Abstract

Agriculture is the efficient use of different technologies and methods to cultivate land, raise crops, and rear animals to produce food, fuel, fibre, and other necessities for human survival. The application of Unmanned Aerial Vehicle (UAV) technologies to smart farming transforms yield planning, precision spraying, and crop monitoring, resulting in improved resource management and higher production for farmers globally. There is a labor shortage as a consequence of the growing technology that has reduced the number of people working in agriculture. This issue can be resolved with the use of agricultural machinery, such as drones, to spray pesticides. Unfortunately, spraying pesticides in the mountainous orchards is difficult due to the environment, culture, and limitations in operation. The Bellman-Ford technique is used to update the distance estimates until resolution to find the optimal route in weighted elements. We suggest smart agriculture instructing drones to avoid obstructions and choose the most optimal routes for spraying pesticides in an approach that minimizes battery usage, pesticide costs, and operational challenges by fusing reinforcement learning (RL) using the Policy Gradient Method (PG) and the Gaussian filter using data preprocessing. This research conducted experiments using various reward systems, flying direction granularities, and settings to develop a strategy appropriate for slope orchards. When handling path planning problems in complicated contexts, RL works more accurately than the accurate in PG methods.