Relevant bibliographies by topics / Chlorophyll Content Prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Chlorophyll Content Prediction'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Chlorophyll Content Prediction"

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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Chlorophyll Content Prediction"

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Paul, Subir. "Hyperspectral Remote Sensing for Land Cover Classification and Chlorophyll Content Estimation using Advanced Machine Learning Techniques." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4537.

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In the recent years, remote sensing data or images have great potential for continuous spatial and temporal monitoring of Earth surface features. In case of optical remote sensing, hyperspectral (HS) data contains abundant spectral information and these information are advantageous for various applications. However, high-dimensional HS data handling is a very challenging task. Different techniques are proposed as a part of this thesis to handle the HS data in a computationally efficient manner and to achieve better performance for land cover classification and chlorophyll content prediction. P
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Book chapters on the topic "Chlorophyll Content Prediction"

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Kogan, Felix N. "NOAA/AVHRR Satellite Data-Based Indices for Monitoring Agricultural Droughts." In Monitoring and Predicting Agricultural Drought. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162349.003.0013.

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Operational polar-orbiting environmental satellites launched in the early 1960s were designed for daily weather monitoring around the world. In the early years, they were mostly applied for cloud monitoring and for advancing skills in satellite data applications. The new era was opened with the series of TIROS-N launched in 1978, which has continued until present. These satellites have such instruments as the advanced very high resolution radiometer (AVHRR) and the TIROS operational vertical sounder (TOVS), which included a microwave sounding unit (MSU), a stratospheric sounding unit (SSU), an
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Conference papers on the topic "Chlorophyll Content Prediction"

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Khoshrou, Mohsen Imanzadeh, Payam Zarafshan, Mohammad Dehghani, Gholamreza Chegini, Akbar Arabhosseini, and Behzad Zakeri. "Deep Learning Prediction of Chlorophyll Content in Tomato Leaves." In 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM). IEEE, 2021. http://dx.doi.org/10.1109/icrom54204.2021.9663468.

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Yankun Peng, Hui Huang, Wei Wang, Xiu Wang, Jianhu Wu, and Leilei Zhang. "Prediction of Chlorophyll Content in Wheat Leaves Using Hyperspectral Images." In 2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010. American Society of Agricultural and Biological Engineers, 2010. http://dx.doi.org/10.13031/2013.29919.

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Zhang, Ying, Caijuan Li, and Xiaohua Hu. "Content prediction of Chlorophyll-a in seawater based on Fuzzy BP method." In 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019495.

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Yao Zhang, Lihua Zheng, Minzan Li, Hong Sun, and Qin Zhang. "Prediction of Water Chlorophyll-a Content Based on Multi-scale Spectral Analysis." In 2013 Kansas City, Missouri, July 21 - July 24, 2013. American Society of Agricultural and Biological Engineers, 2013. http://dx.doi.org/10.13031/aim.20131620105.

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Yankun Peng, Wei Wang, Hui Huang, Xiu Wang, and Xiaodong Gao. "Prediction of Chlorophyll Content of Winter Wheat using Leaf-level Hyperspectral Imaging Data." In 2009 Reno, Nevada, June 21 - June 24, 2009. American Society of Agricultural and Biological Engineers, 2009. http://dx.doi.org/10.13031/2013.27133.

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Li, Yunmei. "Applicability of linear regression equation for prediction of chlorophyll content in rice leaves." In Optics & Photonics 2005, edited by Wei Gao and David R. Shaw. SPIE, 2005. http://dx.doi.org/10.1117/12.613208.

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Cheng, Shang, Zhigang Li, and Yujie Liu. "Prediction of Chlorophyll-a Content Base on Multi-module One Dimensional Convolutional Neural Network." In SPML 2023: 2023 6th International Conference on Signal Processing and Machine Learning. ACM, 2023. http://dx.doi.org/10.1145/3614008.3614026.

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Saputro, Adhi Harmoko, Syifa Dzulhijjah Juansyah, and Windri Handayani. "Banana (Musa sp.) maturity prediction system based on chlorophyll content using visible-NIR imaging." In 2018 International Conference on Signals and Systems (ICSigSys). IEEE, 2018. http://dx.doi.org/10.1109/icsigsys.2018.8373569.

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Wang, Xu, Guoyin Wang, and Xuerui Zhang. "Prediction of Chlorophyll-a content using hybrid model of least squares support vector regression and radial basis function neural networks." In 2016 Sixth International Conference on Information Science and Technology (ICIST). IEEE, 2016. http://dx.doi.org/10.1109/icist.2016.7483440.

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Ding, Yong-jun, Min-zan Li, Shu-qiang Li, and Deng-kui An. "Predicting chlorophyll content of greenhouse tomato with ground-based remote sensing." In SPIE Asia-Pacific Remote Sensing, edited by Allen M. Larar, Hyo-Sang Chung, and Makoto Suzuki. SPIE, 2010. http://dx.doi.org/10.1117/12.866205.

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Reports on the topic "Chlorophyll Content Prediction"

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Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li, and A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, 2001. http://dx.doi.org/10.32747/2001.7580664.bard.

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Commercial agriculture has come under increasing pressure to reduce nitrogen fertilizer inputs in order to minimize potential nonpoint source pollution of ground and surface waters. This has resulted in increased interest in site specific fertilizer management. One way to solve pollution problems would be to determine crop nutrient needs in real time, using remote detection, and regulating fertilizer dispensed by an applicator. By detecting actual plant needs, only the additional nitrogen necessary to optimize production would be supplied. This research aimed to develop techniques for real tim
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Seginer, Ido, Daniel H. Willits, Michael Raviv, and Mary M. Peet. Transpirational Cooling of Greenhouse Crops. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7573072.bard.

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Background Transplanting vegetable seedlings to final spacing in the greenhouse is common practice. At the time of transplanting, the transpiring leaf area is a small fraction of the ground area and its cooling effect is rather limited. A preliminary modeling study suggested that if water supply from root to canopy is not limiting, a sparse crop could maintain about the same canopy temperature as a mature crop, at the expense of a considerably higher transpiration flux per leaf (and root) area. The objectives of this project were (1) to test the predictions of the model, (2) to select suitable
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