Rice Plant Disease Detection and Diagnosis using Deep Convolutional Neural Networks and Multispectral Imaging
This addresses early disease detection for rice farmers to reduce production losses, but it is incremental as it applies existing deep learning methods to a new agricultural dataset.
The paper tackles rice blast disease detection by introducing a public multispectral and RGB image dataset and a deep learning pipeline, achieving higher F1 accuracy with multispectral data compared to RGB-only input.
Rice is considered a strategic crop in Egypt as it is regularly consumed in the Egyptian people's diet. Even though Egypt is the highest rice producer in Africa with a share of 6 million tons per year, it still imports rice to satisfy its local needs due to production loss, especially due to rice disease. Rice blast disease is responsible for 30% loss in rice production worldwide. Therefore, it is crucial to target limiting yield damage by detecting rice crops diseases in its early stages. This paper introduces a public multispectral and RGB images dataset and a deep learning pipeline for rice plant disease detection using multi-modal data. The collected multispectral images consist of Red, Green and Near-Infrared channels and we show that using multispectral along with RGB channels as input archives a higher F1 accuracy compared to using RGB input only.