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Smart Agriculture With Autonomous Unmanned Ground and Air Vehicles: Approaches to Calculating Optimal Number of Stops in Harvest Optimization and a Suggestion
Abstract
This study researches smart agriculture and its components, robotic systems and machine learning algorithms, development of agricultural robots, and their effects on the industry. In application, it is aimed to collect the harvest of autonomous unmanned aerial vehicles and UGVs in communication with each other by means of time minimization of the target. It wanted to be tested with different approaches for an optimal number of stops by using particle swarm optimization. Deterministic, binary mixed (0-1) integer modeling was used to determine the optimal picking time of the apples allocated to the stalls with the k-means method. With this modeling, it has been determined which unmanned aerial vehicle will be collected and how it is calculated whether the air vehicle has collected the apple or not using 0-1 binary modeling. The route of the unmanned UGV was made by using the nearest neighbor, nearest insertion, and 2-opt methods. This study has been extended and reviewed by the summary paper at International OECD Studies Conference March 2020, Ankara, Turkey.
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