看见“葡萄成熟时“：高光谱的田间检测案例

When Grapes Ripen: Case Studies of Hyperspectral Field Detection

在葡萄种植与葡萄酒酿造领域，准确监测葡萄成熟度和糖度是决定采收时机和最终酒品质量的关键。高光谱成像技术已被证明能够有效检测葡萄的成熟度、糖度(°Brix)和花青素含量，作为高光谱成像技术的专业提供商，我们期待看到这项技术在葡萄品质监测中的创新应用。

目前大多数研究都局限于实验室环境或对单颗葡萄进行检测。今天，我们将聚焦两项突破性研究，展示高光谱技术如何在田间现场实现葡萄品质的非破坏性监测。

In viticulture and winemaking, accurately monitoring grape maturity and sugar content is crucial for determining optimal harvest timing and final wine quality. Hyperspectral imaging technology has proven effective in detecting grape maturity, sugar content (°Brix), and anthocyanin levels.As a professional provider of hyperspectral imaging solutions, we are excited to see innovative applications of this technology in grape quality monitoring.

Currently, most research has been limited to laboratory settings or single-berry measurements. Today, we highlight two groundbreaking studies demonstrating how hyperspectral technology enables non-destructive quality monitoring directly in the field.

『从实验室走向田间 / From Lab to Vineyard 』

意大利团队开发了一种基于可见光-近红外(400-1000nm)高光谱相机的非破坏性方法，直接在葡萄园中进行13填的连续监测。他们使用偏最小二乘回归(PLS)预测可溶性固形物含量，获得R²=0.77的预测精度(RMSECV=0.79°Brix)，并通过偏最小二乘判别分析(PLS-DA)将葡萄按成熟度(以20°Brix为界)分类，准确率达86-91%。

An Italian research team developed a non-destructive method using visible-to-near-infrared (400-1000nm) hyperspectral cameras for continuous 13-day monitoring in vineyards. Using partial least squares regression (PLS), they achieved soluble solids content (SSC) predictions with R²=0.77 (RMSECV=0.79°Brix). Through partial least squares discriminant analysis (PLS-DA), they classified grape maturity (using 20°Brix as the threshold) with 86-91% accuracy.

图1 / Figure 1：

(a) RGB图像来源于葡萄园行扫描截面的高光谱数据。 (b) 基于曼哈顿函数分类生成的ROI（红色区域）。

(a) The RGB image is derived from hyperspectral data captured during a vineyard row scan transect.

(b) The region of interest (ROI, marked in red) was generated through classification using the Manhattan distance function.

『边走边测：硬件与算法的结合 / “On the Go" Monitoring: Hardware and Algorithm Synergy 』

西班牙团队的研究则更进一步，他们开发了一套创新的"行进间"高光谱成像系统，将可见光-近红外高光谱相机（400-1000nm、光谱分辨率2.1nm的）安装在以5公里/小时速度移动的全地形车上。相机在车辆行进过程中连续采集数据。

为适应不同时段的光照变化，系统会智能调整帧率（40-50帧/秒），每个测量区块平均获取710条扫描线，总计约63.9万个光谱像素点，。系统还配备RTK校正功能，为所有采集数据提供厘米级的地理参考。

A Spanish team advanced the technology further by developing an innovative "on-the-go" hyperspectral imaging system. They mounted a visible-to-near-infrared hyperspectral camera (400-1000nm, 2.1nm spectral resolution) on an all-terrain vehicle moving at 5 km/h, enabling continuous data acquisition during operation.

To adapt to varying light conditions, the system automatically adjusts frame rates (40-50 fps). Each measurement block captures approximately 710 scan lines, totaling ~639,000 spectral pixels. The system also integrates RTK correction for centimeter-level georeferencing of collected data.

图2 / Figure 2：

使用安装在全地形车(ATV)上的摄像头以 5 公里/小时的速度进行移动高光谱成像。通过推扫式扫描，从ATV的运动中获得整个葡萄树冠层的图像，并用于估计葡萄成分。

On-the-go hyperspectral imaging with a camera mounted on an all-terrain vehicle (ATV) at 5 km/h. Images of the entire vine canopy were obtained from the ATV's motion, by push-broom scanning, and used for the estimation of grape composition.

