In-Field Mapping of Grape Yield and Quality With Illumination-Invariant Deep Learning

Abstract

This article presents an end-to-end, IoT-enabled robotic system for the nondestructive, real-time, and spatially resolved mapping of grape yield and quality (brix and acidity) in vineyards. The system features a comprehensive analytical pipeline that integrates two key modules: a high-performance model for grape bunch detection and weight estimation, and a novel deep learning framework for quality assessment from hyperspectral imaging (HSI) data. A critical barrier to in-field HSI is the “domain shift” caused by variable illumination. To overcome this, our quality assessment is powered by the light-invariant spectral autoencoder (LISA), a domain-adversarial framework that learns illumination-invariant features from uncalibrated data. We validated the system’s robustness on a purpose-built HSI dataset spanning three distinct illumination domains: controlled artificial lighting (lab), and variable natural sunlight captured in the morning and afternoon. Results show the complete pipeline achieves a recall (0.82) for bunch detection and an R2 (0.76) for weight prediction, while the LISA module improves quality prediction generalization by over 20% compared with the baselines. By combining these robust modules, the system successfully generates high-resolution, georeferenced data of both grape yield and quality, providing actionable, data-driven insights for precision viticulture (PV).