A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework

• Main Authors: Bari, Bifta Sama, Islam, Md Nahidul, Rashid, Mamunur, Hasan, Md Jahid, Mohd Azraai, Mohd Razman, Musa, Rabiu Muazu, Ahmad Fakhri, Ab. Nasir, Majeed, Anwar P.P. Abdul
• Format: Article
• Language: English
• Published: PeerJ Inc. 2021
• Subjects: QA75 Electronic computers. Computer science; QA76 Computer software; T Technology (General); TJ Mechanical engineering and machinery; TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://umpir.ump.edu.my/id/eprint/35164/1/A%20real-time%20approach%20of%20diagnosing%20rice%20leaf%20disease%20using%20deep%20learning-based%20faster%20R-CNN%20framework.pdf; http://umpir.ump.edu.my/id/eprint/35164/; https://doi.org/10.7717/PEERJ-CS.432; https://doi.org/10.7717/PEERJ-CS.432

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food securit...