ArticleFrontiers in plant science2023
A lightweight method for maize seed defects identification based on Convolutional Block Attention Module.
Article in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Integrating AI in seed science: Toward an intelligent design paradigm.Plant communications · 2026Review
- Grading evaluation of haploid fertility restoration traits based on inception-ResNet in maize.Plant phenomics (Washington, D.C.) · 2025Article
- Enhancement of the prediction of the openness of fresh-cut roses with an improved YOLOv8s model validated by an automatic Grading Machine.Frontiers in plant science · 2025Article
- Classification of maize seed hyperspectral images based on variable-depth convolutional kernels.Frontiers in plant science · 2025Article
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5 authors.
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Abstract
Maize is widely cultivated and planted all over the world, which is one of the main food resources. Accurately identifying the defect of maize seeds is of great significance in both food safety and agricultural production. In recent years, methods based on deep learning have performed well in image processing, but their potential in the identification of maize seed defects has not been fully realized. Therefore, in this paper, a lightweight and effective network for maize seed defect identification is proposed. In the proposed network, the Convolutional Block Attention Module (CBAM) was integrated into the pretrained MobileNetv3 network for extracting important features in the channel and spatial domain. In this way, the network can be focused on useful feature information, and making it easier to converge. To verify the effectiveness of the proposed network, a total of 12784 images was collected, and 7 defect types were defined. Compared with other popular pretrained models, the proposed network converges with the least number of iterations and achieves the true positive rate is 93.14% and the false positive rate is 1.14%.
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