Automated Rice Disease Identification and Management Using Machine Learning

The abstract provided brings attention to a project aimed at enhancing rice crop well-being by using artificial intelligence and machine learning. Rice as an essential food grain for over half of the world’s population is highly susceptible to various plant diseases that can highly decrease yield and jeopardize food security. To solve this, the project presents an AI-based system that can automatically identify rice plant diseases using images of rice leaves. The system utilizes supervised learning and unsupervised learning methodologies. In supervised learning, models including Convolutional Neural Networks (CNNs) are trained on labeled data in order to identify and classify known diseases correctly. Concurrently, unsupervised learning techniques such as K-Means clustering are employed to detect patterns and clusters in the image data that could reflect emerging or previously undesignated types of diseases. This two- pronged method not only enhances the precision of disease identification but also enables the system to adjust to new plant health concerns as they emerge. A farmer can easily load photos of the infected rice leaves, and the system will study the image and diagnose the disease as well as suggest remedies accordingly. The system saves on expertise dependency in agriculturalists, and the process leads to early identification and encourages precision farming, finally resulting in curbing losses during crop growth and maximizing the efficiency of food production.