Hu Yating, Ma Benxue, Wang Huting, Zhang Yuanjia, Li Yujie, Yu Guowei
College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, China.
Key Laboratory of Northwest Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Shihezi, China.
Front Plant Sci. 2023 May 8;14:1105601. doi: 10.3389/fpls.2023.1105601. eCollection 2023.
Efficient, rapid, and non-destructive detection of pesticide residues in fruits and vegetables is essential for food safety. The visible/near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral imaging (HSI) systems were used to detect different types of pesticide residues on the surface of Hami melon. Taking four pesticides commonly used in Hami melon as the object, the effectiveness of single-band spectral range and information fusion in the classification of different pesticides was compared. The results showed that the classification effect of pesticide residues was better by using the spectral range after information fusion. Then, a custom multi-branch one-dimensional convolutional neural network (1D-CNN) model with the attention mechanism was proposed and compared with the traditional machine learning classification model K-nearest neighbor (KNN) algorithm and random forest (RF). The traditional machine learning classification model accuracy of both models was over 80.00%. However, the classification results using the proposed 1D-CNN were more satisfactory. After the full spectrum data was fused, it was input into the 1D-CNN model, and its accuracy, precision, recall, and F1-score value were 94.00%, 94.06%, 94.00%, and 0.9396, respectively. This study showed that both VNIR and SWIR hyperspectral imaging combined with a classification model could non-destructively detect different pesticide residues on the surface of Hami melon. The classification result using the SWIR spectrum was better than that using the VNIR spectrum, and the classification result using the information fusion spectrum was better than that using SWIR. This study can provide a valuable reference for the non-destructive detection of pesticide residues on the surface of other large, thick-skinned fruits.
高效、快速且无损地检测水果和蔬菜中的农药残留对于食品安全至关重要。可见/近红外（VNIR）和短波红外（SWIR）高光谱成像（HSI）系统被用于检测哈密瓜表面不同类型的农药残留。以哈密瓜中常用的四种农药为对象，比较了单波段光谱范围和信息融合在不同农药分类中的有效性。结果表明，信息融合后的光谱范围用于农药残留分类效果更好。然后，提出了一种带有注意力机制的定制多分支一维卷积神经网络（1D-CNN）模型，并与传统机器学习分类模型K近邻（KNN）算法和随机森林（RF）进行比较。两种传统机器学习分类模型的准确率均超过80.00%。然而，使用所提出的1D-CNN得到的分类结果更令人满意。全光谱数据融合后输入1D-CNN模型，其准确率、精确率、召回率和F1分数值分别为94.00%、94.06%、94.00%和0.9396。本研究表明，VNIR和SWIR高光谱成像与分类模型相结合能够无损检测哈密瓜表面不同的农药残留。使用SWIR光谱的分类结果优于使用VNIR光谱的结果，使用信息融合光谱的分类结果优于使用SWIR光谱的结果。本研究可为其他大型厚皮水果表面农药残留的无损检测提供有价值的参考。
