Farm robots need better training data to see and pick crops
A new survey reveals that agricultural robotics relies on camera and depth-sensing technology to perform tasks like harvesting and weeding, but publicly available training datasets remain scarce and expensive to create. The shortage is slowing commercialization of autonomous farming systems that could address labor shortages and reduce costs for growers.
Originaltitel: RGB-D datasets for robotic perception in site-specific agricultural operations: a survey
<p>Fusing color (RGB) images and range or depth (D) data in the form of RGB-D or multi-sensory setups is a relatively new but rapidly growing modality for many agricultural tasks. RGB-D data have potential to provide valuable information for many agricultural tasks that rely on perception, but collection of appropriate data and suitable ground truth information can be challenging and labor-intensive, and high-quality publicly available datasets are rare. This paper presents a survey of the existing RGB-D datasets available for agricultural robotics, and summarizes key trends and challenges in this research field. It evaluates the relative advantages of the commonly used sensors, and how the hardware can affect the characteristics of the data collected. It also analyzes the role of RGB-D data in the most common vision-based machine learning tasks applied to agricultural robotic operations: visual recognition, object detection, and semantic segmentation, and compares and contrasts methods that utilize 2-D and 3-D perceptual data.</p>