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Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification

Plant-leaf diseases have become a significant threat to food security due to reducing the quantity and quality of agricultural products. Plant disease detection methods are commonly based on experience through manual observations of leaves. Developing fast, accurate, and automated techniques for ide...

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Tác giả chính: Bui, Thi Hanh, Hoang, Van Manh, Nguyen, Ngoc Viet
Định dạng: Bài trích
Ngôn ngữ:English
Nhà xuất bản: Springer 2022
Chủ đề:
Convolutional neural network
Image augmentation
Truy cập trực tuyến:https://link.springer.com/article/10.1007/s41348-022-00601-y
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5784
https://doi.org/10.1007/s41348-022-00601-y
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spelling oai:localhost:PNK-57842022-08-17T05:54:55Z Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification Bui, Thi Hanh Hoang, Van Manh Nguyen, Ngoc Viet Convolutional neural network Image augmentation Plant-leaf diseases have become a significant threat to food security due to reducing the quantity and quality of agricultural products. Plant disease detection methods are commonly based on experience through manual observations of leaves. Developing fast, accurate, and automated techniques for identifying of crop diseases using computer vision and artificial intelligence (AI) can help overcome human shortcomings. In the current study, the EfficientNet architectures with pre-trained Noisy-Student weights were implemented using the transfer learning approach to classify leaf image-based healthy and diseased plant groups. The deep learning models were performed on the extended and enhanced PlantVillage datasets, consisting of leaf images of 14 different plant species, with background and augmented images. Early stopping and learning rate scheduler techniques were used to speed up learning and improve the efficiency of the training and testing process. The experimental results obtained on the two test sets showed that the EfficientNet-B3 and EfficientNet-B5 architectures achieved the highest performance metrics on the non-augmented and augmented datasets, respectively. The average testing accuracy of both models was up to 99.997% with good precision and sensitivity. The improved networks also revealed an excellent efficacy compared to several popularly convolutional neural networks in the literature, such as AlexNet, GoogleNet, VGGNet, ResNets, DenseNets, and MobileNets. Growingly, the enhanced artificial intelligence models may provide more powerful and practical solutions to the detection of plant-leaf diseases 2022-05-05T07:26:26Z 2022-05-05T07:26:26Z 2022 Bài trích https://link.springer.com/article/10.1007/s41348-022-00601-y https://dlib.phenikaa-uni.edu.vn/handle/PNK/5784 https://doi.org/10.1007/s41348-022-00601-y en Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic Convolutional neural network
Image augmentation
spellingShingle Convolutional neural network
Image augmentation
Bui, Thi Hanh
Hoang, Van Manh
Nguyen, Ngoc Viet
Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification
description Plant-leaf diseases have become a significant threat to food security due to reducing the quantity and quality of agricultural products. Plant disease detection methods are commonly based on experience through manual observations of leaves. Developing fast, accurate, and automated techniques for identifying of crop diseases using computer vision and artificial intelligence (AI) can help overcome human shortcomings. In the current study, the EfficientNet architectures with pre-trained Noisy-Student weights were implemented using the transfer learning approach to classify leaf image-based healthy and diseased plant groups. The deep learning models were performed on the extended and enhanced PlantVillage datasets, consisting of leaf images of 14 different plant species, with background and augmented images. Early stopping and learning rate scheduler techniques were used to speed up learning and improve the efficiency of the training and testing process. The experimental results obtained on the two test sets showed that the EfficientNet-B3 and EfficientNet-B5 architectures achieved the highest performance metrics on the non-augmented and augmented datasets, respectively. The average testing accuracy of both models was up to 99.997% with good precision and sensitivity. The improved networks also revealed an excellent efficacy compared to several popularly convolutional neural networks in the literature, such as AlexNet, GoogleNet, VGGNet, ResNets, DenseNets, and MobileNets. Growingly, the enhanced artificial intelligence models may provide more powerful and practical solutions to the detection of plant-leaf diseases
format Bài trích
author Bui, Thi Hanh
Hoang, Van Manh
Nguyen, Ngoc Viet
author_facet Bui, Thi Hanh
Hoang, Van Manh
Nguyen, Ngoc Viet
author_sort Bui, Thi Hanh
title Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification
title_short Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification
title_full Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification
title_fullStr Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification
title_full_unstemmed Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification
title_sort enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification
publisher Springer
publishDate 2022
url https://link.springer.com/article/10.1007/s41348-022-00601-y
https://dlib.phenikaa-uni.edu.vn/handle/PNK/5784
https://doi.org/10.1007/s41348-022-00601-y
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score 8.887929

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