Artículos de revistas sobre el tema "Chlorophyll Content Prediction"
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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 completoKrishnapriya, Vengavasi, R. Arunkumar, R. Gomathi y S. Vasantha. "PREDICTION MODELS FOR NON-DESTRUCTIVE ESTIMATION OF TOTAL CHLOROPHYLL CONTENT IN SUGARCANE". Journal of Sugarcane Research 9, n.º 2 (31 de diciembre de 2019): 150. http://dx.doi.org/10.37580/jsr.2019.2.9.150-163.
Texto completoMa, Ling, Yao Zhang, Yiyang Zhang, Jing Wang, Jianshe Li, Yanming Gao, Xiaomin Wang y Longguo Wu. "Rapid Nondestructive Detection of Chlorophyll Content in Muskmelon Leaves under Different Light Quality Treatments". Agronomy 12, n.º 12 (19 de diciembre de 2022): 3223. http://dx.doi.org/10.3390/agronomy12123223.
Texto completoLin, Wenpeng, Xumiao Yu, Di Xu, Tengteng Sun y Yue Sun. "Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes". Remote Sensing 13, n.º 18 (8 de septiembre de 2021): 3570. http://dx.doi.org/10.3390/rs13183570.
Texto completoSong, Yufei, Shiwu Li, Zhiguo Liu, Yuekui Zhang y Nan Shen. "Analysis on Chlorophyll Diagnosis of Wheat Leaves Based on Digital Image Processing and Feature Selection". Traitement du Signal 39, n.º 1 (28 de febrero de 2022): 381–87. http://dx.doi.org/10.18280/ts.390140.
Texto completoShi, 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, n.º 3 (24 de febrero de 2023): 663. http://dx.doi.org/10.3390/agronomy13030663.
Texto completoAn, Gangqiang, Minfeng Xing, Binbin He, Chunhua Liao, Xiaodong Huang, Jiali Shang y Haiqi Kang. "Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data". Remote Sensing 12, n.º 18 (22 de septiembre de 2020): 3104. http://dx.doi.org/10.3390/rs12183104.
Texto completoLv, Gang y Hai Qing Yang. "Nondestructive Measurement of Grape Leaf Chlorophyll Content Using Multi-Spectral Imaging Technology and Calibration Models". Advanced Engineering Forum 1 (septiembre de 2011): 365–69. http://dx.doi.org/10.4028/www.scientific.net/aef.1.365.
Texto completoEswari, Jujjavarapu Satya, Manwendra Kumar Tripathi, Swasti Dhagat y 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, n.º 2 (26 de septiembre de 2019): 110–21. http://dx.doi.org/10.2174/2405520412666190124163629.
Texto completoLin, W. C., J. W. Hall y A. Klieber. "Video Imaging for Quantifying Cucumber Fruit Color". HortTechnology 3, n.º 4 (octubre de 1993): 436–39. http://dx.doi.org/10.21273/horttech.3.4.436.
Texto completoFlorence, Anna, Andrew Revill, Stephen Hoad, Robert Rees y Mathew Williams. "The Effect of Antecedence on Empirical Model Forecasts of Crop Yield from Observations of Canopy Properties". Agriculture 11, n.º 3 (18 de marzo de 2021): 258. http://dx.doi.org/10.3390/agriculture11030258.
Texto completoKang, Yeseong, Jinwoo Nam, Younggwang Kim, Seongtae Lee, Deokgyeong Seong, Sihyeong Jang y Chanseok Ryu. "Assessment of Regression Models for Predicting Rice Yield and Protein Content Using Unmanned Aerial Vehicle-Based Multispectral Imagery". Remote Sensing 13, n.º 8 (14 de abril de 2021): 1508. http://dx.doi.org/10.3390/rs13081508.
Texto completoJi, Jiangtao, Nana Li, Hongwei Cui, Yuchao Li, Xinbo Zhao, Haolei Zhang y Hao Ma. "Study on Monitoring SPAD Values for Multispatial Spatial Vertical Scales of Summer Maize Based on UAV Multispectral Remote Sensing". Agriculture 13, n.º 5 (2 de mayo de 2023): 1004. http://dx.doi.org/10.3390/agriculture13051004.
Texto completoKamenova, Ilina, Petar Dimitrov y 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.
