Current status and prospects of the visual detection and positioning technology for intelligent picking of famous tea

The mechanization of famous tea harvesting is an essential way to develop China’s tea industry. This paper centers on the detection and positioning technologies in famous tea harvesting, systematically reviewing research progress in these domains. In tea detection, traditional methods rely on color space selection and image segmentation, exhibiting limitations such as insufficient accuracy and poor generalization capability. Conversely, deep learning algorithms demonstrate superior detection accuracy and robustness. Current research focuses on enhancing detection accuracy, inference speed, and multi-variety recognition. In picking positioning, depth information measurement technology utilizing RGB-D cameras provides foundational support. Positioning methods have evolved from traditional visual processing techniques to deep learning and point cloud approaches, seeking to overcome challenges including occlusion and irregular growth patterns. Notwithstanding notable technological advancements, existing methods confront three primary limitations: difficulties in adapting to diverse growth stage characteristics, reliance on large-scale annotated datasets, and inadequate occlusion handling. Future research ought to concentrate on three directions: developing highly universal tea bud detection models, refining model training techniques for small-sample scenarios, and improving tea-picking point positioning accuracy under occluded conditions. This review aims to furnish critical references for advancing high-end intelligent tea-picking machinery, thereby facilitating the tea industry’s mechanization and intelligentization.