Deep Learning Algorithm for Object Detection with Depth Measurement in Precision Agriculture

Aguirre Santiago, Leonardo Solaque, Alexandra Velasco


Autonomous driving in precision agriculture will have an important impact for the field. This is why several efforts have been done in this direction. We have developed an agricultural robotic platform named CERES, which has a payload of 100 Kg of solid fertilizer, 20 liters for fumigating purposes, and a weeding system. Our research points to make this robot autonomous. In this paper, we propose a method, based on deep learning algorithms, to combine object detection with depth measurements for object tracking and decision making of an agro-robot. For this, we combine an object detection algorithm carried out with YOLOv2 and a depth measurement strategy implemented with a ZED Camera. The main purpose is to determine the distance to the obstacles, mainly people, because we require to prevent collisions and damages either for people and for the robot. We have chosen to detect people because, in the desired environment, these are frequent and unpredictable obstacles, and the risk of collision may be high.We use a host computer, achieving a detection network with an average accuracy of up to 72% in detecting the class Person. While using a NVIDIA Jetson TX1, the accuracy increases up to 84% due to the powerful dedicated GPU destined to process Convolutional Neural Networks(CNN).


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