Authors

Mrs. Sundari B

Department of Electronics and Communication Engineering, Peri Institute of Technology, Chennai, Tamilnadu, India

Abstract

Rice (Oryza sativa) is one of the most important staple crops worldwide, playing a vital role in global food security. However, rice production is significantly affected by various plant diseases that reduce yield and grain quality. Early and accurate detection of these diseases is essential for effective crop management and minimizing economic losses. Traditional disease identification methods rely heavily on manual inspection by experts, which is time-consuming, subjective, and not always accessible to farmers in rural areas. This research presents an automated disease detection system for Oryza sativa using image processing and machine learning techniques. The proposed approach involves capturing leaf images, followed by preprocessing steps such as noise removal, image resizing, contrast enhancement, and background segmentation. Relevant features including color, texture, and shape characteristics are extracted to distinguish between healthy and diseased leaves. A supervised machine learning classifier, such as Support Vector Machine (SVM), Random Forest, or Convolutional Neural Network (CNN), is trained using labeled datasets to identify common rice diseases. Experimental evaluation demonstrates that the proposed system achieves high classification accuracy in detecting diseases such as leaf blast, brown spot, and bacterial leaf blight. The model’s performance is assessed using metrics including accuracy, precision, recall, and F1-score. The results confirm that image-based machine learning methods provide an efficient and reliable solution for early disease detection. The developed system offers a cost-effective and scalable tool that can assist farmers and agricultural experts in monitoring crop health. Furthermore, the study provides a foundation for integrating mobile-based applications and IoT-enabled smart agriculture systems for real-time disease monitoring and precision farming.

Keywords

Oryza sativa Rice Plant Diseases Image Processing Machine Learning Plant Disease Detection Computer Vision Leaf Image Analysis Convolutional Neural Network (CNN) Support Vector Machine (SVM) Feature Extraction Agricultural Automation

Citation of this Article

Sundari B. (2025). Disease Detection in Oryza sativa Using Image Processing and Machine Learning. Journal of Artificial Intelligence and Emerging Technologies. 2(5), 28-34. Article DOI: https://doi.org/10.47001/JAIET/2025.205005  

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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