In vermicompost production regaining red californian worm eggs by separating from compost with deep learning techniques

Deep learning techniques, which are being used effectively in many areas today, will become more and more important every day and will increase the competitiveness of producers by contributing significantly to the digital transformation in the agricultural sector. In this thesis work, worm eggs detected by CNN model created using SSD architecture were separated from compost and recycled to production. The data set consists of 1000 compost images containing 3809 worm eggs and a single class named "cocoon" representing the worm eggs. The success of different iteration numbers in object detection has been observed in the SSD architecture used by transferring learning over VGG16. The CNN model used in the thesis was written in the Anaconda Spyder IDE platform in Python programming language and was trained with a GeForce RTX 2080 GPU. The training of the network created with the SSD model proposed in the thesis study was carried out in 1000 epochs by doing data augmentation and transfer learning with 1000 images and 1e-5 learning rate in the data set. As a result of the training, the loss value decreased to 2.527 at the 800th epoch and to 2.494 at the 1000th epoch, and after this point, it followed a horizontal course with converging values. AP value of the single-class model increased up to 84.9% at the 1000th epoch and the best result was obtained.