A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework.  To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research.  The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions.  The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets.  The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.
Faster R-CNN
PeerJ. Computer science
Bari Bifta Sama BS ;Islam Md Nahidul MN ;Rashid Mamunur M ;Hasan Md Jahid MJ ;Razman Mohd Azraai Mohd MAM ;Musa Rabiu Muazu RM ;Ab Nasir Ahmad Fakhri AF ;P P Abdul Majeed Anwar A 0000-0002-3094-5596