Texto completoTa, Na, Qingrui Chang y Youming Zhang. "Estimation of Apple Tree Leaf Chlorophyll Content Based on Machine Learning Methods". Remote Sensing 13, n.º 19 (29 de septiembre de 2021): 3902. http://dx.doi.org/10.3390/rs13193902.
Texto completoZhang, Ying, Xiao Hu Zhao y Cai Juan Li. "Soft Sensing for Algae Blooms Based on Physical-Chemical Factors of Marine Environment". Applied Mechanics and Materials 58-60 (junio de 2011): 630–35. http://dx.doi.org/10.4028/www.scientific.net/amm.58-60.630.
Texto completoZillmann, E., M. Schönert, H. Lilienthal, B. Siegmann, T. Jarmer, P. Rosso y 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 (28 de abril de 2015): 149–55. http://dx.doi.org/10.5194/isprsarchives-xl-7-w3-149-2015.
Texto completoAbdel-Sattar, Mahmoud, Adel Al-Saif, Abdulwahed Aboukarima, Dalia Eshra y Lidia Sas-Paszt. "Quality Attributes Prediction of Flame Seedless Grape Clusters Based on Nutritional Status Employing Multiple Linear Regression Technique". Agriculture 12, n.º 9 (25 de agosto de 2022): 1303. http://dx.doi.org/10.3390/agriculture12091303.
Texto completoYu, Siyao, Haoran Bu, Xue Hu, Wancheng Dong y Lixin Zhang. "Establishment and Accuracy Evaluation of Cotton Leaf Chlorophyll Content Prediction Model Combined with Hyperspectral Image and Feature Variable Selection". Agronomy 13, n.º 8 (13 de agosto de 2023): 2120. http://dx.doi.org/10.3390/agronomy13082120.
Texto completoQian, Ji, Juan Zhou y Yang Liu. "Labview-based Study on the Modeling Method of Chlorophyll Content Prediction in Tomato Leaves". Advances in Modelling and Analysis B 60, n.º 2 (30 de junio de 2017): 416–28. http://dx.doi.org/10.18280/ama_b.600211.
Texto completoPaul, Subir, Vinayaraj Poliyapram, Nevrez Imamoglu, Kuniaki Uto, Ryosuke Nakamura y 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.
Texto completoYuan, Jianqing, Zhongbin Su, Qingming Kong, Li Kang, Qi Zhang y 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, n.º 10 (31 de octubre de 2015): 75–82. http://dx.doi.org/10.14257/ijunesst.2015.8.10.08.
Texto completoHoel, 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, n.º 4 (enero de 2002): 147–57. http://dx.doi.org/10.1080/090647103100004843.
Texto completoShanmugapriya, P., K. R. Latha, S. Pazhanivelan, R. Kumaraperumal, G. Karthikeyan y N. S. Sudarmanian. "Spatial prediction of leaf chlorophyll content in cotton crop using drone-derived spectral indices". Current Science 123, n.º 12 (25 de diciembre de 2022): 1473. http://dx.doi.org/10.18520/cs/v123/i12/1473-1480.
Texto completoWang, Jinghua, Xiang Li, Wancheng Wang, Fan Wang, Quancheng Liu y Lei Yan. "Research on Rapid and Low-Cost Spectral Device for the Estimation of the Quality Attributes of Tea Tree Leaves". Sensors 23, n.º 2 (4 de enero de 2023): 571. http://dx.doi.org/10.3390/s23020571.
Texto completoEze, Elias, Sam Kirby, John Attridge y Tahmina Ajmal. "Time Series Chlorophyll-A Concentration Data Analysis: A Novel Forecasting Model for Aquaculture Industry". Engineering Proceedings 5, n.º 1 (29 de junio de 2021): 27. http://dx.doi.org/10.3390/engproc2021005027.
Texto completoLiu, Chuang, Yi Liu, Yanhong Lu, Yulin Liao, Jun Nie, Xiaoliang Yuan y Fang Chen. "Use of a leaf chlorophyll content index to improve the prediction of above-ground biomass and productivity". PeerJ 6 (11 de enero de 2019): e6240. http://dx.doi.org/10.7717/peerj.6240.
Texto completoQiao, Lang, Dehua Gao, Junyi Zhang, Minzan Li, Hong Sun y Junyong Ma. "Dynamic Influence Elimination and Chlorophyll Content Diagnosis of Maize Using UAV Spectral Imagery". Remote Sensing 12, n.º 16 (17 de agosto de 2020): 2650. http://dx.doi.org/10.3390/rs12162650.
