Literatura académica sobre el tema "Chlorophyll Content Prediction"
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Artículos de revistas sobre el tema "Chlorophyll Content Prediction"
Lv, Jie, Feng Li Deng y Zhen Guo Yan. "Using PROSEPCT and SVM for the Estimation of Chlorophyll Concentration". Advanced Materials Research 989-994 (julio de 2014): 2184–87. http://dx.doi.org/10.4028/www.scientific.net/amr.989-994.2184.
Texto completoLiu, Yang, Jinfei Zhao, Yurong Tang, Xin Jiang y 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, n.º 9 (31 de agosto de 2022): 1348. http://dx.doi.org/10.3390/agriculture12091348.
Texto completoXu, Yanan, Keling Tu, Ying Cheng, Haonan Hou, Hailu Cao, Xuehui Dong y Qun Sun. "Application of Digital Image Analysis to the Prediction of Chlorophyll Content in Astragalus Seeds". Applied Sciences 11, n.º 18 (19 de septiembre de 2021): 8744. http://dx.doi.org/10.3390/app11188744.
Texto completoZhao, Long, Zhao Mei Qiu, Peng Jun Mao y 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 (marzo de 2014): 65–69. http://dx.doi.org/10.4028/www.scientific.net/amr.910.65.
Texto completoJin, Xiu Liang, Chang Wei Tan, Jun Chan Wang, Lu Tong, Fen Tuan Yang, Xin Kai Zhu y Wen Shan Guo. "Estimation of Wheat Chlorophyll Content Based on HJ Satellite CCD". Advanced Materials Research 468-471 (febrero de 2012): 1599–604. http://dx.doi.org/10.4028/www.scientific.net/amr.468-471.1599.
Texto completoAli, Abebe Mohammed, Roshanak Darvishzadeh, Andrew Skidmore, Marco Heurich, Marc Paganini, Uta Heiden y Sander Mücher. "Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes". Remote Sensing 12, n.º 11 (1 de junio de 2020): 1788. http://dx.doi.org/10.3390/rs12111788.
Texto completoP. SHANMUGAPRIYA, K. R. LATHA, S. PAZHANIVELAN, R. KUMARAPERUMAL, G. KARTHIKEYAN y N. S. SUDARMANIAN. "Cotton yield prediction using drone derived LAI and chlorophyll content". Journal of Agrometeorology 24, n.º 4 (2 de diciembre de 2022): 348–52. http://dx.doi.org/10.54386/jam.v24i4.1770.
Texto completoShafiq Amirul Sabri, Mohd, R. Endut, C. B. M. Rashidi, A. R. Laili, S. A. Aljunid y N. Ali. "Analysis of Near-infrared (NIR) spectroscopy for chlorophyll prediction in oil palm leaves". Bulletin of Electrical Engineering and Informatics 8, n.º 2 (1 de junio de 2019): 506–13. http://dx.doi.org/10.11591/eei.v8i2.1412.
Texto completoLarson, James E., Penelope Perkins-Veazie y Thomas M. Kon. "Apple Fruitlet Abscission Prediction. II. Characteristics of Fruitlets Predicted to Persist or Abscise by Reflectance Spectroscopy Models". HortScience 58, n.º 9 (septiembre de 2023): 1095–103. http://dx.doi.org/10.21273/hortsci17245-23.
Texto completoDamayanti, R., D. F. A. Riza, A. W. Putranto y R. J. Nainggolan. "Vernonia Amygdalina Chlorophyll Content Prediction by Feature Texture Analysis of Leaf Color". IOP Conference Series: Earth and Environmental Science 757, n.º 1 (1 de mayo de 2021): 012026. http://dx.doi.org/10.1088/1755-1315/757/1/012026.
Texto completoTesis sobre el tema "Chlorophyll Content Prediction"
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.
Texto completoCapítulos de libros sobre el tema "Chlorophyll Content Prediction"
Kogan, Felix N. "NOAA/AVHRR Satellite Data-Based Indices for Monitoring Agricultural Droughts". En Monitoring and Predicting Agricultural Drought. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195162349.003.0013.
