End-to-End Deep Learning Model for Corn Leaf Disease Classification
Plant diseases compose a great threat to global food security. However, the rapid identification of plant diseases remains challenging and time-consuming. It requires experts to accurately identify if the plant is healthy or not and identify the type of infection. Deep learning techniques have recen...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9734016/ |
| _version_ | 1818307688964030464 |
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| author | Hassan Amin Ashraf Darwish Aboul Ella Hassanien Mona Soliman |
| author_facet | Hassan Amin Ashraf Darwish Aboul Ella Hassanien Mona Soliman |
| author_sort | Hassan Amin |
| collection | DOAJ |
| description | Plant diseases compose a great threat to global food security. However, the rapid identification of plant diseases remains challenging and time-consuming. It requires experts to accurately identify if the plant is healthy or not and identify the type of infection. Deep learning techniques have recently been used to identify and diagnose diseased plants from digital images to help automate plant disease diagnosis and help non-experts identify diseased plants. While many deep learning applications have been used to identify diseased plants and aims to increase the detection rate, the limitation of the large parameter size in the models persist. In this paper, an end-to-end deep learning model is developed to identify healthy and unhealthy corn plant leaves while taking into consideration the number of parameters of the model. The proposed model utilizes two pre-trained convolutional neural networks (CNNs), EfficientNetB0, and DenseNet121, to extract deep features from the corn plant images.The deep features extracted from each CNN are then fused using the concatenation technique to produce a more complex feature set from which the model can learn better about the dataset. In this paper, data augmentation techniques were used to add variations to the images in the dataset used to train the model, increasing the variety and number of the images and enabling the model to learn more complex cases of the data. The obtained result of this work is compared with other pre-trained CNN models, namely ResNet152 and InceptionV3, which have a larger number of parameters than the proposed model and require more processing power. The proposed model is able to achieve a classification accuracy of 98.56% which shows the superiority of the proposed model over ResNet152 and InceptionV3 that achieved a classification accuracy of 98.37% and 96.26% respectively. |
| first_indexed | 2024-12-13T07:02:22Z |
| format | Article |
| id | doaj.art-06436d5b42f643638a6cb9b90f6729b3 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T07:02:22Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-06436d5b42f643638a6cb9b90f6729b32022-12-21T23:55:53ZengIEEEIEEE Access2169-35362022-01-0110311033111510.1109/ACCESS.2022.31596789734016End-to-End Deep Learning Model for Corn Leaf Disease ClassificationHassan Amin0https://orcid.org/0000-0002-3310-0273Ashraf Darwish1https://orcid.org/0000-0002-4604-1436Aboul Ella Hassanien2Mona Soliman3Faculty of Science, Helwan University, Cairo, EgyptFaculty of Science, Helwan University, Cairo, EgyptFaculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptFaculty of Computers and Artificial Intelligence, Cairo University, Giza, EgyptPlant diseases compose a great threat to global food security. However, the rapid identification of plant diseases remains challenging and time-consuming. It requires experts to accurately identify if the plant is healthy or not and identify the type of infection. Deep learning techniques have recently been used to identify and diagnose diseased plants from digital images to help automate plant disease diagnosis and help non-experts identify diseased plants. While many deep learning applications have been used to identify diseased plants and aims to increase the detection rate, the limitation of the large parameter size in the models persist. In this paper, an end-to-end deep learning model is developed to identify healthy and unhealthy corn plant leaves while taking into consideration the number of parameters of the model. The proposed model utilizes two pre-trained convolutional neural networks (CNNs), EfficientNetB0, and DenseNet121, to extract deep features from the corn plant images.The deep features extracted from each CNN are then fused using the concatenation technique to produce a more complex feature set from which the model can learn better about the dataset. In this paper, data augmentation techniques were used to add variations to the images in the dataset used to train the model, increasing the variety and number of the images and enabling the model to learn more complex cases of the data. The obtained result of this work is compared with other pre-trained CNN models, namely ResNet152 and InceptionV3, which have a larger number of parameters than the proposed model and require more processing power. The proposed model is able to achieve a classification accuracy of 98.56% which shows the superiority of the proposed model over ResNet152 and InceptionV3 that achieved a classification accuracy of 98.37% and 96.26% respectively.https://ieeexplore.ieee.org/document/9734016/Colutional neural networksdeep learningdeep featuresfeature fusionplant disease |
| spellingShingle | Hassan Amin Ashraf Darwish Aboul Ella Hassanien Mona Soliman End-to-End Deep Learning Model for Corn Leaf Disease Classification IEEE Access Colutional neural networks deep learning deep features feature fusion plant disease |
| title | End-to-End Deep Learning Model for Corn Leaf Disease Classification |
| title_full | End-to-End Deep Learning Model for Corn Leaf Disease Classification |
| title_fullStr | End-to-End Deep Learning Model for Corn Leaf Disease Classification |
| title_full_unstemmed | End-to-End Deep Learning Model for Corn Leaf Disease Classification |
| title_short | End-to-End Deep Learning Model for Corn Leaf Disease Classification |
| title_sort | end to end deep learning model for corn leaf disease classification |
| topic | Colutional neural networks deep learning deep features feature fusion plant disease |
| url | https://ieeexplore.ieee.org/document/9734016/ |
| work_keys_str_mv | AT hassanamin endtoenddeeplearningmodelforcornleafdiseaseclassification AT ashrafdarwish endtoenddeeplearningmodelforcornleafdiseaseclassification AT aboulellahassanien endtoenddeeplearningmodelforcornleafdiseaseclassification AT monasoliman endtoenddeeplearningmodelforcornleafdiseaseclassification |