Experience with the YOLOv5 neural network for sunflower plant detection
Abstract
Experience with the YOLOv5 neural network for sunflower plant detection
Incoming article date: 17.09.2022This article describes the results of research on the possibility of detecting sunflower plants from photographs taken by a UAV. The solution to this problem will allow automated control of an important agricultural parameter - seedling density. The problem is complicated by the limited amount of training sample and "disturbances" associated with field weeding. As the result we obtain that the YOLOv5m neural network is capable on a sample of 122 pictures to qualitatively detect plants with an error of 0.077% of training. Artificially increasing the sample to 363 pictures reduces the learning error to 0.063%. Disturbances reduce the detection efficiency of sunflower plants in the test images. It is possible to increase the detection efficiency either by adding original images to the training sample or by artificially enlarging the sample.
Keywords: detection, YOLOv5, sunflower, seedling density, neural network