Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM

Rui Ma, Jia Wang, Wei Zhao, Hongjie Guo, Dongnan Dai, Yuliang Yun, Li Li, Fengqi Hao, Jinqiang Bai and Dexin Ma ()
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Rui Ma: College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China
Jia Wang: College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China
Wei Zhao: College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China
Hongjie Guo: College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China
Dongnan Dai: College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China
Yuliang Yun: College of Mechanical and Electrical Engineering, Qingdao Agricultural University, Qingdao 266109, China
Li Li: Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing 100083, China
Fengqi Hao: Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Jinqiang Bai: Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan 250014, China
Dexin Ma: College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China

Agriculture, 2022, vol. 13, issue 1, 1-16

Abstract: Seeds are the most fundamental and significant production tool in agriculture. They play a critical role in boosting the output and revenue of agriculture. To achieve rapid identification and protection of maize seeds, 3938 images of 11 different types of maize seeds were collected for the experiment, along with a combination of germ and non-germ surface datasets. The training set, validation set, and test set were randomly divided by a ratio of 7:2:1. The experiment introduced the CBAM (Convolutional Block Attention Module) attention mechanism into MobileNetV2, improving the CBAM by replacing the cascade connection with a parallel connection, thus building an advanced mixed attention module, I_CBAM, and establishing a new model, I_CBAM_MobileNetV2. The proposed I_CBAM_MobileNetV2 achieved an accuracy of 98.21%, which was 4.88% higher than that of MobileNetV2. Compared to Xception, MobileNetV3, DenseNet121, E-AlexNet, and ResNet50, the accuracy was increased by 9.24%, 6.42%, 3.85%, 3.59%, and 2.57%, respectively. Gradient-Weighted Class Activation Mapping (Grad-CAM) network visualization demonstrates that I_CBAM_MobileNetV2 focuses more on distinguishing features in maize seed images, thereby boosting the accuracy of the model. Furthermore, the model is only 25.1 MB, making it suitable for portable deployment on mobile terminals. This study provides effective strategies and experimental methods for identifying maize seed varieties using deep learning technology. This research provides technical assistance for the non-destructive detection and automatic identification of maize seed varieties.

Keywords: MobileNetV2; CBAM; image classification; maize seeds (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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