Abstract
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.
Highlights
Rice (Oryza sativa L.) is a staple food crop satisfying food demand for 50% of the global population
The on-site investigations and unmanned aerial vehicles (UAVs) photography were complete within the same week, and they ended at the rice maturity stage (Table S1)
Rice nutrition state monitoring can be conducted with intelligent technology for decision making
Summary
Rice (Oryza sativa L.) is a staple food crop satisfying food demand for 50% of the global population. Extreme weather results in problems of high temperature, drought, and flooding, and crop growth is hindered. With the increasing global average temperature, the yields of major food crops are apparently decreasing [3]. A flood-based irrigation system is generally used in the production and cultivation management of rice, and the planting process requires a large amount of irrigation water. Water-saving cultivation management methods that reduce the amount of irrigation water in the field without influencing rice yield are urgently required. Culture environments and management methods have different influences on the physiological traits of rice. They influence rice yield and quality [15,16,17]. An intelligent production cultivation management model was developed to achieve the goal of intelligent cultivation management
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