National Agriculture and Food Research Organization (NARO) has developed a new system which calculates the amount of additional fertilizer needed to stabilize rice yields based on the simple and accurate growth diagnosis by merging the normalized difference vegetation index (NDVI) obtained from the sky by unmanned aerial vehicle (UAV-NDVI) with the NDVI collected on the ground by GreenSeeker handheld crop sensor (GS-NDVI). It is expected that this system will contribute to stabilizing both the yield and quality of rice.
Overview
It is crucial to stabilize rice yields and quality as it helps stabilizing farmers' profits. However, extreme weather and climate change in recent years may be a risk. Another change is accumulation of farmland among few farmers due to the aging of farmers. This leads to a growing demand for a technology that can be introduced to efficiently cultivate paddy rice.
As one of the solutions, unmanned aerial vehicles (UAV) are popularly used in the paddy field management for spraying pesticides and fertilizers. It is also utilized to collect image data from the sky. This data called "UAV-NDVI" is used for "growth diagnosis," in which the growth condition of rice can be grasped.
Conventionally, growth diagnosis used to be done purely based on GS-NDVI collected on the ground. It is much easier to collect UAV-NDVI comparing to GS-NDVI. However, UAV-NDVI can be affected by the height of the sun and the amount of sunshine as UAV measures the sun light reflected by the plant community. It may result in a gap between UAV-NDVI and GS-NDVI.
To solve this, a few GS-NDVI collected at a several spots on the ground can be used to improve the accuracy of UAV-NDVI. This is simpler when compared to the case where all data needed for a growth diagnosis is collected as GS-NDVI. Also, the result is more precise than when it is diagnosed solely based on UAV-NDVI. The system calculates the amount of the additional fertilizer needed to achieve the targeted yield based on the growth diagnosis results.
It is expected that large-scale producers and private companies will utilize the results of this research to stabilize the yield and quality of rice produced in Japan.
Publication
Nakano, H., Tanaka, R., Guan, S., Ohdan, H., 2023. Predicting rice grain yield using normalized difference vegetation index from UAV and GreenSeeker. Crop and Environment.
https://doi.org/10.1016/j.crope.2023.03.001
For Inquiries
Contact: http://www.naro.go.jp/english/inquiry/index.html
