Abstract

Portable hyperspectral imaging was used for field and indoor spectra acquisition of the strawberries at three ripeness stages: ripe, mid-ripe and unripe. The mean spectra were pre-processed by multiplicative scatter correction (MSC). Principal component analysis (PCA) was employed to generate score scatter plots and visualize score images for differentiating specific grouping of samples. Three methods, including X-loading weight, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were applied to extract the effective wavelengths. Two classifiers, partial least squares – discriminant analysis (PLS-DA) and least squares – support vector machine (LS-SVM) were used for ripeness assessment. The results showed that the overall accuracy of all classifiers for field assessment ranged from 91.7% to 96.7%, slightly lower than for indoor assessment. Furthermore, the LS-SVM model combined with effective wavelengths with the CARS method performed better for field assessment of strawberry ripeness, providing an accuracy of 96.7%. It can be concluded that hyperspectral imaging can be used for real-time assessment of strawberry ripeness in the field.

摘要

便携式高光谱成像技术用于在三个成熟阶段对草莓进行田间和室内光谱采集：成熟，中熟和未熟。平均光谱通过乘积散射校正（MSC）进行了预处理。主成分分析（PCA）用于生成分数散点图并可视化分数图像，以区分特定的样本分组。运用X负载权重，竞争自适应加权采样（CARS）和连续投影算法（SPA）这三种方法来提取有效波长。使用两个分类器，偏最小二乘-判别分析（PLS-DA）和最小二乘-支持向量机（LS-SVM）进行成熟度评估。结果表明，用于现场评估的所有分类器的总体准确性在91.7％至96.7％之间，略低于室内评估。此外，结合有效波长的LS-SVM模型和CARS方法在野外草莓成熟度评估中表现更好，准确度达96.7％。可以得出结论，高光谱成像可用于田间草莓成熟度的实时评估。