Detection method of maize seed appearance quality based on multi-spectrum
Defective seeds significantly affect seed quality and pricing,and their sorting and removal is an important part of seed quality detection.At present,the seed quality detection is mainly completed by manual operation,which is inefficient and subjective.Aiming at the need for rapid and accurate identification of corn seeds in appearance quality detection,this paper proposed an improved YOLOv5 target detection model with input of multi-spectral RGB+NIR+NIR1 imaging information of corn seeds to identify and classify appearance quality of corn seeds.By changing the Spatial pyramid pooling(SPP)structure in the YOLOv5 backbone network CSPDarknet,the efficiency of network model detection was improved,and the attention mechanism was used to strengthen the feature information fusion in the feature extraction network to improve the accuracy of network model detection.The test results showed that the comprehensive evaluation index F1 value of the improved model YOLOv5+SE+SPPF reached 96.71%,the mAP value reached 96.96%,the average time for each image detection was about 0.28 s,and the average time for each seed detection was about 20 ms,which provided a reference for achieving efficient and accurate seed quality detection and optimal grading,and can be applied to the intelligent seed sorting equipment.
NETL
NSTL
万方数据
