Agriculture is rapidly changing as it adopts robotic technologies to meet the increasing global food demand. Automated harvesting systems are becoming a practical solution to key issues like labor shortages, rising costs, and the need for better productivity and quality. This paper presents a detailed study on the design, operation, and real-world applications of intelligent robotic systems for automated harvesting. The proposed system uses technologies like artificial intelligence, machine learning, machine vision, and sensor-based systems to accurately detect, classify, and harvest crops. Vision systems with cameras and image processing identify ripeness based on color, size, and texture. Sensors provide real-time environmental data to assist in decision-making. Robotic arms with adjustable grippers carefully handle crops, minimizing mechanical damage and post-harvest loss. Additionally, the paper covers system design, navigation strategies, and real-time data processing methods that enhance field operations. Despite its benefits, the system has challenges, including high initial costs, difficulty managing different crop types, and sensitivity to environmental changes like lighting and weather. In conclusion, the study points out future improvements such as integrating the Internet of Things (IoT), cloud computing, and autonomous navigation systems. These advancements can improve scalability, cost-effectiveness, and overall performance. Intelligent robotic harvesting systems could transform modern agriculture by enabling precise farming, enhancing sustainability, and ensuring food security.
Thomas et al. (Sun,) studied this question.