Akademik Veri Yönetim Sistemi
Turkish Journal of Engineering, cilt.9, sa.1, ss.1-11, 2025 (Scopus)
Although human dependence on agriculture decreases with developing technology, it continues. As many resources are increasingly restricted due to various climatic reasons, the importance of studies in this field increases. Applications using deep learning models are frequently encountered in the agricultural field. In particular, there are applications where deep learning models are used as a tool for optimum planting, land use, yield improvement, production/disease/pest control, and other activities.In this study, watermelons in an aerial view of a watermelon field were detected by utilizing the Alexnet deep learning architecture. To obtain yield, watermelons in watermelon fields should be specified and then counted. Aerial images are used for this application. The field image was divided into 50% overlapping sub-images, and each was classified as watermelon, leaf, and soil. Consequently, watermelon regions on the field image were specified. After training the Alexnet and Vgg19 network structure with the dataset, watermelons were to be identified by segmenting the images. It was observed that the Vgg19 network achieved 97.78% accuracy. The results of the experimental applications show that the Vgg19 can be applied for watermelon fruit and yield detection applications.