Smart Harvesting Solutions: Robotics in Fruit and Vegetable Harvesting for Reduced Labor Dependency

Abstract

Traditional agricultural harvest has a large inefficiency and a dependence on labor, as there is a serious shortage of manpower in many regions. Most mechanized systems are not precise, incur high production damage rates, and cannot flexibly harvest multiple crops or be selective based on ripeness. The paper presents a robotic harvesting system that aims to automatically harvest fruit and vegetables with high reliability, using computer vision, Convolutional Neural Network (CNN), depth sensors, and reinforcement learning. Adaptive arm control minimizes produce handling while maximizing yield and classification reducing waste by classifying ripeness and implicitly planning optimal picking sequence. Experimental results show a remarkable performance boost with 95% accuracy in detecting mature fruits, 50% reduction in time/acre for harvesting & 5% damage rates which significantly brings down operational price. These provide a sustainable, scalable solution to address agricultural requirements and improve both the quality of yield and farm labor.