Sustainable Agriculture Through IoT Based Plant Disease Identification and Crop Management Using Deep Learning

Early detection of plant diseases and proper crop management is essential to maximize the yield of plants and reduce losses, which in turn would help in the quest for sustainable agriculture. This project utilizes IoT (Internet of Things) technology along with deep learning to better control the monitoring of plant health and manage agricultural crops. Using the ESP32-CAM, an Internet of Things-enabled camera module, the system collects high-resolution photographs of crops that are processed instantaneously. We process such images through advanced deep learning algorithms coded in Python and MATLAB to identify plant diseases and crop health. The implementation of IoT is vital as it makes monitoring and collecting data seamless and effortless, while the deep learning models aid in disease prediction and categorization. The automated nature of the solution helps with timely interventions and data-driven decisions in order to bolster sustainable farming practices. This project aims to assist in building the crop management more effective, reducing the dependence on chemical treatments, by using the improved process of IoT and deep learning. This paper proposes an Internet of Things (IoT)-based system for non-interactive real-time plant disease detection and crop management using deep learning techniques.