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<abstract> <p>This paper presents an autonomous navigation method for an agricultural mobile robot "AgriEco Robot", with four-wheel-drive and embedded perception sensors. The proposed method allows an accurate guidance between strawberry crop rows while automatically spraying pesticides, as well as detecting the end and switching to the next rows. The main control system was developed using Robot Operating System (ROS) based on a 2D LIDAR sensor. The acquired 2D point clouds data is processed to estimate the robot's heading and lateral offset relative to crop rows. A motion controller is incorporated to ensure the developed autonomous navigation method. Performance in terms of accuracy of the autonomous navigation has been evaluated in real-world conditions within strawberry greenhouses, proving its usefulness for automatic pesticide spraying.</p> </abstract>

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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.