A Lightweight Convolutional Neural Network for Rice Diseases Classification

• Published in: 14th IEEE International Conference on Control System, Computing and Engineering, ICCSCE 2024 - Proceedings
• Main Author: Ismail M.A.; Rashid M.R.; Ahmad K.A.; Setumin S.; Bakar S.J.A.; Ani A.I.C.
• Format: Conference paper
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85207088496&doi=10.1109%2fICCSCE61582.2024.10696617&partnerID=40&md5=465c894be73b62879b083b495252a1dc

Rice, also known as Oryza Sativa, is widely recognized as an important source of food crops and a primary source of nutrients for human beings as it is rich in carbohydrates, providing a high-energy food. The United Nations General Assembly stated that rice is the staple food of more than half the world's population. However, pests and disease outbreaks are the major problems for farmers as this rice crop is susceptible to fungi and viruses. Leaf Brown Spot, Leaf Blast, and Leaf Hispa are among the most well-known diseases that affect the rice crop. Accurate and fast classification of these diseases is essential for implementing timely and targeted approaches to mitigate crop losses. This paper investigates the feasibility of our proposed lightweight model to be used for the classification task with a limited number of training samples. The images were preprocessed to enhance the color and edges. The K-means clustering method was used for image segmentation to separate the affected regions from the background. The proposed convolutional neural network model was made from scratch to have only three convolutional layers. The results demonstrate that the proposed model outperforms the VGG-16 model for overall performance with 90% and 85% for both batch sizes. © 2024 IEEE.