Estimating the Protein Concentration in Rice Grain Using UAV Imagery Together with Agroclimatic Data

Akira Hama,Akihiko Kondoh,Kei Tanaka,Atsushi Mochizuki,Yasuo Tsuruoka

doi:10.3390/agronomy10030431

Abstract

Global warming and climate change can potentially change not only rice production but also rice nutrient content. To adapt a rice-dependent country’s farming to the impacts of climate change, it is necessary to assess and monitor the potential risk that climate change poses to agriculture. The aim of this study was to clarify the relationship between rice grain protein content (GPC) and meteorological variables through unmanned aerial vehicle remote sensing and meteorological measurements. Furthermore, a method for GPC estimation that combines remote sensing data and meteorological variables was proposed. The conclusions of this study were as follows: (1) The accuracy and robustness of the GPC estimation model were improved by evaluating the nitrogen condition with the green normalized difference vegetation index at the heading stage (GNDVIheading) and evaluating photosynthesis with the average daily solar radiation during the grain-filling stage (SRgrain-filling). GPC estimation considering SRgrain-filling in addition to GNDVIheading was able to estimate the observed GPC under the different conditions. (2) Increased temperature from the transplantation date to the heading stage can affect increased GPC when extreme temperature does not cause the heat stress.

Highlights

The United Nation’s 17 Sustainable Development Goals (SDGs) came into effect in January 2016, and they will continue until 2030
The results of simple linear regression (SLR) analysis demonstrate that the higher the GNDVIheading, the higher the grain protein content (GPC)
We proposed the simple method for GPC estimation by using GNDVIheading together with

Summary

Introduction

The United Nation’s 17 Sustainable Development Goals (SDGs) came into effect in January 2016, and they will continue until 2030. Many SDGs are related to agriculture (e.g., SDG2 Zero hunger: End hunger, achieve food security and improved nutrition and promote sustainable agriculture) that require urgent action from both developing and developed countries [1]. Implications of climate change, such as global warming, have the potential to interrupt progress being made toward SDGs related to agriculture [2]. To adapt agriculture to the impacts of climate change, it is necessary to assess and monitor the potential risk that climate change poses to agriculture. Remote sensing and imagery techniques are useful in monitoring and detecting the effects of climate change on crops [3,7,8,9,10]; implementation of remote sensing and imagery techniques for monitoring and detecting the effects of climate change is still underdeveloped [11]

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Conclusion