[en] 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.
Electrical & electronics engineering
Author, co-author :
Ennadifi, Elias ; Université de Mons - UMONS
Mokhtari, Mohammed El Amine ; Université de Mons - UMONS > Faculté Polytechnique > Service Information, Signal et Intelligence artificielle
Laraba, Sohaib ; Université de Mons - UMONS > Faculté Polytechnique > Service Information, Signal et Intelligence artificielle
Gosselin, Bernard ; Université de Mons - UMONS > Faculté Polytechnique > Service Information, Signal et Intelligence artificielle
Local Unsupervised Wheat Head Segmentation
Publication date :
22 September 2022
Event name :
2022 IEEE 18th International Conference on Intelligent Computer Communication and Processing
Event place :
Event date :
September 22-24 2022
Event number :
By request :
Journal title :
Peer reviewed :
Research unit :
F105 - Information, Signal et Intelligence artificielle
Research institute :
R450 - Institut NUMEDIART pour les Technologies des Arts Numériques
Name of the research project :
3970 - PHENWHEAT - Caractérisation de la dynamique de croissance de cultures de froment d’hiver au moyen d’une plateforme de phénotypage par proxidétection en conditions variables de stress biotique et abiotique - Région wallonne
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