Context. Automatic weed detection and control is crucial in precision agriculture, especially in cereal fields where overlapping crops and narrow row spacing present significant challenges. This research prioritized small weed detection and its performance in dense images by using innovative techniques.

Aims. This study investigated two recent convolutional neural networks (CNNs) with different architectures and detection models for weed detection in cereal fields. The feature pyramid network (FPN) technique was applied to improve performance. To tackle challenges such as high weed density and occlusion, a method of dividing images into smaller parts with pixel area thresholds was implemented, achieving an approximately 22% increase in average precision (AP).

Methods. The dataset includes red–green–blue (RGB) images of cereal fields captured in Germany (2018–2019) at varying growth stages. Images were annotated using ‘LabelImg’, assigning weed labels. Models were evaluated by precision, recall, prediction time, and detection rate.

Key results. The evaluation results showed that the FasterRCNN-ResNet50 with FPN had the best performance in terms of detection numbers. In the tests, the model successfully detected 508 of 535 annotated weeds in 36 images, achieving a detection rate of 94.95%, with a 95% confidence interval of [92.76%, 96.51%]. Additionally, a method was proposed to boost average precision and recall in high-density weed images, enhancing detection operations.

Conclusions. The results of this research showed that the presented algorithms and methods have a high ability to solve above-mentioned challenges.

Implications. This research evaluated deep learning models, and recommends the best and stresses reliable weed identification at all growth stages.