Automatic detection and counting of wheat spike based on DMseg-Count

• Published in: Scientific reports Vol. 14; no. 1; pp. 29676 - 15
• Main Authors: Zang, Hecang, Peng, Yilong, Zhou, Meng, Li, Guoqiang, Zheng, Guoqing, Shen, Hualei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 29.11.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 631/449/447; 639/705/794; Accuracy; Agricultural production; Algorithms; Computer vision; Crop production; Crop yield; Deep learning; Field phenotyping; Humanities and Social Sciences; Image Processing, Computer-Assisted - methods; Information processing; Labeling; Local segmentation branch; Methods; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Spike counting; Supervision; Triticum; Unmanned aerial vehicles; Wheat; Deep learning; Field phenotyping; Spike counting; Wheat; Local segmentation branch
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-80244-1

The automatic detection and counting of wheat spike images are of great significance for yield prediction and variety evaluation. Therefore, accurate and timely estimation of spike numbers is crucial for wheat production. However, in actual production, due to the susceptibility of wheat spike images to factors such as lighting conditions, shooting angles, occlusion, and overlap, the contour and features of wheat spike is unclear, which affects the accuracy of automatic detection and counting of wheat spike. In order to solve the above problems and further improve the accuracy of wheat spike counting, an improved wheat spike counting model DMseg-Count was proposed by enhancing local contextual supervision information based on existing target object counting model DM-Count. Firstly, wheat spike local segmentation branch was introduced to improve the network architecture of DM-Count, so as to extract the local contextual supervision information of wheat spike. Secondly, an element-by-element point multiplication mechanism was designed to fuse global and local contextual supervision information of wheat spike. Finally, the total loss function was constructed to optimize the model. The test results showed that the mean absolute error (MAE) and root mean square error (RMSE) of the proposed DMseg-Count model were 5.79 and 7.54, respectively, which were 9.76 and 10.91 higher than the standard distribution matching for crowd counting (DM-Count) model. Compared with other deep learning models, the proposed DMseg-Count model can detect wheat spike image in challenging situations, and has better computer vision processing capabilities and performance evaluation detection effect. In summary, the proposed DMseg-Count model can effectively detect wheat spike and has good counting performance, which provides a new method for automatic counting of wheat spike and yield prediction in complex field environments.