Abstract
Identifying weed species at early-growth stages is critical for precision agriculture. Accurate classification at the species-level enables targeted control measures, significantly reducing pesticide use. This paper presents a dataset of RGB images captured with a Sony ILCE-6300L camera mounted on an unmanned aerial vehicle (UAV) flying at an altitude of 11 m above ground level. The dataset covers various agricultural fields in Spain, focusing on two summer crops: maize and tomato. It is designed to enhance early-season weed classification accuracy by including images from two phenological stages. Specifically, the dataset contains 31,002 labeled images from the early-growth stage-maize with four unfolded leaves (BBCH14) and tomato with the first flower bud visible (BBCH501)-as well as 36,556 images from a more advanced-growth stage-maize with seven unfolded leaves (BBCH17) and tomato with the ninth flower bud visible (BBCH509). In maize, the weed species include Atriplex patula, Chenopodium album, Convolvulus arvensis, Datura ferox, Lolium rigidum, Salsola kali and Sorghum halepense. In tomato, the weed species include Cyperus rotundus, Portulaca oleracea and Solanum nigrum. The images, stored in JPG format, were labeled in orthomosaic partitions, with each image corresponding to a specific plant species. This dataset is ideally suited for developing advanced deep learning models, such as CNNs and ViTs, for early classification of weed species in maize and tomato crops using UAV imagery. By providing this dataset, we aim to advance UAV-based weed detection and mapping technologies, contributing to precision agriculture with more efficient, accurate tools that promote sustainable and profitable farming practices.