Classification of plug seedling quality by improved convolutional neural network with an attention mechanism

• Published in: Frontiers in plant science Vol. 13; p. 967706
• Main Authors: Du, Xinwu, Si, Laiqiang, Jin, Xin, Li, Pengfei, Yun, Zhihao, Gao, Kaihang
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 04.08.2022
• Subjects: convolutional neural network; EfficientNet-B7-CBAM model; Plant Science; plug seedlings; quality classification; transfer learning
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2022.967706

The classification of plug seedling quality plays an active role in enhancing the quality of seedlings. The EfficientNet-B7-CBAM model, an improved convolutional neural network (CNN) model, was proposed to improve classification efficiency and reduce high cost. To ensure that the EfficientNet-B7 model simultaneously learns crucial channel and spatial location information, the convolutional block attention module (CBAM) has been incorporated. To improve the model’s ability to generalize, a transfer learning strategy and Adam optimization algorithm were introduced. A system for image acquisition collected 8,109 images of pepper plug seedlings, and data augmentation techniques improved the resulting data set. The proposed EfficientNet-B7-CBAM model achieved an average accuracy of 97.99% on the test set, 7.32% higher than before the improvement. Under the same experimental conditions, the classification accuracy increased by 8.88–20.05% to classical network models such as AlexNet, VGG16, InceptionV3, ResNet50, and DenseNet121. The proposed method had high accuracy in the plug seedling quality classification task. It was well-adapted to numerous types of plug seedlings, providing a reference for developing a fast and accurate algorithm for plug seedling quality classification.