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Prediction of Plant Nutrition State of Rice under Water-Saving Cultivation and Panicle Fertilization Application Decision Making

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Department of Agronomy, National Chung Hsing University, Taichung 402202, Taiwan
2
Crop Science Division, Taiwan Agricultural Research Institute, Council of Agriculture, Taichung 413008, Taiwan
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Department of Civil Engineering, and Innovation and Development Center of Sustainable Agriculture, National Chung Hsing University, Taichung 402202, Taiwan
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Pervasive AI Research (PAIR) Labs, Hsinchu 30010, Taiwan
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Miaoli District Agricultural Research and Extension Station, Council of Agriculture, Miaoli 36346, Taiwan
*
Author to whom correspondence should be addressed.
Academic Editors: Spyros Fountas and Thanos Balafoutis
Agronomy 2021, 11(8), 1626; https://doi.org/10.3390/agronomy11081626
Received: 1 July 2021 / Revised: 9 August 2021 / Accepted: 11 August 2021 / Published: 16 August 2021
(This article belongs to the Special Issue Smart Farming in Service of Modernizing Agriculture)
Rice is a staple food crop in Asia. The rice farming industry has been influenced by global urbanization, rapid industrialization, and climate change. A combination of precise agricultural and smart water management systems to investigate the nutrition state in rice is important. Results indicated that plant nitrogen and chlorophyll content at the maximum tillering stage were significantly influenced by the interaction between water and fertilizer. The normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE), obtained from the multispectral images captured by a UAV, exhibited the highest positive correlations (0.83 and 0.82) with plant nitrogen content at the maximum tillering stage. The leave-one-out cross-validation method was used for validation, and a final plant nitrogen content prediction model was obtained. A regression function constructed using a nitrogen nutrition index and the difference in field cumulative nitrogen had favorable variation explanatory power, and its adjusted coefficient of determination was 0.91. We provided a flow chart showing how the nutrition state of rice can be predicted with the vegetation indices obtained from UAV image analysis. Differences in field cumulative nitrogen can be further used to diagnose the demand of nitrogen topdressing during the panicle initiation stage. Thus, farmers can be provided with precise panicle fertilization strategies for rice fields. View Full-Text
Keywords: rice; water-saving cultivation; UAV remote sensing; vegetation index; nitrogen fertilizer rice; water-saving cultivation; UAV remote sensing; vegetation index; nitrogen fertilizer
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MDPI and ACS Style

Li, G.-S.; Wu, D.-H.; Su, Y.-C.; Kuo, B.-J.; Yang, M.-D.; Lai, M.-H.; Lu, H.-Y.; Yang, C.-Y. Prediction of Plant Nutrition State of Rice under Water-Saving Cultivation and Panicle Fertilization Application Decision Making. Agronomy 2021, 11, 1626. https://doi.org/10.3390/agronomy11081626

AMA Style

Li G-S, Wu D-H, Su Y-C, Kuo B-J, Yang M-D, Lai M-H, Lu H-Y, Yang C-Y. Prediction of Plant Nutrition State of Rice under Water-Saving Cultivation and Panicle Fertilization Application Decision Making. Agronomy. 2021; 11(8):1626. https://doi.org/10.3390/agronomy11081626

Chicago/Turabian Style

Li, Guan-Sin, Dong-Hong Wu, Yuan-Chih Su, Bo-Jein Kuo, Ming-Der Yang, Ming-Hsin Lai, Hsiu-Ying Lu, and Chin-Ying Yang. 2021. "Prediction of Plant Nutrition State of Rice under Water-Saving Cultivation and Panicle Fertilization Application Decision Making" Agronomy 11, no. 8: 1626. https://doi.org/10.3390/agronomy11081626

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