数据处理上采用了支持向量机(SVM)算法，通过五折交叉验证，对糖度的预测达到R²=0.91(RMSE=1.358°Brix)，对外部样本的预测R²=0.92(RMSE=1.274°Brix)。对花青素浓度的预测也取得了R²=0.72(交叉验证)和R²=0.83(外部预测)的良好结果。实现了对葡萄的可溶性固形物和花青素浓度的实时监测。

计算性能方面，在Intel Core i7处理器(16GB内存)上，处理36幅高光谱图像平均需要5小时35分钟，相当于每列扫描线处理时间约0.79秒。而使用训练好的SVM模型预测单个样本仅需0.05秒，展现了良好的实用性能。

For data processing, the team employed support vector machine (SVM) algorithms. Through five-fold cross-validation, sugar content predictions reached R²=0.91 (RMSE=1.358°Brix), with external validation achieving R²=0.92 (RMSE=1.274°Brix). Anthocyanin concentration predictions also showed strong results: R²=0.72 (cross-validation) and R²=0.83 (external validation), enabling real-time monitoring of soluble solids and anthocyanins.

In terms of computational performance, processing 36 hyperspectral images on an Intel Core i7 processor (16GB RAM) averaged 5 hours and 35 minutes (~0.79 seconds per scan line). Trained SVM models required only 0.05 seconds per sample prediction, demonstrating practical usability.

图3 / Figure 3：

(a)基于RGB通道的高光谱图像（为便于说明，对直方图进行了归一化）。 (b)像素光谱与葡萄标准光谱的R²值相关矩阵（应用σ=1.0高斯平滑核函数）。 (c)基于R²≥0.75阈值的葡萄像素分割结果图。

(a) Hyperspectral image from a block in red, green and blue (RGB) channels (histogram normalised for the sake of illustration). (b) Correlation matrix with R² values between the pixel spectrum and a grape reference spectrum. A Gaussian smoothing was applied with σ = 1.0. (c) Image with segmented grape pixels (pixels in (b) whose R² ≥ 0.75). All the images were stretched in the horizontal axis for aesthetic purposes.

图 4/ Figure 4：

可溶性固体物模型的交叉验证和预测结果.

Regression plot for (a) fivefold cross validation (R² = 0.91; RMSE = 1.358) and (b) prediction results (R² = 0.92; RMSE = 1.274) for the TSS models.

图5 / Figure 5：

浆果花青素浓度的交叉验证和预测模型的回归图.

Regression plot for (a) fivefold cross validation (R² = 0.72; RMSE = 0.282) and (b) prediction results (R² = 0.83; RMSE = 0.211) for the anthocyanin concentration models.

『挑战与未来展望 / Challenges and Future Outlook 』

尽管仍需解决环境干扰、数据处理速度等问题，这两项研究证实了高光谱技术在田间应用的可行性。

我们期待与更多农业机构合作，提供专业的高光谱硬件解决方案。我们的设备能够为客户的研发团队提供高质量的原始数据，兼容多种数据格式。随着技术优化，这项技术有望成为智慧葡萄园的标准配置，为葡萄和葡萄酒产业带来精细化管理的新时代。

While challenges like environmental interference and processing speed remain, these studies confirm hyperspectral technology's field applicability.

We look forward to collaborating with agricultural institutions to provide professional hyperspectral hardware solutions. Our equipment delivers high-quality raw data in multiple compatible formats for research teams. As the technology evolves, it is poised to become a standard tool for smart vineyards, ushering in a new era of precision management for grape and wine production.

案例来源 / Source：

1. Benelli, A., Cevoli, C., Ragni, L., & Fabbri, A. (2022). Reprint of: In-field and non-destructive monitoring of grapes maturity by hyperspectral imaging. Biosystems Engineering, 223(Part B), 200-208.

2. Gutiérrez, S., Tardaguila, J., Fernández-Novales, J., & Diago, M. P. (2018). On-the-go hyperspectral imaging for the in-field estimation of grape berry soluble solids and anthocyanin concentration. Australian Journal of Grape and Wine Research, 24(2), 127-133.