Hybrid pairing of the corresponding silkworm species is a pivotal link in sericulture, ensuring egg quality and directly influencing silk quantity and quality. Considering the potential of image recognition and the impact of varying pupal postures, this study used machine learning and deep learning for global modeling to identify pupae species and sex separately or simultaneously. The performance of traditional feature-based approaches, deep learning feature-based approaches, and their fusion approaches were compared. First, 3600 images of the back, abdomen, and side postures of 5 species of male and female pupae were captured. Next, six traditional descriptors, including the histogram of oriented gradients (HOG), and six deep learning descriptors, including ConvNeXt-S, were utilized to extract significant species and sex features. Finally, classification models were constructed using the multilayer perceptron (MLP), support vector machine, and random forest. The results indicate that the {HOG + ConvNeXt-S + MLP} model excelled, achieving 99.09% accuracy for separate species and sex recognition and 98.40% for simultaneous recognition, with precision–recall and receiver operating characteristic curves ranging from 0.984 to 1.0 and 0.996 to 1.0, respectively. In conclusion, it can capture subtle distinctions between pupal species and sexes and shows promise for extensive application in sericulture.

Spider silk is part of a special class of natural protein fibers that have high strength and toughness: these materials have excellent comprehensive properties that are not found in other natural fibers (including silk) or most synthetic fibers. Spider egg case filaments have good hardness, can resist water, can protect spider eggs from external threats, have a significantly high initial modulus and high moisture absorption rate, and are expected to be used as a new generation of environmentally friendly natural polymer fibers and biomaterials. However, spiders are predatory and difficult to rear in large numbers, and it is also difficult to obtain spider egg case filaments in large quantities. Silkworms and spiders have a similar spinning system, and the use of transgenic technology in silkworms can obtain stable and high-yield exogenous gene proteins for a long time, representing an ideal bioreactor for the production of spider silk. In this study, the eukaryotic bioreactor and piggyBac transposon system were employed to recombinantly introduce the egg case silk protein of Nephila clavata (Nc-CYSP1) into the silkworm in the silkworm heavy-chain expression system. The results revealed that the silk glands produced a new type of transgenic silk with a significantly high initial modulus and high moisture absorption. In summary, this study provides an experimental reference for future research on the large-scale production and application of spider egg case filamentous protein, with great application prospects in the development of new environmentally friendly materials.