Machine Learning-Based Rice Disease Diagnosis Through Joint Utilization of Satellite, Drone, and Weather Data

Remote sensing and machine learning techniques enable precise diagnosis of crop growth anomalies, providing an effective means to mitigate production losses caused by disease outbreaks while supporting sustainable agricultural management. This study aims to detect rice diseases using satellite, drone, and weather data in a timely manner. A random forest model for rice disease detection was developed using drone imagery collected in 2023 year, where disease-damaged pixels were classified through K-means clustering, and the corresponding damaged areas were used for rice paddy disease classification model training. This model has been applied to agricultural fields in 2024 year as follows. First, Sentinel-1 and Sentinel-2 satellite data were utilized to classify paddy rice fields, with irrigated areas identified through the normalized difference vegetation index, land surface water index, and VV polarization. Second, the risk of rice disease occurrence was calculated based on air temperature, relative humidity, and precipitation. These variables represent weather conditions that can cause crop diseases. Third, drone measurements were conducted to monitor the abnormal growth of paddy rice when the risk score increased. Fourth, the location of disease outbreaks was detected using the random forest model, which uses surface reflectance at blue, green, red, red-edge, and near-infrared wavelengths as input data. Subsequently, drone spraying operations were carried out to reduce crop damage caused by the identified diseases. These results highlight the potential of agricultural management using remote sensing techniques.

Acknowledgments: This research was funded by RDA, grant number RS-2022-RD010059.