Research on Wheat Spike Phenotype Extraction Based on YOLOv11 and Image Processing

With the aim of tuning the complexity of traditional image processing parameters, the automated extraction of spike phenotypes based on the fusion of YOLOv11 and image processing was proposed, with winter wheat in Lingcheng District, Dezhou City, Shandong Province as the research object. The keypoint detection of spikes was studied, and the integration of FocalModulation and TADDH modules improved the feature extraction ability, solved the problems of light interference and spike awn occlusion under the complex environment in the field, and the detection accuracy of the improved model reached 96.00%, and the mAP50 reached 98.70%, which were 6.6% and 2.8% higher than that of the original model, respectively. On this basis, this paper integrated morphological processing and a watershed algorithm, and innovatively constructed an integrated extraction method for spike length, spike width, and number of grains in the spike to realize the automated extraction of phenotypic parameters in the spike. The experimental results show that the extraction accuracy of spike length, spike width, and number of grains reached 98.08%, 96.21%, and 93.66%, respectively, which provides accurate data support for wheat yield prediction and genetic breeding research, and promotes the development of intelligent agricultural phenomic technology innovation.