A soybean and weed image dataset collected using FarmBot in a controlled outdoor field environment: raw and curated image data for precision agriculture research

This dataset consists of images of soybean plants and weeds captured during a controlled outdoor field experiment conducted at Weihenstephan-Triesdorf University of Applied Sciences (HSWT) in Freising, Germany. The images were collected using a FarmBot precision agriculture robot equipped with an ELP USB4KHDR01-MFV camera featuring a Sony IMX317 sensor and a 5–50 mm optical zoom lens. All images were recorded in 4K Ultra HD resolution (3840 × 2160 pixels). Soybean seeds were planted on August 14, 2025, following standard agronomic spacing guidelines, with 5 cm between seeds and 37.5 cm between rows across three parallel rows. Image acquisition began on August 24, 2025, which was eight days after the first signs of germination were observed. Data collection continued until September 20, 2025, covering a total period of 25 consecutive days. During each day, 60 images were captured—30 in the morning and 30 in the afternoon—using automated scripts developed with a Python-based API together with the FarmBot operating system interface. From the initial collection of 1,037 images, a total of 641 high-quality images from the first 20 days of observation were retained after a quality assessment process. Images were selected based on criteria such as focus, consistent exposure, and clear separation between plant classes. Images from days 21 to 25 were excluded because the high density of weeds made it difficult to clearly distinguish between classes. All selected images were annotated using the Roboflow platform with bounding box annotations created in collaboration with a domain expert. The annotations label instances of soybean plants and weeds within each image. The resulting dataset is intended to support research in computer vision applications for agriculture, including weed detection, crop growth monitoring, and automated precision farming systems. It can be used for training and evaluating models for tasks such as object detection, image segmentation, and image classification in agricultural environments.本数据集包含在德国弗赖辛市魏亨斯泰凡-特里斯多夫应用技术大学（Weihenstephan-Triesdorf University of Applied Sciences，HSWT）开展的受控户外田间试验中拍摄的大豆植株与杂草图像。图像采集使用搭载ELP USB4KHDR01-MFV相机的FarmBot精准农业机器人完成，该相机配备索尼IMX317传感器与5–50 mm光学变焦镜头，所有图像均以4K超高清分辨率（3840 × 2160像素）录制。 大豆种子于2025年8月14日遵循标准农艺种植间距规范播种，种子间距为5 cm，行间距为37.5 cm，共设置3条平行种植行。图像采集始于2025年8月24日，即首次观察到发芽迹象后的第8天，数据采集持续至2025年9月20日，总计覆盖25个连续采集日。每日采集60张图像，上午、下午各30张，采集脚本基于Python API开发，并结合FarmBot操作系统界面完成。 初始采集的1037张图像中，经质量评估流程后，保留了前20个观测日的641张高质量图像。筛选标准包括对焦清晰、曝光均匀、作物类别边界清晰可辨。第21至25日的图像因杂草密度过高，难以清晰区分作物类别而被排除。 所有入选图像均通过Roboflow平台完成标注，由领域专家协作生成边界框标注，标注内容为每张图像中的大豆植株与杂草实例。本数据集旨在支撑农业领域计算机视觉应用研究，包括杂草检测、作物生长监测及自动化精准农业系统开发，可用于训练与评估农业场景下目标检测、图像分割及图像分类等任务的模型。