Detection and Management of Water Stress at Plants by Deep Learning and Image processing Case-study of Tomato

This project aims to develop an innovative technique for detecting water stress in tomato plants using deep learning and image processing techniques, and to integrate it into a mobile application for real-time monitoring. The methodology adopted includes the acquisition and preprocessing of image data, the construction and training of a deep learning model, and the development of a user-friendly mobile application. The results show a promising performance of the model in the precise detection of water stress, confirming the usefulness and usability of the developed mobile application.