2022 International Electrical Engineering Congress (iEECON) 2022
DOI: 10.1109/ieecon53204.2022.9741677
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Paddy seed variety classification using transfer learning based on deep learning

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Cited by 15 publications
(4 citation statements)
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“…Computer vision (CV) and Image processing applications in agriculture are of utmost significance because of their low cost and non-destructive evaluation compared to manual approaches [1]. CV applications depend on image processing presenting benefits compared to conventional approaches relying upon manual work.…”
Section: Introductionmentioning
confidence: 99%
“…Computer vision (CV) and Image processing applications in agriculture are of utmost significance because of their low cost and non-destructive evaluation compared to manual approaches [1]. CV applications depend on image processing presenting benefits compared to conventional approaches relying upon manual work.…”
Section: Introductionmentioning
confidence: 99%
“…Ma R et al [19] used parallel connections instead of cascade connections to improve the CBAM attention, and after adding it into MobileNetV2 for the identification of maize seed varieties, the final accuracy reached 98.21%. Jaithavil D et al [20] used Transfer Learning to train three network models VGG16, InceptionV3 and MobileNetV2 on more than 1200 paddy seed datasets with overall recognition rates of 80.00%, 83.33% and 83.33% respectively. Zhou Y et al [21] added attention modules such as SELayer [22], EcaLayer [23], CBAM [24] and CoordAtt to YoloV5s and after experiments, it was found that YoloV5s with the addition of the CoordAtt attention had the best performance.…”
Section: Introductionmentioning
confidence: 99%
“…Elfatimi E. [22] classified the bean leaf dataset using the MobileNetV2 model, and achieved an average classification accuracy of over 97% on the training dataset, and over 92% on the test data. Jaithavil D. [23] used multiple CNNs to classify rice seed varieties, and the experimental results illustrated that the overall accuracy of VGG16, InceptionV3, and MobileNetV2 was 80.00%, 83.33%, and 83.33%, respectively. Zhang Z [24] proposed a rice disease identification system using lightweight MobileNetV2.…”
Section: Introductionmentioning
confidence: 99%