Online real-time gender identification method for silkworm chrysalises based on Fast-SCNN-mp model

Breeding silkworms is one of the most important practices in silk production. The high efficiency and quality of the breeding can be required for the high accuracy of the gender identification on the silkworm pupae. Currently, gender identification can rely mainly on the manual observation of the gonad characteristics at the tail of pupae. It can take one week after pupation. However, manual identification cannot fully meet the requirements of the large-scale industry due to the labor intensity and cost. Machine vision can be expected in the field of silkworm pupae identification, due to the low cost, easy integration, and adaptability to online detection. All existing models have been constructed using ideal silkworm pupa images with the “intact gonads”. It is often required to consider the gonad feature defects that are caused by practical working conditions, such as the pupa placement angle deviation and pupa curling in online detection. In this study, an improved lightweight real-time semantic segmentation model, named Fast-SCNN-mp, was proposed using the basic Fast-SCNN model. The performance was improved after multi-dimensional optimization. A multi-scale convolutional attention module was also introduced to detect the gonadal regions during feature extraction; Cascaded depthwise separable convolutions and bottleneck residual module were adopted to realize the efficient feature compression and enhancement; A pyramid pooling module was integrated into the feature aggregation to fuse the multi-scale contextual information, particularly for the feature representation. A series of experiments was conducted on a gonad-defective dataset over the full tilt angle range of 0~18°, >18°~45°, >45°~72°, and >72°~90°, including 875 images of 5 silkworm pupae varieties. The results showed that the precision, recall, F1-score, and accuracy of the Fast-SCNN-mp model reached 98.57%, 98.65%, 98.61%, and 98.61%, respectively, which were 2.79, 2.73, 2.76, and 2.79 percentage points higher than those of the basic Fast-SCNN model. Furthermore, the 2 conventional classifications and 5 mainstream semantic segmentation were utilized to further verify the model. On the dataset with a roll angle >72°~90°, the Fast-SCNN-mp model achieved an accuracy of 96.30%, which was comparable to that of Mask2Former, the state-of-the-art mainstream semantic segmentation, whereas the accuracy of the optimal conventional classification, convolutional compact Transformer (CCT), only reached 81.48%. In terms of the model parameters and inference speed (FPS), the Fast-SCNN-mp model shared only 2.17 M parameters, which was the lowest among all models. Meanwhile, an inference speed of 68.10 FPS also outperformed all the rest, indicating a 32.55-fold increase, compared with the top-performing model Mask2Former. In conclusion, the Fast-SCNN-mp model effectively balanced the trade-off between performance and real-time requirements, indicating the high accuracy of identification, the light weight, and high inference efficiency. The findings can provide an efficient and reliable technical solution for the online intelligent identification of the silkworm pupae. A valuable reference can also offer model optimization and application in the real-time classification tasks in modern agriculture.