Source of Publication
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.
disease detection, leaf disease classification, CNN, image classification, optimization
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abasi, Ammar Kamal; Makhadmeh, Sharif Naser; Alomari, Osama Ahmad; Tubishat, Mohammad; and Mohammed, Husam Jasim, "Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach" (2023). All Works. 6169.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series