Abstract
This review provides an overview of apple detection and localization using image analysis and artificial intelligence techniques for enabling robotic fruit harvesting in orchard environments. Classic methods for detecting and localizing infield apples are discussed along with more advanced approaches using deep learning algorithms that have emerged in the past few years. Challenges faced in apple detection and localization such as occlusions, varying illumination conditions, and clustered apples are highlighted, as well as the impact of environmental factors such as light changes on the performance of these algorithms. Potential future research perspectives are identified through a comprehensive literature analysis. These include combining cutting-edge deep learning and multi-vision and multi-modal sensors to potentially apply them in real-time for apple harvesting robots. Additionally, utilizing 3D vision for a thorough analysis of complex and dynamic orchard environments, and precise determination of fruit locations using point cloud data and depth information are presented. The outcome of this review paper will assist researchers and engineers in the development of advanced detection and localization mechanisms for infield apples. The anticipated result is the facilitation of progress toward commercial apple harvest robots.
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