Model updating strategy study about sex identification of silkworm pupae using transfer learning and NIR spectroscopy

Abstract

This paper proposes a novel model updating strategy named SilkwormNet for the first time to address the sex discrimination problem of silkworm pupae with new species. SilkwormNet integrates a ResNet block, a multi-head attention mechanism, and a Schedule-Free optimization strategy. Initially, the preprocessed spectra from one species were input into SilkwormNet to establish an optimal primary model. Then, the feature extraction layers and classification head remained unfrozen and the optimal weight parameters from the basic model were applied for model updating to identify the new species. Finally, SilkwormNet used only 20 % data to update model. Uniform Manifold Approximation and Projection (UMAP) and Confusion Matrix were employed to comprehensively evaluate the results. When the basic model was built using variety 221B_403, the accuracy was highly improved after model updating, for example, for variety 871B_463 increased from 50  % to 99.22 %, for variety 9312_ShanheB increased from 74.22 % to 99.22 %; for variety FB_P71 increased from 69.53 % to 98.44 %; and for variety 7532_906 increased from 50 % to 100 %. When using just 10 % data to update the model, the range of accuracy was between 90.62 % and 95.31 %. The results of SilkwormNet were also compared with SVM, Random Forest, and 1D-CNN to further demonstrate its superiority.