Online Estimation of Table-Top Grown Strawberry Mass in Field Conditions with Occlusions
This addresses the challenge of automated harvesting and yield monitoring for strawberries in agriculture, though it is incremental as it builds on existing vision and deep learning techniques.
This study tackled the problem of accurately estimating the mass of table-top grown strawberries in field conditions with occlusions, achieving mean mass estimation errors of 8.11% for isolated strawberries and 10.47% for occluded cases.
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.