Grape yield estimation using on-the-fly MIMO millimeter-wave radar at operational field speed: A variety-dependent proof of concept

Abstract

Accurate pre-harvest grape yield estimation is a critical challenge in precision viticulture, yet current optical approaches (RGB cameras, LiDAR) are fundamentally limited by foliage occlusion and sensitivity to ambient lighting conditions. This paper presents a ground-based grape yield estimation system using a dual 77 GHz Multiple-Input Multiple-Output (MIMO) Frequency-Modulated Continuous-Wave (FM-CW) radar mounted on a rover operating at an average speed of 1.0 m/s (3.7 km/h), matching practical vineyard tractor speeds. Unlike prior radar-based studies conducted in static or slow-moving setups, the proposed pipeline achieves continuous 3D reconstruction of vinerows through digital beamforming in the elevation plane, combined with an IMU-fused Kalman filter for point cloud stabilization on irregular terrain. An experimental campaign was conducted on 136 km of scanned vinerows across seven grape varieties and three phenological growth stages (BBCH 75–89) in a commercial vineyard in Gascony, France. Statistical features extracted from 3D radar echo level images are used to train a Support Vector Regressor (SVR) evaluated in cross-validation. The primary out-of-sample result is a cross-validation Mean Absolute Error (CV MAE) of 31.2% on the full multi-variety dataset without any defoliation, achieved against certified wine-vat bulk weights as the ground truth reference. This figure should be contextualized against the ∼ 24–25% discrepancy observed between portable field weighing plates and vat-certified weights across the same dataset, and against manual pre-harvest estimation errors routinely exceeding 30% in viticultural practice. A sensitivity analysis over physical parameters reveals that grape variety is the dominant −and limiting- source of performance heterogeneity: CV MAE ranges from 7% for Tannat to 69% for Baco, demonstrating that a single global model is insufficient for multi-variety deployment and that variety-specific calibration is a necessary condition for operational use. These results are best understood as an architectural proof of concept: the primary contribution is the demonstration that MIMO digital beamforming removes the speed bottleneck of prior mechanically-scanned radar systems, enabling gapless 3D vinerow reconstruction at practical field speeds. The yield estimation results provide a first quantitative characterization of the system’s sensing capability and its variety-dependent limitations and establish a baseline for future variety-specific model development.

The authors' abstract, as published at the source. Computers and Electronics in Agriculture, 2026 · DOI ↗