Agricultural Robot: Enhancing Farming Efficiency Through IoT and AI/ML

Autonomous Multi-Purpose Agricultural Robot with Inbuilt Machine Learning for Precision Farming Processes This study proposes a new autonomous agricultural robot with integrated multiple precision farming mechanisms and sophisticated machine learning features to improve crop management and minimize human intervention. The prototype incorporates six principal functions: seed planting with servo-regulated precision dispensing at computed time intervals; preparation of soil through a multi-layered cutting system for effective ploughing; specific irrigation through an adjustable sprinkler system; autonomous mobility over diversified terrain; smart weed control through mechanical grasping appendages; and accurate pesticide spraying through a calibrated sprayer system. The robot’s weed control system is an important innovation, using a convolutional neural network model trained on a multispecies image dataset of weeds and crops to deliver 94.7% accuracy for distinguishing between crops and weeds under real-time field conditions. The vision system causes the mechanical hands to selectively pluck out weeds while leaving crops intact, cutting herbicide use by 78% over traditional methods. Field trials that were undertaken over the course of three cultivation seasons prove that the integrated platform boosted operating efficiency by 65% and lowered water use by 43%, pesticide application by 78%, and labour demands by 82% compared to conventional methods of farming. Crop production was boosted by 24% on average, with significant gains in areas of limited resources. The modular nature of the system permits tailor-made solutions as per specific crop needs and field conditions. the performance metrics used for evaluation are Precision, Recall, mAP50 and mAP50-95. This study proves the feasibility of consolidating several farming operations into one autonomous platform based on machine learning, providing a sustainable solution to overcome agricultural labour shortages while enhancing resource efficiency and crop yields.