The purpose of this study is to monitor the growth of rice on a weekly basis by multicopter. The data collected were used to 1) determine whether top-dressing was required, 2) assess the potential for lodging risk, 3) estimate yield, 4) create maps of rice growth for protein content estimation. The normalized difference vegetation index (NDVI) and green excess index (2G_RBi) were both suitable for use as monitoring indices, and their application revealed the following: 1) The standard deviation of 2G_RBi was thought to be useful for determining the timing of top-dressing. The timing of top-dressing application was estimated most effective 10-15 days after maximum standard deviations were recorded. Areas with poor growth could also be identified using NDVI of the non-productive tillering stages and areas where top-dressing needed to be applied could be identified. 2) To diagnose lodging, plant height was estimated using the differences between the digital surface model (DSM) before the field was prepared for planting and on the monitoring day, and the risk of lodging 14 days before heading was shown for the entire area. 3) Yield was highly correlated with NDVI of the heading stage, and yield maps were created using a yield estimation equation. 4) With regard to eating quality, a strong correlation was observed between the protein content of brown rice and NDVI values from the heading stage to the first half of the maturing stage(15 days after heading stage), and accurate maps of eating quality were created.
The monitoring of rice growth using a multicopter is both safe and cost effective for individual farmers. By producing objective data and maps for assessments of top-dressing, lodging risk, yield, and protein contents, the findings presented here were shown to be useful for the detailed management of crop growth in fields.
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