Sustainable Agriculture: An IoT-Based Solution for Early Disease Detection in Greenhouses

The Internet of Things (IoT) is a cutting-edge paradigm that involves the interconnection of multiple advanced sensors and peripheral devices to enable the automation and orchestration of various complex systems. In the agricultural context, smart devices are utilized to acquire and transmit data from a diverse range of sensors that monitor intricate environmental parameters crucial to ensuring optimal plant growth. These factors encompass but are not limited to humidity, temperature, soil moisture, and water pH. Continuous monitoring and control of these environmental aspects are essential for maximizing plant growth and yield. Manual disease monitoring is labor-intensive and requires specialized expertise in plant pathology. To overcome these challenges, convolutional neural network models employing deep learning techniques have been developed to detect and diagnose diseases in plants. Our models utilize images of diseased plant leaves to accurately classify the health status of plants. The dataset includes healthy plants, as well as those affected by Bacterial Blight, Anthracnose Bacteria, and Thrips insects. The proposed IoT framework can significantly enhance crop monitoring, enabling farmers to manage their crops effectively and increase productivity through state-of-the-art technologies.