This study reviews the advancement of apple-related technology in six distinct domains: pruning, pollinating, thinning, bagging, harvesting, and sorting. Firstly, the pruning section discusses machine vision systems and moves on to mechanical branch-cutting methods, followed by automated apple pruning. Secondly, concerning pollination, we primarily focus on the growth of apple fruits, beginning with identifying blossoms, using artificial pollen, and mechanized pollination. Thirdly, in the thinning section, a review of achievements of the vision systems and mechanical and robotic thinning technologies. Fourthly, in apple bagging, we address the effects of apple bagging strategies, including the primary procedures, varieties of bags, and influencing aspects. Next, in the harvesting part, the view focuses on the vision strategies and recognition systems that include visible light,
spectral, and thermal imaging by means of thorough analysis that considers the time, observations, techniques, and findings. After that, the localization of the apple assists in separating apples such as leaves, branches, and other overlapping apples, in addition to instructing end-effectors on how to grasp and extract apples. Furthermore, harvester robots’ progress includes developments in machinery, grippers, arms, and manipulators, besides the assistance platforms for harvesting. Developing the machinery for sorting apples is the final area of focus. The
machinery includes conveyors, sorting executors, and bin fillers. Vision systems imaging types and methods are followed by illumination systems, and identification strategies such as machine and deep learning algorithms are considered. In limitations and future views, we summarize the challenges of the six distinct domains and provide opportunities and future perspectives. By confirming the economic advantages and sustaining the expansion of academic organizations for technology development and industry, the study assists in boosting apple fruit quality and production.