Training of Yolov5 for Apple Detection in Orchard

Fruit cultivation is a significant part of the country economics and agriculture. In this paper, aiming to improve apple cultivation, we trained a neural network for apple detection using the YOLOv5 architecture, utilizing the dataset from the lzp-2021/1-0134 project. The dataset consisted of a set of apple tree photographs with apple fruits. Dataset contained 40 images with size 640x640 px. YOLOv5m model was trained five times. The best result model achieved mAP@0.5 equal to 0.9 and mAP@0.5:0.95 equal to 0.63. The artificial intelligence opens new possibilities for horticulture saving resources, which can be redirected on other tasks significantly increasing the efficiency of commercial orchards.