Online Estimation of Table-Top Grown Strawberry Mass in Field Conditions with Occlusions

• URL: http://arxiv.org/abs/2507.23487v1
• Date: Thu, 31 Jul 2025 12:10:23 GMT
• Title: Online Estimation of Table-Top Grown Strawberry Mass in Field Conditions with Occlusions
• Authors: Jinshan Zhen, Yuanyue Ge, Tianxiao Zhu, Hui Zhao, Ya Xiong,
• Abstract summary: This study proposes a vision-based pipeline integrating RGB-D sensing and deep learning to enable non-destructive, real-time and online mass estimation.<n>Experiments demonstrated mean mass estimation errors of 8.11% for isolated strawberries and 10.47% for occluded cases.
• Score: 2.736203444988168
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate mass estimation of table-top grown strawberries under field conditions remains challenging due to frequent occlusions and pose variations. This study proposes a vision-based pipeline integrating RGB-D sensing and deep learning to enable non-destructive, real-time and online mass estimation. The method employed YOLOv8-Seg for instance segmentation, Cycle-consistent generative adversarial network (CycleGAN) for occluded region completion, and tilt-angle correction to refine frontal projection area calculations. A polynomial regression model then mapped the geometric features to mass. Experiments demonstrated mean mass estimation errors of 8.11% for isolated strawberries and 10.47% for occluded cases. CycleGAN outperformed large mask inpainting (LaMa) model in occlusion recovery, achieving superior pixel area ratios (PAR) (mean: 0.978 vs. 1.112) and higher intersection over union (IoU) scores (92.3% vs. 47.7% in the [0.9-1] range). This approach addresses critical limitations of traditional methods, offering a robust solution for automated harvesting and yield monitoring with complex occlusion patterns.

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