Integrated Analysis of Machine Learning and Deep Learning in Silkworm Pupae (Bombyx mori) Species and Sex Identification.

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.

In situ preparation of molybdenum-dioxide-incorporated carbonized silk fiber and its application in supercapacitors.

A previous study found that the capacitive behavior of nanoparticles fed to the silkworm can be delivered to carbonized silk fibers, which can be used to fabricate electrodes for the construction of flexible supercapacitors. However, the tendency of nanoparticles to aggregate decreases the quantity of nanoparticles that enter the silk and therefore reduces the capacitance performance of the prepared carbonized silk. Here, we sprayed ammonium molybdate tetrahydrate (AMT) on the surface of mulberry leaves used for feeding silkworms and investigated the effect of feeding AMT on the growth of silkworms and the properties of spun silk. The precursor incorporated into the silk was converted into scattered MoO2 NPs, which were embedded within the carbonized silk fiber (CSF) via carbothermal reduction. The specific capacitance of CSF obtained from silkworms fed with an aqueous solution of AMT-treated mulberry leaves reached up to 298 F/g at 0.2 g/A, which is much higher than that of the control group (102 F/g). Since AMT is highly water-soluble, and its concentration can be easily modulated, we believe that the proposed strategy is feasible for the large-scale fabrication of CSF with enhanced capacitive performance.