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

Du, Xinwu; Si, Laiqiang; Jin, Xin; Li, Pengfei; Yun, Zhihao; Gao, Kaihang

doi:10.3389/fpls.2022.967706

Cited by 13 publications

(11 citation statements)

References 39 publications

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“…Our research results were similar to those of Zhang et al [29] who added the CBAM in the YOLOv4 model to enhance the feature extraction ability of the model, and the results showed that the mAP@0.5 of When they identified the sheep, group1 and group2 were 91.58% and 90.61%, respectively. This was also proved by the study of Du et al [28]. They incorporated the CBAM in the EfficientNet-B7 model to classify the plug seedling quality.…”

Section: Models Back Datasetmentioning

confidence: 70%

“…The convolutional block attention module (CBAM) [27] can effectively improve the accuracy of the model by using the spatial and channel features of the images to redistribute the feature weights and strengthen the feature differences of the image. Du et al [28] effectively classified the quality of plug seedlings using the improved CNN based on the attention mechanism. Zhang et al [29] improved the YOLOv4 model with the CBAM to realize sheep facial biometrics recognition.…”

Section: Introductionmentioning

confidence: 99%

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Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion

Bai,

Zhu,

et al. 2023

Foods

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To achieve accurate detection the content of multiple parts pork adulterated in mutton under the effect of mutton flavor essence and colorant by RGB images, the improved CBAM-Invert-ResNet50 network based on the attention mechanism and the inversion residual was used to detect the content of pork from the back, front leg, and hind leg in adulterated mutton. The deep features of different parts extracted by the CBAM-Invert-ResNet50 were fused by feature, stitched, and combined with transfer learning, and the content of pork from mixed parts in adulterated mutton was detected. The results showed that the R2 of the CBAM-Invert-ResNet50 for the back, front leg, and hind leg datasets were 0.9373, 0.8876, and 0.9055, respectively, and the RMSE values were 0.0268 g·g−1, 0.0378 g·g−1, and 0.0316 g·g−1, respectively. The R2 and RMSE of the mixed dataset were 0.9264 and 0.0290 g·g−1, respectively. When the features of different parts were fused, the R2 and RMSE of the CBAM-Invert-ResNet50 for the mixed dataset were 0.9589 and 0.0220 g·g−1, respectively. Compared with the model built before feature fusion, the R2 of the mixed dataset increased by 0.0325, and the RMSE decreased by 0.0070 g·g−1. The above results indicated that the CBAM-Invert-ResNet50 model could effectively detect the content of pork from different parts in adulterated mutton as additives. Feature fusion combined with transfer learning can effectively improve the detection accuracy for the content of mixed parts of pork in adulterated mutton. The results of this study can provide technical support and a basis for maintaining the mutton market order and protecting mutton food safety supervision.

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Section: Models Back Datasetmentioning

confidence: 70%

Section: Introductionmentioning

confidence: 99%

Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion

Bai,

Zhu,

et al. 2023

Foods

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“…It was reported that the improvement in the accuracy with augmentation was 1.54% 15 . Although various conditions may be involved, in this study, increasing the number of images generated by augmentation sometimes reduced accuracy by a few percent to 10%.…”

Section: Discussionmentioning

confidence: 99%

“…Augmentation can increase the diversity of the data 14 . Thus, augmentation has been performed in studies using deep learning in various fields 15 , 16 , but it is not clear what kind of augmentation improves the performance of the model. In this study, we tried various augmentation methods to build a better deep learning model from a small dataset.…”

Section: Introductionmentioning

confidence: 99%

Deep learning classification of urinary sediment crystals with optimal parameter tuning

Nagai

Onodera

Okuda

2022

Sci Rep

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The examination of urinary sediment crystals, the sedimentary components of urine, is useful in screening tests, and is always performed in medical examinations. The examination of urinary sediment crystals is typically done by classifying them under a microscope. Although automated analyzers are commercially available, manual classification is required, which is time-consuming and varies depending on the technologist performing the test and the laboratory. A set of test images was created, consisting of training, validation, and test images. The training images were transformed and augmented using various methods. The test images were classified to determine the patterns that could be correctly classified. Convolutional neural networks were used for training. Furthermore, we also considered the case where the crystal subcategories were not treated as separate. Learning with all parameters except the random cropping parameter showed the highest accuracy value. Treating the subcategories together or separately did not seem to affect the accuracy value. The accuracy of the best pattern was 0.918. When matched to a real-world case, the percentage of correct answers was 88%. Although the number of images was limited, good results were obtained in the classification of crystal images with optimal parameter tuning. The parameter optimization performed in this study can be used as a reference for future studies, with the goal of image classification by deep learning in clinical practice.

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“…To realize the culling and replanting of cavity seedlings, the first step is to complete the screening of cavity seedlings, and the powerful feature extraction capability of convolutional neural networks is the preferred solution for image classification. We used the EfficientNet-B7-CBAM deep learning model, which was previously improved by our research team [39]. Convolutional neural networks are typically trained by increasing the image resolution, increasing the network width, or adding residual structures to deepen the network.…”

Section: ) Identification Of Information On Cavity Seedlingsmentioning

confidence: 99%

Design and Experiments of a Vision-Guided Integrated Cavity Tray Seedling Culling and Replanting System

Du,

Gao,

2023

IEEE Access

Self Cite

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Greenhouse seedling facilities have become a major method for modern vegetable production. In the process of raising seedlings, culling and replanting of cavity seedlings are essential to ensure the quality of the cavity seedling output. However, the process of culling and replanting is complex, traditional manual work is intensive and inefficient, and existing mechanized equipment is relatively homogeneous and low in intelligence. In this study, an integrated culling-replanting system based on visual guidance was developed to address these issues. It allows for real-time feedback of equipment operation information through the human-machine interface, culling and replanting of unqualified holes, and detection of culled seedling growth information. Further prototypes were built and culling and replanting trials were conducted. The results of the trials showed that the average success rates of culling and replanting were 93.00%, 83.07%, and 76.95% for culling and replanting with hole sizes of 21, 32, and 50, respectively. The average survival rates after surgery were all above 95.00%. The designed vision-guided integrated culling and replanting system is stable in operation, compact, has a strong human-machine interaction, and has a high transplanting success rate, which can effectively solve the problems of high intensity and low efficiency of culling and replanting operations in the culling and replanting processes, improve the intelligence level of the replanting equipment, and meet the actual operational requirements of the seedling breeding process.

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

Cited by 13 publications

References 39 publications

Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion

Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion

Deep learning classification of urinary sediment crystals with optimal parameter tuning

Design and Experiments of a Vision-Guided Integrated Cavity Tray Seedling Culling and Replanting System

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