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smart agri with wild life and solenoid and npk sensor
Modern agriculture demands smart, scalable, and autonomous solutions to tackle challenges such as crop disease, wildlife intrusion, and inefficient irrigation. This project presents a Smart Agriculture System integrating IoT and Machine Learning (ML) to monitor environmental conditions, detect leaf diseases, and deter wildlife in real time. At the core, an ESP32 microcontroller collects data from a temperature sensor, soil moisture sensor, water level sensor, and other components. A solenoid valve, controlled through a relay, along with a water pump, automates irrigation based on soil conditions. For plant health, an ESP32-CAM, interfaced via a camera converter, captures leaf images which are processed using ML algorithms through a Flask-based web server to identify signs of disease. Wildlife detection is achieved through real-time video analysis, triggering a buzzer, LED light, or servo motor-based deterrent system to scare animals away. A toy-based setup with a container simulates the real-time farm environment, while an external adaptor powers the system for stability. The dashboard offers real-time monitoring, historical logs, and remote actuation features. This solution promotes precision agriculture, resource optimization, and crop protection through intelligent automation.