Vermicompost, created by earthworms after eating and digesting organic waste, plays an important role as an organic fertiliser in sustainable agriculture. In this study, a deep learning-based smart system was developed to separate earthworm cocoons used in the production of vermicompost from the compost and return it to production. In the first stage of the study, a dataset containing 1000 images of cocoons was created. The cocoons in each image were labeled and training was performed using a deep learning architecture, one-stage and two-stage models. The models were trained over 2000 epochs with a learning rate of 0.01. From the experimental results, faster R-CNN with ResNet50-FPN model detected the earthworm cocoons better compared to other models. The best performance was obtained by this model with an average precision (AP) of 0.89. In the other stage of the study, the cocoons detected by the software were separated from the compost using a specially designed conveyor belt system. In this process, the detected cocoons are separated from the compost using 10 pneumatic valves that spray air at the separation point. The study is the first of its kind that enables earthworm cocoons to be returned to production with the use of a real-time intelligent system. It also contributes to the literature on small object detection using deep learning.
Object detection, vermicompost, earthworm cocoon, agriculture
ÇELİK, ALİ and UĞUZ, SİNAN
"A deep learning based system for real-time detection and sorting of earthworm cocoons,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 30:
5, Article 20.
Available at: https://journals.tubitak.gov.tr/elektrik/vol30/iss5/20