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

Ammar Kamal Abasi (), Sharif Naser Makhadmeh, Osama Ahmad Alomari, Mohammad Tubishat and Husam Jasim Mohammed
Additional contact information
Ammar Kamal Abasi: Department of Machine Learning, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI), Abu Dhabi P.O. Box 131818, United Arab Emirates
Sharif Naser Makhadmeh: Department of Data Science and Artificial Intelligence, University of Petra, Amman P.O. Box 11196, Jordan
Osama Ahmad Alomari: Department of Computer Science and Information Technology, College of Engineering, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab Emirates
Mohammad Tubishat: College of Technological Innovation, Zayed University, Abu Dhabi P.O. Box 144534, United Arab Emirates
Husam Jasim Mohammed: Department of Business Administration, College of Administration and Financial Sciences, Imam Ja’afar Al-Sadiq University, Baghdad 10001, Iraq

Sustainability, 2023, vol. 15, issue 20, 1-18

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.

Keywords: disease detection; leaf disease classification; CNN; image classification; optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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