Bush Detection for Vision-based UGV Guidance in Blueberry Orchards: Data Set and Methods

Abstract

Object detection has reached strong performance in the last decade, having seen its usage spreading to various application areas, such as medicine, transportation, sports, and others. However, one of the more underutilized areas where advanced detection methods have yet to fully fulfill their promise is in the area of agriculture, where a strong potential exists for applying learned models to achieve practical, real-world impact affecting a large number of people. In this work, we focus on this application area and consider the problem of orchard guidance for ground robots, focusing on obstacle and plant detection from RGB camera images. First, we present an overview of public data sets used to train models to detect relevant objects from camera images and other sensor inputs. Then we introduce a novel data set collected in blueberry orchards that contains camera images in various conditions and provides blueberry bushes as targets for detection. The introduced data set provides the research community with a novel task of blueberry bush detection, which was not commonly considered thus far due to the lack of relevant data sets. We describe a detailed analysis of the data set, and finally provide an experimental study with several state-of-the-art deep object detection models, that set a baseline for the performance on this novel data set. The data set is made available online, enriching the variability of the existing tasks in the field and supporting further development of smart agriculture applications.