Detection of Gibberella Infection Rate in Wheat Based on MHSA-YOLOv7
In disease resistance breeding,the infection rate of gibberella in wheat is an important indicator to measure the phenotype identification of grain resistance.In view of the problems of long detection time,high hardware cost and damage to plants in the detection of wheat gibberella infection,a deep learning network model,or MHSA-YOLOv7 suitable for the detection of small objects such as wheat ear grain is designed.By integrating the Muti-Head Self-Attention(MHSA)mechanism in the original YOLOv7 backbone network,the model can extract deep semantic features,and the weighted Bidirectional Feature Pyramid Network(BiFPN)is used to realize the cross-layer connection between modules,so that the model can extract and transmit richer feature information.The experimental results show that MHSA-YOLOv7 achieves a detection accuracy of 90.75%on the wheat single ear gibberella dataset.Compared with the original YOLOv7 model,the improved algorithm has stronger feature extraction ability for small objects such as wheat ear grain,and the detection Accuracy,Recall,F1 score,mAP@0.5 and mAP@0.5:0.95 are improved by 0.33%,1.83%,0.011,1.19%and 0.38%respectively.The improved algorithm effectively satisfies the accurate detection of wheat gibberella infection rate,and provides technical support for long-term observation of wheat disease trends and accurate assessment of wheat grain resistance.
国家科技期刊平台
NSTL
万方数据
维普
