A Band Influence Algorithm for Hyperspectral Band Selection to Classify Moldy Peanuts

Moldy peanuts are often found in harvested and stored peanuts. Aflatoxins in moldy peanuts pose a potential risk to food safety. Hyperspectral imaging techniques is often used for rapid nondestructive testing of food. However, the information redundancy of hyperspectral data has a negative effect on the processing speed and classification accuracy. In this study, a novel band selection method, namely the band influence algorithm (BIA), was proposed to extract key features and remove redundancy for the classification of moldy peanuts. Firstly, hyperspectral images of moldy, healthy and damaged peanuts were collected with 128 bands ranging from 400 nm to 1000nm. Secondly, the BIA method was used to extract feature bands according to the influence of each band subset on the accuracy of the classification model. The effectiveness of BIA method was compared with five representative band selection methods on four classification models: decision tree (DT), k-nearest neighbor (KNN), support vector machine (SVM), and ShuffleNet V2. The experimental results show that the BIA method performs superior effect and stability than other methods on all classification models. The integration of BIA and ShuffleNet V2 achieved the best classification effect. Especially when using 10 feature bands on ShuffleNet V2, the average accuracy, F1 score, and kappa coefficient of BIA reached 97.66%, 0.977, and 0.963 respectively.