Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach
Document Type
Article
Publication Title
Sustainability (Switzerland)
Abstract
In modern agriculture, correctly identifying rice leaf diseases is crucial for maintaining crop health and promoting sustainable food production. This study presents a detailed methodology to enhance the accuracy of rice leaf disease classification. We achieve this by employing a Convolutional Neural Network (CNN) model specifically designed for rice leaf images. The proposed method achieved an accuracy of 0.914 during the final epoch, demonstrating highly competitive performance compared to other models, with low loss and minimal overfitting. A comparison was conducted with Transfer Learning Inception-v3 and Transfer Learning EfficientNet-B2 models, and the proposed method showed superior accuracy and performance. With the increasing demand for precision agriculture, models like the proposed one show great potential in accurately detecting and managing diseases, ultimately leading to improved crop yields and ecological sustainability.
DOI
10.3390/su152015039
Publication Date
10-1-2023
Keywords
CNN, disease detection, image classification, leaf disease classification, optimization
Recommended Citation
A. Abasi et al., "Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach," Sustainability (Switzerland), vol. 15, no. 20, Oct 2023.
The definitive version is available at https://doi.org/10.3390/su152015039