Description:Tomato is one of the most widely grown and consumed vegetables globally. However, tomato plants are susceptible to various diseases that can significantly reduce crop yield and quality. Early detection and treatment of these diseases are crucial to prevent widespread crop losses. In this project, we propose an innovative solution to this problem, which involves the development of an automated tomato disease detection and pesticide application system. The system consists of a camera module, an Arduino microcontroller board, and a customized pesticide spray mechanism. The camera module captures high-resolution images of tomato plants at regular intervals, which are then analyzed using machine learning algorithms to detect and classify any disease present. The Arduino board controls the entire process, including image acquisition, disease detection, and pesticide application. Based on the severity of the disease, the system delivers a customized amount of pesticide to the affected area, reducing the amount of pesticide used overall. The proposed system has several advantages over conventional methods of tomato disease detection and treatment. It provides a time-efficient and cost-effective solution for early detection and treatment of tomato diseases, which can significantly improve crop yield and quality. The system also reduces the amount of pesticide used, making it a more environmentally. Overall, the results of the project demonstrate the effectiveness and feasibility of the proposed system for tomato disease detection and pesticide application, with potential applications in other crops as well. , Claims:The proposed automated tomato disease detection and pesticide application system provides a cost-effective and time-efficient solution for early detection and treatment of tomato diseases, resulting in improved crop yield and quality compared to conventional methods.
2. By integrating computer vision and machine learning techniques, the proposed system can accurately identify and classify tomato diseases with high accuracy, reducing the need for manual inspection and diagnosis.
3. The customized pesticide spray mechanism used in the proposed system minimizes the amount of pesticide used overall, making it a more environmentally friendly and sustainable solution for precision agriculture.
4. The proposed system has the potential to be applied to other crops and can be adapted to detect a wide range of diseases and pests, making it a versatile tool for precision agriculture.