It provides for the development and evaluation of a web-based application meant to aid in the identification of rice crop stress and possible mitigation measures. Traditional methods have been used, but this approach should replace the manual classification that is time-consuming, tedious, and complex, as it leverages advanced machine learning algorithms to analyze images of rice crops, resulting in the accurate detection of various stress factors such as nutrient deficiencies, pest’ infestations, and water stress. In order to ensure the system has high accuracy and reliability for real-world situations, training and testing were conducted using an inclusive dataset that contained labeled images that featured different kinds of stressed rice crops. Also, besides the classification of stress, the app recommends specific advice for particular stress types, such as fertilization, pest control, and irrigation techniques. The recommendations are generated from a massive database with expert agricultural tips and current research to ensure that they give accurate and practical solutions. The usability and effectiveness of the application were assessed through field trials with local farmers and agricultural experts. Results indicated a significant improvement in the early detection and management of crop stress, leading to increased yields and resource efficiency. This web application aims to empower farmers with timely and accurate information, foster sustainable agricultural practices, and enhance food security.