Wheat head counting in the wild by an augmented feature pyramid networks-based convolutional neural network

Wheat head counting plays an important role in crop yield estimation, which also meets great challenges of high density, scale variation, and illumination difference. In this paper, an improved wheat head counting network (WHCnet) was proposed to count wheat heads from the top view in the wild, with a novel object counting by detection pipeline. Firstly, data enhancement such as contrast adjustment and Gaussian blur were conducted to improve the generalization ability. Secondly, the Augmented Feature Pyramid Networks (AugFPN) was used to pool the original information adaptively, which made full use of the underlying information and solved the problem of poor detection of small wheat heads. Finally, cascaded Intersection over Union (IoU) threshold was used to make the IoU of the training model as close as possible to the input target, which could effectively remove negative samples in the complex background, reduce the noise bounding box and improve the target positioning accuracy of occluded wheat heads. Experiments on the test set image showed that the proposed method had an average error rate of 3.7% and an AP of 95.17%, which was significantly better than other state-of-the-art methods. Besides, the average counting time for a single wheat field image (3968 x 2976) obtained by an un-maimed aerial vehicle was 87 ms, and the counting accuracy was 95.8%, which effectively verified the generalization ability of the model. These results indicate that the proposed method can meet the requirements of wheat head counting in the field environment and provide reliable reference data for wheat yield estimation.