A phenotyping weeds image dataset for open scientific research

This in-house-built image dataset consists of 10810 weed images captured through a dedicated phenotyping activity in quasi-field conditions. The targets are seven of the most widespread and hard-to-control weeds in wheat (but also in other winter cereals) in the Mediterranean environment. In the framework of open scientific research, our aim is to share low-cost and high-resolution images representing challenging agricultural environments where weather, lighting and other factors can change by the hour and affect the quality of images. This way the dataset could be used to train Artificial Intelligence architectures designed for weed recognition, allowing the implementation of tools directly available in the field for farmers and technicians for effective and timely weed management. The dataset encompasses weed images ranging from the post-emergence phase (i.e. the complete cotyledons unfolding) until the pre-flowering stage. The weed selection was made by considering (i) bottom-up information and specific requests by farmers and technicians, (ii) weed susceptibility to commercial formulations for chemical control <50%, reported at least twice by field technicians, (iii) the difficulty of control considering any methods, and (iv) the type of growing season (overlapping or not with wheat). The final weeds selection encompassed both monocots (Avena sterilis and Lolium multiflorum) and dicots (Convolvulus arvensis, Fumaria officinalis, Papaver rhoeas, Veronica persica and Vicia sativa). Image acquisition was facilitated by using a white panel as a background; this helped to (i) spread the light and thereby make the plants well-illuminated, while still avoiding strong shadows when using the flash and (ii) simplify image processing. The images were acquired with a Canon EOS 700D hand-held camera set in the macro mode with aperture, shutter speed, ISO and flash in auto mode. Photo capture timing, target distances and light conditions did not have a fixed pattern but were deliberately programmed to vary in such a way as to mimic field conditions. For image shooting at various times of the day, the only precaution was to frame the subject with homogeneous light conditions (full sunlight/full shade). The varied outdoor conditions (light, distance, timing) and camera type (RGB) with auto mode were essential features to make the images photos look similar to those that a user can take in a field, for example with a smartphone camera. After selection and categorization, images were cropped to select the region of interest following the 1:1 ratio but maintaining a minimum size of 512 x 512 pixels. More details on the dataset and its use for weed recognition tasks will be soon available in the proceedings of the forthcoming ECPA conference (2-6 July 2023, Bologna, Italy).

本自研图像数据集包含10810张杂草图像，均通过针对准田间环境的专属表型分析（phenotyping）活动采集获取。数据集的目标对象为地中海环境下小麦（及其他冬谷类作物）田中7种分布最广、最难防治的杂草。本数据集依托开放科学研究框架构建，旨在共享具有挑战性的农业环境场景下的低成本高分辨率图像——此类场景中天气、光照等因素随时变化，会对图像质量产生影响。借此，该数据集可用于训练专为杂草识别（weed recognition）设计的人工智能架构（Artificial Intelligence architectures），进而助力开发可直接部署于田间的工具，帮助农户与技术人员实现高效及时的杂草防治管理。数据集涵盖的杂草图像覆盖从出苗后阶段（即子叶完全展开时期）至开花前阶段的各个生长时期。本次杂草筛选综合考量了以下四项标准：(i) 一线反馈信息与农户、技术人员的具体需求；(ii) 经田间技术人员至少两次验证的、对商用化学防治药剂敏感性低于50%的杂草；(iii) 各类防治手段下均难以管控的杂草；(iv) 生育期与小麦是否重叠的杂草类型。最终入选的杂草涵盖单子叶植物（即野燕麦*Avena sterilis*与多花黑麦草*Lolium multiflorum*）与双子叶植物（即田旋花*Convolvulus arvensis*、紫堇*Fumaria officinalis*、虞美人*Papaver rhoeas*、波斯婆婆纳*Veronica persica*与救荒野豌豆*Vicia sativa*）。图像采集过程采用白色背景板辅助拍摄，其作用包括：(i) 均匀布光，使植株光照充足，同时在使用闪光灯时避免产生浓重阴影；(ii) 简化后续图像处理流程。所有图像均采用佳能EOS 700D手持相机拍摄，设置为微距模式（macro mode），光圈（aperture）、快门速度（shutter speed）、ISO感光度（ISO）及闪光灯均处于自动档位。拍摄时机、拍摄距离与光照条件均无固定模式，而是刻意设置为动态变化，以尽可能贴近真实田间环境。针对一日内不同时段的拍摄，唯一的规范是确保拍摄主体处于均匀光照环境（即完全光照或完全遮阴状态）。多样化的户外环境变量（光照、拍摄距离、拍摄时机）与采用自动模式的RGB相机设置，是使图像贴近用户使用智能手机等设备在田间实拍效果的核心要素。经过筛选与分类后，图像将按照1:1比例裁剪以提取感兴趣区域（region of interest），且裁剪后的图像最小尺寸不低于512×512像素。有关本数据集及其在杂草识别任务中应用的更多细节，将于近期在即将召开的ECPA会议（2023年7月2日-6日，意大利博洛尼亚）的会议论文集中公开。