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

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

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...

Descripció completa

Dades bibliogràfiques
Autors principals: 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
Idioma:English
Publicat: PeerJ Inc. 2021
Matèries:
QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
TJ Mechanical engineering and machinery
TK Electrical engineering. Electronics Nuclear engineering
Accés en línia: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

Internet

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

Ítems similars