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Abstract The United States (US) apple industry relies heavily on semi‐skilled manual labor force for essential field operations such as training, pruning, blossom and green fruitlet thinning, and harvesting. Blossom thinning is one of the crucial crop‐load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical and mechanical thinning are available for large‐scale blossom thinning, such approaches often yield unpredictable thinning results and may damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor‐intensive, and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for precision blossom thinning in apple orchards using a deep learning‐based computer vision system, a six‐degrees‐of‐freedom UR5e robotic manipulator, and an electrically actuated miniature end‐effector. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system's ability to remove varying proportions of flowers from apple flower clusters. During boundary thinning, the end‐effector was actuated around the cluster boundary, while center thinning involved end‐effector actuation only at the cluster centroid for a fixed duration of 2 s. Field evaluation results showed that the boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 s per cluster, whereas the center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 s per cluster. Upon further improvement for commercial adoption, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.
Bhattarai et al. (Thu,) studied this question.