Mapping of Rice Growth Using Low Altitude Remote Sensing by Multicopter

Abstract

The purpose of this study was to monitor the growth of rice on a weekly basis by multicopter. The data collected were used to 1) determine whether topdressing was required, 2) assess the potential for lodging risk, 3) estimate yield, and 4) create maps of rice growth for estimated protein contents. The conventional NDVI and 2 G_RBi were both suitable as monitoring indices, and their application revealed the following: 1) The standard deviation of 2 G_RBi values was found to be useful for determining the timing of topdressing, which was estimated to be most effective 10-15 days after maximum standard deviations were recorded. Areas with poor growth could also be identified by using the NDVI values of the non-productive tillering stages and areas where topdressing was needed could be identified. 2) To diagnose lodging, plant length was estimated using the differences between the 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 found to be highly correlated with the NDVI values 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 the 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. The findings presented here show that the use of this method to obtain objective data and maps to assess topdressing, lodging risk, yield, and protein content is useful for the detailed management of crop growth in fields.