Information about the presence of weeds in fields is important to decide on a weed control strategy. This is especially crucial in precision weed management, where the position of each plant is essential for conducting mechanical weed control or patch spraying.

For detecting weeds, this study proposes a fully convolutional neural network, which detects weeds in images and classifies each one as either a monocot or dicot. The network has been trained on over 13 000 weed annotations in high-resolution RGB images from Danish wheat  and rye fields. Due to occlusion in cereal fields, weeds can be partially hidden behind or touching the crops or other weeds, which the network handles.

The network can detect weeds with an average precision (AP) of 0.76. The weed detection network has been evaluated on an Nvidia Titan X, on which it is able to process a 5 MPx image in 0.02 s, making the method suitable for real-time field operation.