Key points are not available for this paper at this time.
Harvesting apples is one of the most apple-challenging operations; its process is labor-intensive, and for various reasons, automation has yet to advance as swiftly as it might. Researchers have concentrated on developments in robotics and automated apple harvesting, two domains with a plethora of opportunities and difficulties that require more evaluation for future growth and quality. In this paper, we provide an overview of apple harvesting by beginning with a perspective that focuses on vision techniques and recognition systems. We then cover the outcomes, methods, time, and observations via robust analysis, including visible light, spectral, and thermal imaging. After that, we were followed by the localization of the apple, which aids in detaching apples from branches, leaves, and other overlapping apples, besides directing end-effectors to grip and remove apples. Next, the harvester robots progress includes developments in machinery and equipment that contain grippers, arms, and manipulators, which speed up operations and upgrade performance. Additionally, the platforms that provide aid for harvesting boost productivity, reduce the demand for strength, and lower the danger of accidents at work. Furthermore, the discussion part includes a comprehensive analysis covering works on apple detection systems and automated apple harvesting robot technology. Finally, we summarize the challenges, limitations, opportunities, and future perspectives and provide the trends and technologies. This research offers several avenues for future automated apple harvesting advancement and interaction with other fields that attract investment firms, such as sorting and bagging. Assist in sustaining the expansion of research communities and offering services to raise the yield and quality of apple fruit.
Tianjing et al. (Sun,) studied this question.