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ABSTRACT
This study aims to design and construct an autonomous agricultural pesticide spraying robot toaddress the challenges faced by traditional manual spraying methods. The purpose is to improveefficiency, reduce pesticide usage, and minimize human exposure to harmful chemicals. Therobot is envisioned to navigate through fields autonomously, identifying and targeting specific areas for pesticide application with precision and accuracy. The methodology involves integrating state-of-the-art sensors, such as LiDAR and cameras, ontoa robust mobile platform. Machine learning algorithms are employed for real-time crop and weed detection, enabling the robot to differentiate between crops and unwanted vegetation. Pathplanning algorithms are utilized to optimize the spraying route, ensuring complete coveragewhile minimizing overlap and avoiding obstacles. Additionally, the robot is equipped withatankand spraying mechanism capable of dispensing pesticides in controlled doses. The functionality tests (speed and load tests) of the agricultural pesticide spraying robot yieldedpromising results. The robot demonstrated a significant reduction in pesticide usage comparedtoconventional methods while maintaining crop yield and quality. The speed of the robot inthecoverage of a slightly dense area of land (45m by 45m) was calculated to have been405m2 /minute (6.75m2 /second). In carrying out the load test, the pesticide load was applied incrementally from the robot's nominal tank capacity to its maximum designed pay load limit while closely monitoring the behavior of the design. The maximum designed payload limit of therobot is 3 litres of the pesticide load. The efficient response of the robot to the load conditionsunderscored the robustness and adaptability of its design. Overall, the successful implementation of the robot signifies a step towards sustainable and efficient agricultural practices, with potential implications for reducing environmental impact and improving farmer livelihoods
