Enhanced Rice Leaf Disease Detection: A Hybrid Deep Learning Approach

Enhanced Rice Leaf Disease Detection: A Hybrid Deep Learning Approach

Himanshu Kumar Bhagat

B.E CSE (AIML)

Chandigarh University 22BAI70375@cuchd.in

Vatsal Singh

B.E CSE (AIML)

Chandigarh University 22BAI71407@cuchd.in

Vijay Bhardwaj Professor

Chandigarh University prof.dr.bhardwaj@gmail.com

Abstract- This research proposes a hybrid deep learning model for automated rice leaf disease detection, addressing the limitations of traditional methods. A pre-trained ResNet50V2 CNN is combined with a custom fully connected network, enhanced with batch normalization, dropout, and L2 regularization. Trained on 15,023 images from nine disease classes and healthy leaves, the model utilizes extensive data augmentation techniques such as rotation, shifts, zoom, and flipping to improve robustness. The model achieved 99.53% accuracy, surpassing existing benchmarks, and provides a reliable system for timely disease detection, enhancing rice crop management and yield.

Keywords- Hybrid deep learning model, Rice leaf disease detection, Fully connected layer, Data augmentation, Dropout, Batch normalization, L2 regularization.