A Novel Model for Sex Discrimination of Silkworm Pupae From Different Species

Abstract

Sex determination of silkworm pupae is important for silkworm industry. Multivariate analysis methods have been widely applied in hyperspectral imaging spectroscopy for classification. However, these methods require essential steps containing spectra preprocessing or feature extraction, which were not easy determined. Convolutional neural networks (CNNs), which have been employed in image recognition, could effectively learn interpretable presentations of the sample without the need of ad-hoc preprocessing steps. The species of silkworm pupae are usually up to hundreds. Conventional classifiers based on one species of silkworm pupae could not give high performance when explored to other species that not participating in the model building, resulting in bad generalization ability. In this study, a CNN model was trained to automatically identify the sex of silkworm pupae from different years and species based on the hyperspectral spectra. The results were compared with the frequently used conventional machine classifiers including support vector machine (SVM) and K nearest neighbors (KNN). The results showed that CNN outperformed SVM and KNN in terms of accuracy when applied to the raw spectra with 98.03%. However, the performance of CNN decreased to 95.09% when combined with the preprocessed data. Then principal component analysis (PCA) was adopted to reduce data dimensionality and extract features. CNN gave higher accuracy than SVM and KNN based on PCA. The discussion section revealed that CNN had high generalization ability that could classify silkworm pupae from different species with a rather well performance. It demonstrated that HSI technology in combination with CNN was useful in determining the sex of silkworm pupae.