Maize seed appearance quality assessment based on improved Inception-ResNet

Frontiers in Plant Science

Plant Science

Lei Sun¹  Huanyue Wang¹  Chang Song¹  Xiaofei Fan¹  Bo Peng¹  Yuhong Zhou¹  Xuesong Suo¹ 

[1] College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, China;

关键词: corn seed;    quality assessment;    depthwise separable convolution;    attention mechanism;    feature fusion;   

DOI  :  10.3389/fpls.2023.1249989

 received in 2023-06-29, accepted in 2023-08-04,  发布年份 2023

来源: Frontiers

【 摘 要 】

Current inspections of seed appearance quality are mainly performed manually, which is time-consuming, tedious, and subjective, and creates difficulties in meeting the needs of practical applications. For rapid and accurate identification of seeds based on appearance quality, this study proposed a seed-quality evaluation method that used an improved Inception-ResNet network with corn seeds of different qualities. First, images of multiple corn seeds were segmented to build a single seed image database. Second, the standard convolution of the Inception-ResNet module was replaced by a depthwise separable convolution to reduce the number of model parameters and computational complexity of the network. In addition, an attention mechanism was applied to improve the feature learning performance of the network model and extract the best image information to express the appearance quality. Finally, the feature fusion strategy was used to fuse the feature information at different levels to prevent the loss of important information. The results showed that the proposed method had decent comprehensive performance in detection of corn seed appearance quality, with an average of 96.03% for detection accuracy, 96.27% for precision, 96.03% for recall rate, 96.15% for F1 value of reconciliation, and the average detection time of an image was about 2.44 seconds. This study realized rapid nondestructive detection of seeds and provided a theoretical basis and technical support for construction of intelligent seed sorting equipment.

【 授权许可】

Unknown   
Copyright © 2023 Song, Peng, Wang, Zhou, Sun, Suo and Fan