Light-resilient visual regression of strawberry ripeness for robotic harvesting

Abstract

Accurate real-time quantification of strawberry ripeness is critical for advancing selective strawberry harvesting robots. However, existing methods often overlook inconsistencies caused by color distortion under varying light intensities. This study aims to verify and quantitatively analyze the influence of illumination on strawberry ripeness, and further presents a novel light-resilient vision-based ripeness regression method that overcomes these limitations through three key innovations. First, the research established the first fine-grained ripeness metric under controlled lighting conditions and introduced the comprehensive LightStrawberry dataset, featuring multi-illumination strawberry images. Second, we propose SRR-Net, an innovative end-to-end strawberry ripeness regression network built upon YOLOv8/YOLOv11. The network incorporates a dedicated ripeness regression branch that operates in parallel with the detection and segmentation heads, enabling simultaneous and efficient estimation of strawberry maturity. To further mitigate lighting-induced color distortion, RetinexNet was integrated to decompose, adjust, and reconstruct images by normalizing illumination and reflectance. Experiments demonstrated that SRR-Net achieved 0.918 mAP@50 for segmentation and operated at 210.3 FPS based on YOLOv11, while SRR-Net with RetinexNet attained 0.898 mAP@50 and 43.39 FPS. Though slightly lower in precision than other methods, both significantly improved ripeness accuracy, with mean absolute errors (MAE) of 0.040 and 0.037, representing 68.75 % and 71.09 % improvements over conventional Mask R-CNN approaches. Field experiments further demonstrated that SRR-Net and SRR-Net with RetinexNet achieved superior performance in ripeness regression. However, their detection and segmentation performance showed limited adaptability to real orchard conditions due to the characteristics of the LightStrawberry dataset. Overall, both models outperformed the standard YOLOv8/v11 baselines but were slightly inferior to Mask R-CNN. This work provides a robust solution for strawberry-harvesting robotics, enabling reliable ripeness assessment in challenging field environments.

Plant phenotyping relevance

画像からイチゴの成熟度を定量推定する回帰手法、照明補正、データセットを開発・評価しており、植物形質取得が中心である。

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