Silk production is currently being reestablished in Europe for several reasons. Since the process of silk production is time-consuming, it needs to be automated as much as possible. One of the processes is the sorting of silk cocoons by quality and defects, which we focused on. In the assignment, we developed a machine and a program for automated sorting of cocoons. For this we used machine learning. Image data of silk cocoons and their mass were used to train the models. Machine learning was implemented using several algorithms, including random forests, linear support vector machines, kernel support vector machines, kernel stochastic gradient descent, the k-nearest neighbors algorithm, and the YOLO26n model combined with logistic regression. Experimental results indicate that the kernel support vector machine model performed the best, achieving an accuracy of 96 %. The same accuracy was achieved with our customized YOLO26n model, modified to incorporate mass as an input feature. The machine's maximum productivity was 15 cocoons per minute.
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