Precision Health Assessment: Disease Detection using CNN Algorithms

A country's growth relies on both agricultural and industrial development. In India, two of the most widely consumed vegetables are tomato and potato. This article focuses on the detection of diseases in the leaves of these plants, offering valuable benefits to farmers and related sectors. By identifying and addressing leaf diseases promptly, the article aims to support farmers in improving crop yields and safeguarding the agricultural sector, which, in turn, contributes to the overall economic development of the country and its allied industries. Leaf disease detection holds significant importance as it enables the identification of the specific type of disease affecting the plant's leaves. This crucial information allows farmers and plant experts to take appropriate and targeted measures to combat the disease effectively. By knowing the exact nature of the leaf disease, they can implement precise treatments, preventive measures, or management strategies, thereby safeguarding the overall health and productivity of the plant. In this study, a Convolution Neural Network (CNN) is employed to enhance the accuracy of plant disease diagnosis. By utilizing this advanced technology, the aim is to minimize crop loss caused by plant diseases. Often, early changes indicative of disease are not readily apparent, making it challenging for farmers to detect issues promptly. However, the CNN's capabilities allow for early detection, enabling farmers to take timely action and effectively control the spread of diseases, thus preventing significant losses in crop yield. Automated systems offer valuable assistance by accelerating the convergence rate and reducing training time in various tasks. Additionally, they contribute to the improvement of the final classification accuracy. These benefits are particularly advantageous in domains such as machine learning and artificial intelligence, where efficient and accurate model training is crucial for optimal performance.