Local Unsupervised Wheat Head Segmentation

Traditional wheat head detection and segmentation methods based on machine learning algorithms suffer from issues such as low efficiency and poor accuracy, resulting in the algorithms’ inability to generalize. The recent advances in deep learning, specifically in object detection methods, as well as computer development, have enabled the development of robust wheat head detection and segmentation methods. However, while international datasets of box labels are available for head detection, mask labels for segmentation are missing, and collecting them on a large scale is prohibitively expensive, time-consuming, and difficult. In this paper, we propose an unsupervised approach for segmenting wheat heads based only on box labels. Multiple state-of-the-art object detection methods have been trained on reference datasets and our collected data in order to find the best model to extract head bounding boxes. The obtained boxes were used as input of an unsupervised segmentation model named DeepMAC, which predicts the head mask in each box. Then, those masks are exploited to train several state-of-the-art supervised segmentation models. These models showed promising results on the collected dataset, covering all the wheat development stages. The average F1 score of head bounding box detection is 0.93 and the average F1 score of segmentation is 0.86.