Strawberry production is economically important but remains highly dependent on labour-intensive harvesting. The delicate texture, irregular distribution, and non-uniform maturity of strawberry fruit create substantial challenges for mechanised and robotic operations. This review examines the development of strawberry-harvesting technologies from the broader perspectives of crop value, cultivation management, harvesting methods, robotic systems, post-harvest handling, and sustainable production. The nutritional and economic significance of strawberries is first outlined, followed by an analysis of cultivation environments, production patterns, and crop-management practices that influence fruit accessibility and robotic operation. The historical transition from manual harvesting to mechanised and intelligent harvesting is then reviewed. Particular attention is given to the principal technologies of strawberry-harvesting robots, including mobile platforms, robotic manipulators, path planning and obstacle avoidance, end-effectors, visual recognition, multispectral sensing, and software control. Robotic systems designed for elevated and ridge-based cultivation are also compared to clarify the influence of cultivation layout on platform configuration and harvesting strategy. In addition, the integration of harvesting with fruit transfer, post-harvest handling, and sustainable cultivation is discussed. The reviewed studies indicate that effective robotic harvesting depends on the coordinated design of cultivation systems, perception, motion planning, compliant manipulation, and system control. Future research should prioritise robust perception under occlusion, low-damage harvesting, improved operational speed, scenario adaptability, cost reduction, and closer integration between agronomic practices and robotic design.
This review constructs an explicit conceptual framework integrating cross-scale defense mechanisms—mechanistically linking molecular signal transduction and post-transcriptional regulation to cellular homeostasis and field-scale yield stability—and spotlight the emerging integration of machine learning-assisted breeding and genomic prediction for the efficient evaluation of superior germplasms.
Gan Liu, Shaohua Li, Qi He et al.· Water· 0 citations