Hao Yong, Zhang Chengxiang, Li Xiyan, Lei Zuxiang
School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang, China.
Key Laboratory of Conveyance Equipment of the Ministry of Education, Nanchang, China.
Front Nutr. 2022 Oct 24;9:1026730. doi: 10.3389/fnut.2022.1026730. eCollection 2022.
Insect-affected pests, as an important indicator in inspection and quarantine, must be inspected in the imports and exports of fruits like "Yali" pears (a kind of duck head-shaped pear). Therefore, the insect-affected pests in Yali pears should be previously detected in an online, real-time, and accurate manner during the commercial sorting process, thus improving the import and export trade competitiveness of Yali pears. This paper intends to establish a model of online and real-time discrimination for recessive insect-affected pests in Yali pears during commercial sorting. The visible-near-infrared (Vis-NIR) spectra of Yali samples were pretreated to reduce noise interference and improve the spectral signal-to-noise ratio (SNR). The Competitive Adaptive Reweighted Sampling (CARS) method was adopted for the selection of feature modeling variables, while Partial Least Squares Discriminant Analysis (PLS-DA), Support Vector Machine (SVM), and Convolutional Block Attention Module-Convolutional Neural Networks (CBAM-CNN) were used to establish online discriminant models. T-distributed Stochastic Neighbor Embedding (T-SNE) and Gradient-weighted Class Activation Mapping (Grad-CAM) were used for the clustering and attention distribution display of spectral features of deep learning models. The results show that the online discriminant model obtained by SGS pretreatment combined with the CBAM-CNN deep learning method exhibits the best performance, with 96.88 and 92.71% accuracy on the calibration set and validation set, respectively. The prediction time of a single pear is 0.032 s, which meets the online sorting requirements.
受虫害的果实作为检验检疫的重要指标，在“鸭梨”（一种鸭头形状的梨）等水果进出口时必须进行检验。因此，在商业分拣过程中，应预先以在线、实时、准确的方式检测鸭梨中的受虫害果实，从而提高鸭梨的进出口贸易竞争力。本文旨在建立鸭梨在商业分拣过程中隐性受虫害果实的在线实时判别模型。对鸭梨样本的可见-近红外（Vis-NIR）光谱进行预处理，以减少噪声干扰，提高光谱信噪比（SNR）。采用竞争性自适应重加权采样（CARS）方法选择特征建模变量，同时使用偏最小二乘判别分析（PLS-DA）、支持向量机（SVM）和卷积块注意力模块-卷积神经网络（CBAM-CNN）建立在线判别模型。使用T分布随机邻域嵌入（T-SNE）和梯度加权类激活映射（Grad-CAM）对深度学习模型的光谱特征进行聚类和注意力分布显示。结果表明，采用SGS预处理结合CBAM-CNN深度学习方法得到的在线判别模型性能最佳，在校准集和验证集上的准确率分别为96.88%和92.71%。单个梨的预测时间为0.032 s，满足在线分拣要求。
