Monocular Estimation of Grape Berry Size (Caliber) Distributions Using Geometry-Aware Representations and Structured Prediction

Grape caliber distributions are critical for packing, grading, yield estimation, and post-harvest logistics. However, estimating reliable caliber histograms from single images remains challenging due to occlusion and dense bunch structure. This work presents a two-stage monocular pipeline that integrates instance segmentation, geometry-aware representations, residual quantity correction, and structured histogram prediction. In the first stage, a YOLO-based model detects grape instances and a calibration object, enabling the construction of geometry-aware auxiliary channels and a segmentation-derived counting prior. In the second stage, these representations are used to estimate total grape count and caliber distributions. Results show that RGBDT consistently outperforms RGB, indicating that geometry-aware cues improve both histogram fidelity and counting accuracy. The framework achieves stable performance under realistic conditions while maintaining low runtime, supporting practical deployment in agricultural environments.