用光照不变的深度学习，实时精准测绘葡萄产量与品质。
In-Field Mapping of Grape Yield and Quality with Illumination-Invariant Deep Learning
- 构建光照不变的光谱自编码器，从非校准数据中提取稳定特征。
- 葡萄串检测召回率达0.82，重量预测决定系数R²达0.76。
- 适合葡萄园精准管理，尤其适用于光照变化大的户外场景。
本文提出一种端到端、物联网支持的机器人系统，实现葡萄园中葡萄产量与品质（糖度、酸度）的无损、实时、空间分辨测绘。系统包含两个核心模块：高性能葡萄串检测与重量估计算法，以及基于高光谱成像（HSI）数据的新颖质量评估深度学习框架。为克服田间高光谱数据因光照变化导致的“域偏移”问题，本研究采用光照不变光谱自编码器（LISA），一种域对抗框架，可从未校准数据中学习光照不变特征。在涵盖三种不同光照环境（实验室人工光源，以及上午和下午自然阳光）的专用HSI数据集上验证，系统整体实现葡萄串检测召回率0.82，重量预测决定系数R²达0.76；相比基线模型，LISA模块使品质预测泛化能力提升超20%。通过融合多个鲁棒模块，系统成功生成高分辨率、地理标记的葡萄产量与品质数据，为精准葡萄栽培提供可操作的数据驱动洞察。
原文摘要 · Abstract (English)
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.
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