Rice Plant Disease Detection and Diagnosis using Deep Convolutional Neural Networks and Multispectral Imaging

TL;DR

This paper presents a deep learning approach utilizing multispectral and RGB images for early detection of rice plant diseases, aiming to reduce yield loss and improve disease diagnosis accuracy.

Contribution

It introduces a new multispectral dataset and demonstrates that combining multispectral with RGB data enhances disease detection performance.

Findings

Multispectral data improves F1 accuracy over RGB alone.

The dataset includes Red, Green, and Near-Infrared channels.

Deep learning pipeline effectively detects rice diseases.

Abstract

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.