Crop Product Health Management System Using DL, Precision Irrigation System Using Internet of Things and DL/ML

Effective water management and crop health greatly affect agricultural sustainability and production. This paper suggests an integrated Crop Product Health Management System using Deep Learning (DL) for disease detection and Precision Irrigation System driven by the Internet of Things (IoT) and Machine Learning (ML) to maximize water use. The technology detects illnesses, nutrient shortages, and insect infestations in real-time using com-puter vision-based DL models to examine crop photos. IoT-enabled soil moisture sensors, weather data, and predictive ML models concurrently provide exact irrigation scheduling, therefore guaranteeing appropriate water distribution depending on real-time environmental circumstances. By means of real-time insights, water waste reduction, and crop health enhancement, the suggested framework improves decision-making. While ML algorithms examine sensor data to forecast irrigation needs, the DL models are trained on vast sets of plant disease photos. Comparatively to conventional techniques, experimental findings show better yield quality, lower water usage, and efficient disease control. This study emphasizes the possibilities of artificial intelligence-driven solutions for smart agriculture so guaranteeing food security and sustainable farming methods. Modern agricultural systems can benefit from a strong, scalable solution provided by the combination of DL, IoT, and ML, hence driving farming more data-driven and resource-efficient.