Rice Plant Disease Detection and Diagnosis using Deep Convolutional
  Neural Networks and Multispectral Imaging
Yara Ali Alnaggar Ahmad Sebaq Karim Amer ElSayed Naeem Mohamed Elhelw
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.