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

• URL: http://arxiv.org/abs/2309.05818v1
• Date: Mon, 11 Sep 2023 20:51:21 GMT
• Title: Rice Plant Disease Detection and Diagnosis using Deep Convolutional Neural Networks and Multispectral Imaging
• Authors: Yara Ali Alnaggar, Ahmad Sebaq, Karim Amer, ElSayed Naeem, Mohamed Elhelw
• Abstract summary: Egypt is the highest rice producer in Africa with a share of 6 million tons per year. Rice blast disease is responsible for 30% loss in rice production worldwide. This paper introduces a public multispectral and RGB images dataset and a deep learning pipeline for rice plant disease detection.
• Score: 1.0499611180329802
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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