Prediction of Plant Leaf Diseases using Drone and Image Processing Techniques

In Asia, there are many countries where paddy serves as the primary agricultural product. Diseases on paddy plants have the potential to reduce paddy production and damage its quality. Detecting the disease earlier will help to prevent serious infection of the paddy and also minimize crop loss. Drones have recently been utilized for agricultural surveillance, along with cameras and GPS sensors. It is a different instrument for swiftly and autonomously obtaining information in a large area. This study describes a drone implementation system that depends upon the Internet of Things (IoT) architecture that uses information in actual instances, information collection, and examination of information strategies to increase paddy productivity. By using a GPS sensor to specify the location in the actual instances, the system is capable of mapping the location of diseased paddy plants on paddy fields along with data analysis. With the installation of the IoT architecture, this framework was created and also put forth as an initial system to support the systems earlier and to detect disease in actual time.