DIWEED: Drone Imagery dataset for early-season WEED classification

Identification of weed species at the early stages of growth is critical for precision agriculture. Accurate detection and classification to species level allows targeted control measures to be taken, significantly reducing pesticide use. This dataset consists of RGB images, captured with a Sony ILCE-6300L camera mounted on an unmanned aerial vehicle (UAV) at 11 meters altitude. The dataset covers several agricultural fields in Spain, focusing on two summer crops: corn and tomato. It is designed to improve the accuracy of early season weed detection by including images from two phenological stages. Specifically, the dataset contains 31,002 labeled images from the early growth stage - maize with four leaves unfolded (BBCH14) and tomato with the first flower bud visible (BBCH501) - as well as 36,556 images from a later growth stage - maize with seven leaves unfolded (BBCH17) and tomato with the ninth flower bud visible (BBCH509). In maize, weed species include Atriplex patula, Chenopodium album, Convolvulus arvensis, Datura ferox, Lolium rigidum, Salsola kali and Sorghum halepense. In tomato, weed species include Cyperus rotundus, Portulaca oleracea and Solanum nigrum. The images, stored in JPG format, were labeled by partitioning orthomosaics, with each image corresponding to a specific plant species. This dataset is ideal for developing advanced deep learning models, such as CNN and ViT, for early detection and classification of weed species in corn 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 and accurate tools that promote sustainable and profitable agricultural practices.