arXiv Open Access 2023

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

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Abstrak

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.

Topik & Kata Kunci

cs.CV eess.IV

Penulis (5)

Yara Ali Alnaggar

Ahmad Sebaq

Karim Amer

ElSayed Naeem

Mohamed Elhelw

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APA MLA BibTeX

Alnaggar, Y.A., Sebaq, A., Amer, K., Naeem, E., Elhelw, M. (2023). Rice Plant Disease Detection and Diagnosis using Deep Convolutional Neural Networks and Multispectral Imaging. https://arxiv.org/abs/2309.05818

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Informasi Jurnal

Tahun Terbit: 2023
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