This paper presents an end-to-end, IoT-enabled robotic system for the non-destructive, real-time, and spatially-resolved mapping of grape yield and quality (Brix, 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 (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 a $R^2$ (0.76) for weight prediction, while the LISA module improves quality prediction generalization by over 20% compared to 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.

翻译：本文提出了一种端到端、支持物联网的机器人系统，用于在葡萄园中实现葡萄产量与品质（糖度、酸度）的无损、实时、空间分辨测绘。该系统采用一个综合分析流程，集成了两个关键模块：一个用于葡萄串检测与重量估计的高性能模型，以及一个从高光谱数据中进行品质评估的新型深度学习框架。田间高光谱应用的一个关键障碍是光照变化引起的“域偏移”。为克服此问题，我们的品质评估模块由光照不变光谱自编码器驱动，这是一个领域对抗框架，能够从未经校准的数据中学习光照不变特征。我们在一个专门构建的高光谱数据集上验证了系统的鲁棒性，该数据集涵盖三个不同的光照域：受控人工光照（实验室）、以及早晨与下午采集的变化自然日光。结果表明，完整流程在葡萄串检测上实现了0.82的召回率，在重量预测上达到了0.76的$R^2$值，而LISA模块相较于基线方法，将品质预测的泛化能力提升了超过20%。通过结合这些鲁棒模块，该系统成功生成了高分辨率、地理参考的葡萄产量与品质数据，为精准葡萄栽培提供了可操作的、数据驱动的见解。