Generative Monocular Perception Pipeline-Based Framework for Accurate Stem Detection in Automated Strawberry Harvest

Abstract: Horticulture faces a growing labor crisis, driving demand for autonomous harvesting robots, but reliable strawberry peduncle detection remains a critical unsolved challenge due to their fine, millimeter-scale structure and severe intertwining with leaves and stems. Existing single-view RGB imaging struggles with occlusions and ambiguities, while depth sensors falter in reflective greenhouse environments plagued by noise and data gaps. Introducing a generative monocular perception pipeline—the first to reconstruct multi-view cues purely from a single RGB image—this study achieves perceptual consistency through four novel, synergistic innovations: (i) pseudo multi-view synthesis to emulate diverse viewpoints, (ii) monocular depth estimation for precise geometric guidance and background isolation, (iii) line-curve geometric modeling to capture subtle peduncle features, and (iv) occlusion-order reasoning via cross-view consistency analysis. In comparative trials against a YOLO-based detector (85.71% region accuracy vs. 57.14%), our pipeline delivers orientation precision, slashing mean angular error from 18.31° to 13.96°—robust, clutter-resilient cutting cues for next-generation robotic harvesters. Evaluated on farm images, it reduces mean angular error to 13.96° (SD 10.15°) from YOLO's 18.31° (SD 11.27°), with p<0.05 (paired t-test, n=14).

Kohei Arai, Jin Sawada and Mariko Oda. “Generative Monocular Perception Pipeline-Based Framework for Accurate Stem Detection in Automated Strawberry Harvest”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.4 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170414

@article{Arai2026,
title = {Generative Monocular Perception Pipeline-Based Framework for Accurate Stem Detection in Automated Strawberry Harvest},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170414},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170414},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {4},
author = {Kohei Arai and Jin Sawada and Mariko Oda}
}