Enhancing Rice Leaf Disease Classification: A Customized Convolutional Neural Network Approach

• Published in: Sustainability Vol. 15; no. 20; p. 15039
• Main Authors: Abasi, Ammar Kamal, Makhadmeh, Sharif Naser, Alomari, Osama Ahmad, Tubishat, Mohammad, Mohammed, Husam Jasim
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2023
• Subjects: Agricultural production; Agriculture; Automation; Comparative analysis; Crop yields; Food supply; Neural networks; Plant diseases; Rice; Sustainability
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su152015039

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.