TRAINING OF YOLOV5 NEURAL NETWORK FOR PEAR DETECTION IN ORCHARD

Abstract

A fruit-growing is an important branch of agriculture for various reasons. Fruits provide essential nutrients and vitamins to our diet, and they are also a significant source of income for fruit-growers. To improve the efficiency of fruit cultivation, we trained a pear detection neural network with YOLOv5 architecture using a dataset from the project lzp-2021/1-0134. The dataset contained 1273 photographs of pear trees with image sizes 640x640px. We had trained the neural network model YOLOv5m five times and achieved the best result equal to mAP@0.5 0.8 and mAP@0.5:0.95 0.43. The use of artificial intelligence in fruit cultivation can help to optimize the planning of fruit picking, contributing to the precision horticulture.