A high‐resolution, multimodal data set for agricultural robotics: A Ladybird 's‐eye view of Brassica

This article presents an agricultural data set collected by Ladybird, an autonomous field robot designed at the Australian Centre for Field Robotics. The data set contains weekly scans of cauliflower and broccoli (Brassica oleracea) covering a 10 week growth cycle from transplant to harvest. The data set includes ground truth; physical characteristics of the crop; environmental data collected by a weather station and a soil sensor network; and scans of the crop performed by Ladybird, which include stereo color, thermal and hyperspectral imagery. The layout of the farm and data collection methodology are described. A description of Ladybird's capabilities and sensors are provided. Benchmark results are provided to illustrate the contents of the data set and how it can be processed. The hyperspectral data are compiled into hypercubes and the pixels are classified into crop, weed, or soil. An object detector is applied to the color imagery to locate the crop. Our intention in releasing the data set is to facilitate robotics and machine learning research activity in agriculture. The data set can be downloaded from .