Authors - Sneha S. Temgire, Y.S. Angal, Ashwini V. Waghmare, Chetana Sharma, Ashwini Gajre Abstract - Agriculture is essential for food security and economic growth, but traditional farming faces challenges such as plant diseases, inefficient irrigation, and labour-intensive monitoring. This project focuses on automated and manual irrigation in addition with plant disease detection and growth monitoring using image processing on a Raspberry Pi 3B+. By leveraging TensorFlow Lite and OpenCV, the system can analyze plant health and trigger appropriate irrigation actions. The aim is to design accurate agriculture system by reducing water wastage and improving crop monitoring. A key feature of this system is web-based monitoring, where the Raspberry Pi transmits real-time plant health data and sensor readings to an HTML-based webpage. Users can remotely access this data via a web interface, enabling continuous monitoring of plant conditions, disease status, and irrigation control from any location. By combining machine learning, image processing, IoT automation, and real-time web-based monitoring, this system reduces manual labour, optimizes water usage, and ensures early disease detection. The web interface enhances accessibility, allowing farmers and researchers to track plant health remotely and make informed decisions.