Texto completoActas de conferencias sobre el tema "Chlorophyll Content Prediction"
Khoshrou, Mohsen Imanzadeh, Payam Zarafshan, Mohammad Dehghani, Gholamreza Chegini, Akbar Arabhosseini y Behzad Zakeri. "Deep Learning Prediction of Chlorophyll Content in Tomato Leaves". En 2021 9th RSI International Conference on Robotics and Mechatronics (ICRoM). IEEE, 2021. http://dx.doi.org/10.1109/icrom54204.2021.9663468.
Texto completoYankun Peng, Hui Huang, Wei Wang, Xiu Wang, Jianhu Wu y Leilei Zhang. "Prediction of Chlorophyll Content in Wheat Leaves Using Hyperspectral Images". En 2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2010. http://dx.doi.org/10.13031/2013.29919.
Texto completoZhang, Ying, Caijuan Li y Xiaohua Hu. "Content prediction of Chlorophyll-a in seawater based on Fuzzy BP method". En 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2011). IEEE, 2011. http://dx.doi.org/10.1109/fskd.2011.6019495.
Texto completoYao Zhang, Lihua Zheng, Minzan Li, Hong Sun y Qin Zhang. "Prediction of Water Chlorophyll-a Content Based on Multi-scale Spectral Analysis". En 2013 Kansas City, Missouri, July 21 - July 24, 2013. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2013. http://dx.doi.org/10.13031/aim.20131620105.
Texto completoYankun Peng, Wei Wang, Hui Huang, Xiu Wang y Xiaodong Gao. "Prediction of Chlorophyll Content of Winter Wheat using Leaf-level Hyperspectral Imaging Data". En 2009 Reno, Nevada, June 21 - June 24, 2009. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2009. http://dx.doi.org/10.13031/2013.27133.
Texto completoLi, Yunmei. "Applicability of linear regression equation for prediction of chlorophyll content in rice leaves". En Optics & Photonics 2005, editado por Wei Gao y David R. Shaw. SPIE, 2005. http://dx.doi.org/10.1117/12.613208.
Texto completoSaputro, Adhi Harmoko, Syifa Dzulhijjah Juansyah y Windri Handayani. "Banana (Musa sp.) maturity prediction system based on chlorophyll content using visible-NIR imaging". En 2018 International Conference on Signals and Systems (ICSigSys). IEEE, 2018. http://dx.doi.org/10.1109/icsigsys.2018.8373569.
Texto completoWang, Xu, Guoyin Wang y Xuerui Zhang. "Prediction of Chlorophyll-a content using hybrid model of least squares support vector regression and radial basis function neural networks". En 2016 Sixth International Conference on Information Science and Technology (ICIST). IEEE, 2016. http://dx.doi.org/10.1109/icist.2016.7483440.
Texto completoDing, Yong-jun, Min-zan Li, Shu-qiang Li y Deng-kui An. "Predicting chlorophyll content of greenhouse tomato with ground-based remote sensing". En SPIE Asia-Pacific Remote Sensing, editado por Allen M. Larar, Hyo-Sang Chung y Makoto Suzuki. SPIE, 2010. http://dx.doi.org/10.1117/12.866205.
Texto completoChen, Yongjie, Lihua Zheng, Minjuan Wang, Mengliu Wu y Wanlin Gao. "Prediction of chlorophyll and anthocyanin contents in purple lettuce based on image processing". En 2020 ASABE Annual International Virtual Meeting, July 13-15, 2020. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2020. http://dx.doi.org/10.13031/aim.202000544.
Texto completoInformes sobre el tema "Chlorophyll Content Prediction"
Alchanatis, Victor, Stephen W. Searcy, Moshe Meron, W. Lee, G. Y. Li y A. Ben Porath. Prediction of Nitrogen Stress Using Reflectance Techniques. United States Department of Agriculture, noviembre de 2001. http://dx.doi.org/10.32747/2001.7580664.bard.
Texto completoSeginer, Ido, Daniel H. Willits, Michael Raviv y Mary M. Peet. Transpirational Cooling of Greenhouse Crops. United States Department of Agriculture, marzo de 2000. http://dx.doi.org/10.32747/2000.7573072.bard.
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