Curvature sensing strategy for flexible gripper fingers during grasping deformation: enabling in-orchard online apple size grading

Abstract

In-orchard apple size grading remains challenging under occlusions, variable illumination, and irregular fruit morphology. We present a contact–curvature strategy that integrates bending sensors into fin–ray flexible fingers to estimate fruit size during grasping, unifying grasp–and–grade operations. Neural Radiance Fields (NeRF) reconstruction provides offline ground-truth curvature for calibration, yielding strong linear agreement between sensor readings and true curvature (R2=0.9852). Using temporal curvature features across approach, grasp, and steady phases, a gradient-boosting regression model predicts fruit diameter with R2=0.9577 and RMSE = 1.19 mm on the test set. In laboratory conditions, the system achieved an overall grading accuracy of 98.0 % for 200 apples classified into four grades, with a processing capacity of approximately 6 apples·min⁻¹, meeting real-time requirements. In a small-scale orchard pilot study, the system maintainedR2=0.94 andRMSE=1.27 mm, achieving 96 % grading accuracy versus 77 % for a camera-only approach. Compared with vision-only sizing methods, contact-curvature sensing demonstrates inherent robustness to occlusion and illumination while better tolerating morphological irregularities. A methylene–blue protocol confirmed non–destructive operation. Contact–curvature sensing is robust to occlusions/illumination and can, in principle, extend to other near–spherical crops.

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