of top leaves (T1), two pairs of leaves (T2), three pairs of leaves (T3), and four pairs of leaves (T4). In total, 23 spectral transformations were selected

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27

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 (2019): 150. http://dx.doi.org/10.37580/jsr.2019.2.9.150-163.

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28

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.

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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 to

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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 (2019): 110–21. http://dx.doi.org/10.2174/2405520412666190124163629.

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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 m

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Lin, W. C., J. W. Hall, and A. Klieber. "Video Imaging for Quantifying Cucumber Fruit Color." HortTechnology 3, no. 4 (1993): 436–39. http://dx.doi.org/10.21273/horttech.3.4.436.

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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:

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Kang, Yeseong, Jinwoo Nam, Younggwang Kim, et al. "Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery." Remote Sensing 13, no. 8 (2021): 1508. http://dx.doi.org/10.3390/rs13081508.

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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),

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Ji, Jiangtao, Nana Li, Hongwei Cui, et al. "Study on Monitoring SPAD Values for Multispatial Spatial Vertical Scales of Summer Maize Based on UAV Multispectral Remote Sensing." Agriculture 13, no. 5 (2023): 1004. http://dx.doi.org/10.3390/agriculture13051004.

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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 m

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Huang, Yufen, Zhenqi Fan, Hongxin Wu, Ximeng Zhang, and Yanlong Liu. "Estimation of the Relative Chlorophyll Content of Pear Leaves Based on Field Spectrometry in Alaer, Xinjiang." Sensors 25, no. 11 (2025): 3552. https://doi.org/10.3390/s25113552.

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Leaf chlorophyll content is an important indicator of the health status of pear trees. This study used Korla fragrant pears, a Xinjiang regional product, to investigate methods for estimating the relative chlorophyll content of pear leaves. Samples were collected from pear trees in the east, south, west, and north positions of peripheral canopy leaves. The leaf soil plant analysis development (SPAD) method was implemented using a SPAD-502 laser chlorophyll meter. The instrument measures the relative chlorophyll content as the SPAD value. Leaf spectra were acquired using a portable field spectr

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Li, Xuan, Bingxue Zhu, Sijia Li, Lushi Liu, Kaishan Song, and Jiping Liu. "A Comprehensive Review of Crop Chlorophyll Mapping Using Remote Sensing Approaches: Achievements, Limitations, and Future Perspectives." Sensors 25, no. 8 (2025): 2345. https://doi.org/10.3390/s25082345.

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Chlorophyll absorbs light energy and converts it into chemical energy, making it a crucial biochemical parameter for monitoring vegetation health, detecting environmental stress, and predicting physiological states. Accurate and rapid estimation of canopy chlorophyll content is crucial for assessing vegetation dynamics, ecological changes, and growth patterns. Remote sensing technology has become an indispensable tool for monitoring vegetation chlorophyll content since 2015, with more than 50 research papers published annually, contributing to a substantial body of case studies. This review di

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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.

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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 syst

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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.

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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 mos

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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 (2021): 3902. http://dx.doi.org/10.3390/rs13193902.

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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 gro

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Huang, Peng, Pan Yang, Libiao Yang, Futong Xiao, Yanqi Feng, and Yuchao Wang. "Non-Destructive Detection and Visualization of Chlorophyll Content in Cherry Tomatoes Based on Hyperspectral Technology and Machine Learning." Agriculture 14, no. 12 (2024): 2247. https://doi.org/10.3390/agriculture14122247.

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The cherry tomato has an important economic value and an increasingly broad market, and the chlorophyll content of cherry tomato leaves can directly reflect the plant’s photosynthetic ability, thus indirectly reflecting its growth status. Therefore, this paper proposes a regression detection method for chlorophyll in cherry tomato leaves by combining machine learning and hyperspectral technology to realize non-destructive, fast, and more accurate detection. Firstly, Moving-Average (MA) preprocessing was chosen as the pretreatment method for this paper, and three regression models of principal

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Ferreira, Kleiperry F., Jhonathan O. Silva, Pablo Cuevas-Reyes, Luiz Alberto Dolabela Falcão, and Mário M. Espírito-Santo. "Chronosequence and Temporal Changes in Soil Conditions, Vegetation Structure and Leaf Traits in a Tropical Dry Forest in Brazil." Forests 15, no. 10 (2024): 1700. http://dx.doi.org/10.3390/f15101700.

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The structure and diversity of tropical vegetation are shaped by biotic and abiotic factors, which function as environmental filters affecting plant performance on different spatial and temporal scales. We compared soil (Ferrasols and Cambisols) conditions, vegetation structure and leaf traits (e.g., specific leaf area, polyphenols, and chlorophyll contents a/b and in total) in the early, intermediate and late successional stages of a tropical dry forest (TDF) in southeastern Brazil. For this purpose, we measured leaf traits of the most abundant species in the same 18 plots (50 × 20 m/six per

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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 (2022): 1303. http://dx.doi.org/10.3390/agriculture12091303.

