A Prototype Modeling of a Smart Agriculture Monitoring System using IOT Based Drones for Spraying Pesticides

Abstract Due to the health complications arising from the use of pesticides and insecticides, a considerable number of farmers in the agricultural sector are facing significant health challenges. The conventional practice of manually applying pesticides to crop fields through traditional irrigation systems has adverse effects on health. An optimistic alternative to this manual approach involves the integration of drones for pesticide application. This substitution not only lessens the physical burden on farmers but also accelerates task completion, as drones can efficiently carry and spray insecticides across the entire farm. The two indispensable agricultural processes crucial for achieving optimal crop production include fertilizer application and pesticide use. Farmers can easily navigate the user-friendly design of this application, enhancing their ability to carry out agricultural tasks with efficiency. A quadcopter, commonly known as a drone, is employed for agricultural purposes to uniformly distribute pesticides across a field, thereby significantly reducing the labor required by farmers. Through an Android app, the farmer gains control over the drone, establishing a seamless connection with the device via the drone's Wi-Fi module. The drone is capable of precisely navigating the farmer's land, efficiently spraying pesticides across the entire area, regardless of crop variety or field shape. Addressing the need for a comprehensive model to enhance yields, especially in large irrigation areas with diverse crops, seasons, and influencing factors, a formal model of a drone-based smart irrigation system prototype is presented in this study. The developed model utilizes the Event-B methodology, and its validity is verified through the support of the RODIN tool. This approach ensures a robust and reliable foundation for the implementation of an advanced agricultural irrigation system, taking into account various parameters that contribute to successful and efficient crop management.