An algorithm for crops segmentation in UAV images based on U-Net CNN model: Application to Sugarbeets plants

LCS Laboratory, Physics Dept. faculty of Science, Mohammed 5 University in Rabat, Ibn Battouta Street, 10000, Rabat, Morocco

^* Corresponding author: khalid.elamraoui @um5r.ac.ma

Abstract

In recent years, Digital Agriculture (DA) has been widely developed using new technologies and computer vision technics. Drones and Machine learning have proved their efficiency in the optimization of the agricultural management. In this paper we propose an algorithm based on U-Net CNN Model to crops segmentation in UAV images. The algorithm patches the input images into several 256×256 sub-images before creating a mask (ground-truth) that will be fed into a U-Net Model for training. A set of experimentation has been done on real UAV images of Sugerbeets crops, where the mean intersection over Union (MIoU) and the Segmentation accuracy (SA) metrics are adopted to evaluate its performances against other algorithms used in the literature. The proposed algorithm show a good segmentation accuracy compared to three well-known algorithms for UAV image segmentation.