Texto completoGeorgiopoulou, Ioulia, Soultana Tzima, Georgia D. Pappa, Vasiliki Louli, Epaminondas Voutsas y Kostis Magoulas. "Experimental Design and Optimization of Recovering Bioactive Compounds from Chlorella vulgaris through Conventional Extraction". Molecules 27, n.º 1 (22 de diciembre de 2021): 29. http://dx.doi.org/10.3390/molecules27010029.
Texto completoNemeskéri, Eszter y Lajos Helyes. "Physiological Responses of Selected Vegetable Crop Species to Water Stress". Agronomy 9, n.º 8 (13 de agosto de 2019): 447. http://dx.doi.org/10.3390/agronomy9080447.
Texto completoZhu, Jiyou, Weijun He, Jiangming Yao, Qiang Yu, Chengyang Xu, Huaguo Huang y 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, n.º 10 (24 de mayo de 2020): 3636. http://dx.doi.org/10.3390/app10103636.
Texto completoKumar, Chandan, Partson Mubvumba, Yanbo Huang, Jagman Dhillon y Krishna Reddy. "Multi-Stage Corn Yield Prediction Using High-Resolution UAV Multispectral Data and Machine Learning Models". Agronomy 13, n.º 5 (28 de abril de 2023): 1277. http://dx.doi.org/10.3390/agronomy13051277.
Texto completoEvans, Marlene S., Richard D. Robarts y 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, n.º 5 (1 de mayo de 1995): 1037–49. http://dx.doi.org/10.1139/f95-102.
Texto completo王, 博. "Prediction of Chlorophyll Content in Rice under Arsenic Stress Based on Dynamic Fuzzy Neural Network Model". Advances in Environmental Protection 07, n.º 05 (2017): 404–13. http://dx.doi.org/10.12677/aep.2017.75054.
Texto completoMatsunaka, Teruo, Yuji Watanabe, Tadashi Miyawaki y Nobuo Ichikawa. "Prediction of grain protein content in winter wheat through leaf color measurements using a chlorophyll meter". Soil Science and Plant Nutrition 43, n.º 1 (marzo de 1997): 127–34. http://dx.doi.org/10.1080/00380768.1997.10414721.
Texto completoHaboudane, Driss, John R. Miller, Nicolas Tremblay, Pablo J. Zarco-Tejada y Louise Dextraze. "Integrated narrow-band vegetation indices for prediction of crop chlorophyll content for application to precision agriculture". Remote Sensing of Environment 81, n.º 2-3 (agosto de 2002): 416–26. http://dx.doi.org/10.1016/s0034-4257(02)00018-4.
Texto completoCroft, H., J. M. Chen, Y. Zhang, A. Simic, T. L. Noland, N. Nesbitt y 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 (abril de 2015): 85–95. http://dx.doi.org/10.1016/j.isprsjprs.2015.01.008.
Texto completoWibowo, Fachrina. "Prediction of gene action content of Na, K, and Chlorophyll for Soybean Crop Adaptation to Salinity". JERAMI Indonesian Journal of Crop Science 2, n.º 1 (1 de septiembre de 2019): 21–28. http://dx.doi.org/10.25077/jijcs.2.1.21-28.2019.
Texto completoPutra, Kielvien Lourensius Eka Setia, Fabian Surya Pramudya, Alexander Agung Santoso Gunawan y Prasetyo Mimboro. "Predicting Nitrogen Content in Oil Palms through Machine Learning and RGB Aerial Imagery". International Journal of Emerging Technology and Advanced Engineering 13, n.º 6 (25 de junio de 2023): 19–27. http://dx.doi.org/10.46338/ijetae0623_03.
Texto completoZeng, Linglin, Guozhang Peng, Ran Meng, Jianguo Man, Weibo Li, Binyuan Xu, Zhengang Lv y Rui Sun. "Wheat Yield Prediction Based on Unmanned Aerial Vehicles-Collected Red–Green–Blue Imagery". Remote Sensing 13, n.º 15 (26 de julio de 2021): 2937. http://dx.doi.org/10.3390/rs13152937.
Texto completoMineeva, N. M. "Evaluation of Nutrient-Chlorophyll Relationships in the Rybinsk Reservoir". Water Science and Technology 28, n.º 6 (1 de septiembre de 1993): 25–28. http://dx.doi.org/10.2166/wst.1993.0125.
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