基于机器视觉与高光谱成像的西瓜考种系统研究

Watermelon Breeding System Based on Machine Vision and Hyperspectral Imaging

摘要:

传统的西瓜考种方式存在效率低、工作量大、主观性强，易导致不同育种工作人员对相同品质性状的瓜评价不统一等问题。为此，提出了一种基于机器视觉与高光谱成像技术的西瓜考种系统。设计了西瓜图像自动采集装置，包含一台GigE视觉相机和一个称量平台来获取西瓜的表型数据，并对西瓜切面选择了10个不同的感兴趣区域（ROI）进行糖度建模，利用高光谱成像技术实现西瓜切面各个位置糖度分布可视化。通过最小外接矩形法对西瓜的横径和纵径做出测量，得到最小外接矩形的长和宽即为西瓜的横径和纵径。应用Canny边缘检测算法实现对瓜皮轮廓的提取，并取瓜皮轮廓的瓜蒂、瓜脐和两个中间位置，这4处瓜皮厚度的平均值作为皮厚真实值。采用卷积平滑（SGS）算法分别结合多元散射校正（MSC）、标准正态变换（SNV）、单位矢量归一化（UVN）3种算法对光谱数据进行预处理，对经过最佳预处理后的光谱数据采用竞争性自适应重加权算法（CARS）、连续投影算法（SPA）和一次组合降维算法（CARS+SPA）进行特征波长筛选，最后用筛选后的光谱数据建立偏最小二乘回归（PLSR）模型并进行分析比较。研究结果表明，该系统对西瓜横径、纵径、瓜皮厚度的测量值与人工测量值相比准确率最高分别为98.68%、98.82%、93.81%，均方根误差分别为2.43、2.08、1.63mm。对西瓜糖度检测效果最好的模型为（SGS+UVN）-CARS-PLSR，其检测相关系数Rc和Rp分别为0.9204和0.9127，均方根误差RMSEC和RMSEP分别为0.3760°Brix和0.4668°Brix，残差预测偏差RPD为2.4900。

Abstract:

The watermelon breed testing system combining machine vision and hyperspectral imaging was developed to enhance efficiency, reduce labor, and improve consistency in quality evaluation. The system included an automatic image acquisition device, featuring a Gige camera and a weighing platform, to capture watermelon phenotypic data. It used hyperspectral imaging on selected regions of interest to model SSC distribution within the watermelon, with measurements of transverse and longitudinal diameters achieved through minimum external rectangle fitting and rind thickness estimated using Canny edge detection. Convolutional smoothing (SGS) algorithm was used to preprocess the spectral data by combining three algorithms, namely multivariate scattering correction (MSC), standard normal transform (SNV), and unit vector normalisation (UVN), respectively, and then the best preprocessed spectral data were filtered by competitive adaptive reweighting algorithm (CARS), successive projection algorithm (SPA), and one-time combined dimensionality reduction algorithm (CARS+SPA) for feature wavelength screening, and finally the screened spectral data were used to build PLSR models and analyzed for comparison. The results showed that the system had the highest accuracy of 98.68%, 98.82% and 93.81% for the measured values of transverse diameter, longitudinal diameter and rind thickness of watermelon, respectively, with the root mean square error of 2.43mm, 2.08mm and 1.63mm, respectively, as compared with the manual measurements. The best model for predicting watermelon brix was (SGS+UVN)-CARS-PLSR, with prediction correlation coefficients and of 0.9204 and 0.9127, root mean square errors RMSEC and RMSEP of 0.3760°Brix and 0.4668°Brix, respectively, and a relative analytical error of prediction RPD of 2.49.