Smart weeding systems to perform plant-specific operations can contribute to the sustainability of agriculture and the environment. Despite monumental advances in autonomous robotic technologies for precision weed management in recent years, work on under-canopy weeding in fields is yet to be realized. A prerequisite of such systems is reliable detection and classification of weeds to avoid mistakenly spraying and, thus, damaging the surrounding plants. Real-time multi-class weed identification enables species-specific treatment of weeds and significantly reduces the amount of herbicide use. Here, our first contribution is the first adequately large realistic image dataset \textit{AIWeeds} (one/multiple kinds of weeds in one image), a library of about 10,000 annotated images of flax, and the 14 most common weeds in fields and gardens taken from 20 different locations in North Dakota, California, and Central China. Second, we provide a full pipeline from model training with maximum efficiency to deploying the TensorRT-optimized model onto a single board computer. Based on \textit{AIWeeds} and the pipeline, we present a baseline for classification performance using five benchmark CNN models. Among them, MobileNetV2, with both the shortest inference time and lowest memory consumption, is the qualified candidate for real-time applications. Finally, we deploy MobileNetV2 onto our own compact autonomous robot \textit{SAMBot} for real-time weed detection. The 90\% test accuracy realized in previously unseen scenes in flax fields (with a row spacing of 0.2-0.3 m), with crops and weeds, distortion, blur, and shadows, is a milestone towards precision weed control in the real world. We have publicly released the dataset and code to generate the results at \url{https://github.com/StructuresComp/Multi-class-Weed-Classification}.
翻译:尽管近年来自主机器人技术在精确的杂草管理方面取得了巨大进步,但在野外的杂草方面尚有待实现。这种系统的先决条件是可靠地检测和分类杂草以避免错误喷洒,从而破坏周围植物。 实时多级杂交识别使特定物种的处理能够达到特定品种,并大大减少除草剂的使用量。 在这里,我们的第一个贡献是第一个足够大的现实图像数据集(textit{AIWeeds})(一个图像中的一种/多种杂草)、一个约有10 000张附加说明的松动图像的图书馆,以及从北达科塔、加利福尼亚和中华20个不同地点采集的14种最常见杂草。第二,我们提供从模型培训到最大效率将TensorRT-opimed模型安装到一个单一的电脑上。基于我们5个版本的平流数据数据集{AIWeed}(一个/多重杂草样的图 ) (一个图像中的一种多种杂草样的杂草 ),我们用实时实时实时检测,我们用一个最短的软的网络 数据测试,我们用直径的机在实时的服务器上, 一个测试,我们用最短的网络上, 一个测试, 一个实时的软的机 测试, 将一个测试。