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This is a Quantitative Study study.

Data-Driven Trajectory Optimization in Robotic Fruit Harvesting via Deep Learning-Based Perception, Gripper Configuration, and Fruit Morphometrics

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This approach integrates deep learning-based perception and fruit morphometrics to improve harvesting efficiency.

Integrating CNN confidence significantly reduced trajectory length by 14% for oranges and 11% for strawberries.
The approach utilized two multivariate regression models for firm and soft fruits, showing high predictive performance.
Orientation greatly affected trajectory optimization, impacting soft fruits more severely due to their fragility.
The study highlighted the importance of combining perception and motion planning for enhanced efficiency in robotic harvesting.

Zeeshan et al. (2025) studied this question.

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