Mesías-Ruiz Gustavo A, Peña José M, de Castro Ana I, Dorado José
Institute of Agricultural Sciences, Spanish National Research Council (ICA-CSIC), Serrano 115b, 28006 Madrid, Spain.
Environment and Agronomy Department, National Agricultural and Food Research and Technology Institute (INIA-CSIC), Ctra. Coruña km 7.5, Madrid, 28008 Madrid, Spain.
Data Brief. 2024 Dec 6;58:111203. doi: 10.1016/j.dib.2024.111203. eCollection 2025 Feb.
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 and . In tomato, the weed species include and . 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.
在早期生长阶段识别杂草种类对精准农业至关重要。物种层面的准确分类能够采取有针对性的控制措施，显著减少农药使用。本文展示了一个RGB图像数据集，这些图像是由安装在离地11米高空飞行的无人机上的索尼ILCE - 6300L相机拍摄的。该数据集涵盖了西班牙的各种农田，重点关注两种夏季作物：玉米和番茄。它通过纳入两个物候阶段的图像来提高季初杂草分类的准确性。具体而言，该数据集包含31002张来自早期生长阶段的标注图像——有四片展开叶的玉米（BBCH14）和可见第一花芽的番茄（BBCH501），以及36556张来自更高级生长阶段的图像——有七片展开叶的玉米（BBCH17）和可见第九花芽的番茄（BBCH509）。在玉米中，杂草种类包括……和……。在番茄中，杂草种类包括……和……。这些以JPG格式存储的图像在正射镶嵌分区中进行了标注，每张图像对应一个特定的植物物种。该数据集非常适合用于开发先进的深度学习模型，如卷积神经网络（CNNs）和视觉Transformer（ViTs），以利用无人机图像对玉米和番茄作物中的杂草种类进行早期分类。通过提供这个数据集，我们旨在推进基于无人机的杂草检测和测绘技术，用更高效、准确的工具为精准农业做出贡献，促进可持续和盈利的耕作实践。
