A key challenge for autonomous precision weeding is to reliably and accurately detect weed plants and crop plants in real time to minimise damage to surrounding crop plants while performing weeding actions. Specifically for a wheat farm, classifying ryegrass weed plants is particularly difficult even with human eyes since ryegrass shows visually very similar shape and texture to the crop plants themselves. A Deep Neural Network (DNN) that exploits the geometric location of ryegrass is proposed for the real time segmentation of inter-row ryegrass weeds in a wheat field. Our proposed method introduces two subnets in a conventional encoder-decoder style DNN to improve segmentation accuracy. The two subnets treat inter-row and intra-row pixels differently, and provide corrections to preliminary segmentation results of the conventional encoder-decoder DNN. A dataset captured in a wheat farm by an agricultural robot at different time instances is used to evaluate the segmentation performance, and the proposed method performs the best among various popular semantic segmentation algorithms. The proposed method runs at 48.95 Frames Per Second (FPS) with a consumer level graphics processing unit, thus is real-time deployable at camera frame rate.

自主精确除草的关键挑战是实时可靠，准确地检测杂草和农作物，以在执行除草动作时最大程度地减少对周围农作物的损害。特别是对于小麦农场，即使用肉眼也很难对黑麦草杂草进行分类，因为黑麦草的外观和质地与农作物本身非常相似。提出了一种利用黑麦草的几何位置的深度神经网络（DNN），用于麦田中行间黑麦草杂草的实时分割。我们提出的方法以传统的编码器-解码器样式DNN引入了两个子网，以提高分段精度。这两个子网对行间像素和行内像素的处理方式不同，并提供对常规编码器-解码器DNN初步分割结果的校正。一个农业机器人在不同时间点在小麦农场捕获的数据集用于评估分割性能，并且该方法在各种流行的语义分割算法中表现最好。所提出的方法以消费者级别的图形处理单元以48.95帧/秒（FPS）的速度运行，因此可以以摄像机帧速率实时部署。