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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 statu

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Damayanti, Retno. "Image-based ANN Modeling for Nitrogen and Chlorophyll Assessment in Red Betel Leaves using Relief F-selected Texture and Colour Features." International Journal of Agriculture and Biology 33, no. 04 (2025): 340413. https://doi.org/10.17957/ijab/15.2385.

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Accurate and timely detection of chlorophyll and nitrogen content in plants is essential for optimizing nutrient management and crop health monitoring. This study aimed to develop an effective image-based artificial neural network (ANN) model to estimate chlorophyll and nitrogen (N) levels in red betel (Piper crocatum) leaves. Leaf images were captured using standardized digital imaging setups. Color Red Green Blue (RGB) and texture (entropy, homogeneity) features were extracted from these images and analyzed using a carefully optimized ANN model with robust validation techniques to ensure pre

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Wang, Qi, Ziyan Shi, Kaiyao Hou, Ning Yan, Cuiyun Wu, and Xu Li. "Smartphone-Based SPAD Value Estimation for Jujube Leaves Using Machine Learning: A Study on RGB Feature Extraction and Hybrid Modeling." Sensors 25, no. 8 (2025): 2545. https://doi.org/10.3390/s25082545.

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Chlorophyll content in date leaves is critical for fruit quality and yield. Traditional detection methods are usually complex and expensive. This study proposes a rapid detection method for chlorophyll content using smartphone images and machine learning and deep learning models. The SPAD values and RGB images of Xinjiang date palm were collected. The RGB images were preprocessed and their color features were extracted using Python and OpenCV. Through correlation analysis, 21 color features highly correlated with chlorophyll content were selected and downscaled with principal component analysi

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Yang, Xiaofei, Qiao Li, Honghui Li, Hao Zhou, Jinyan Zhang, and Xueliang Fu. "An Innovative Inversion Method of Potato Canopy Chlorophyll Content Based on the AFFS Algorithm and the CDE-EHO-GBM Model." Agriculture 15, no. 11 (2025): 1181. https://doi.org/10.3390/agriculture15111181.

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Chlorophyll content is an important indicator for estimating potato growth. However, there are still some research gaps in the inversion of canopy chlorophyll content using unmanned aerial vehicle (UAV) remote sensing. For example, it faces limitations of the growth cycle, low parameter accuracy, and single feature selection, and there is a lack of efficient and precise systematic research methods. In this study, an improved Adaptive-Forward Feature Selection (AFFS) algorithm was developed by combining remote sensing data and measured data to optimize the input Vegetation Index (VI) variables.

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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 (2023): 571. http://dx.doi.org/10.3390/s23020571.

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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 b

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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 (2023): 2120. http://dx.doi.org/10.3390/agronomy13082120.

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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 (BPN

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Zillmann, E., M. Schönert, H. Lilienthal, et al. "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.

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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 t

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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 (2020): 2650. http://dx.doi.org/10.3390/rs12162650.

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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 chlor

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Niaz, Nadia, Salman Gulzar, Jamil Hasan Kazmi, et al. "Assessment of Chlorophyll Content in Leaves of Crops and Orchards Based on SPAD, Multispectral, and Hyperspectral Techniques." Ecological Questions 35, no. 2 (2023): 1–21. http://dx.doi.org/10.12775/eq.2024.025.

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Strategic planning in developed and developing countries has significantly benefited from early assessment, identification and crop production monitoring. Remote sensing surveillance of crop health has brought significant benefits to farmers regarding early detection of latent issues, such as nutrient deficiencies or crop ailments, and taking remedial action. The study used geospatial techniques to monitor the orchards and crops of Halani in the Pakistani province of Sindh, using GeoEye and Landsat-8 satellite imagery. The absorbance of chlorophyll content in six fruit trees: mango (Mangifera

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He, Qinghai, Zhiyuan Liu, Xiaoli Li, Yong He, and Zhi Lin. "Detection of the Pigment Distribution of Stacked Matcha During Processing Based on Hyperspectral Imaging Technology." Agriculture 14, no. 11 (2024): 2033. http://dx.doi.org/10.3390/agriculture14112033.

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Color is a key indicator for evaluating the quality of tea during processing; various processing procedures can significantly affect the content of fat-soluble pigments of tea, which in turn affects the color and quality of finished tea. Therefore, there is an urgent demand for the fast, non-destructive detection of pigments of stacked tea during processing. This paper presents the use of hyperspectral imaging technology (HSI), combined with machine learning algorithms, to detect chlorophyll a, chlorophyll b, and carotenoids in stacked matcha tea during processing. Firstly, a quantitative rela

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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 (2017): 416–28. http://dx.doi.org/10.18280/ama_b.600211.

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