Recognizing apple leaf diseases using a novel parallel real‐time processing framework based on MASK RCNN and transfer learning: An application for smart agriculture
Effective recognition of fruit leaf diseases has a substantial impact on agro‐based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked‐eye inspection of an infected region is a difficult and tedious process; therefore, it is required to have an automat...
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| Vydáno v: | IET image processing Ročník 15; číslo 10; s. 2157 - 2168 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Wiley
01.08.2021
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| ISSN: | 1751-9659, 1751-9667 |
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| Abstract | Effective recognition of fruit leaf diseases has a substantial impact on agro‐based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked‐eye inspection of an infected region is a difficult and tedious process; therefore, it is required to have an automated system for accurate recognition of the disease. It is widely understood that low contrast images affect identification and classification accuracy. Here a parallel framework for real‐time apple leaf disease identification and classification is proposed. Initially, a hybrid contrast stretching method to increase the visual impact of an image is proposed and then the MASK RCNN is configured to detect the infected regions. In parallel, the enhanced images are utilized for training a pre‐trained CNN model for features extraction. The Kapur's entropy along MSVM (EaMSVM) approach‐based selection method is developed to select strong features for the final classification. The Plant Village dataset is employed for the experimental process and achieve the best accuracy of 96.6% on the ensemble subspace discriminant analysis (ESDA) classifier. A comparison with the previous techniques illustrates the superiority of the proposed framework. |
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| AbstractList | Abstract Effective recognition of fruit leaf diseases has a substantial impact on agro‐based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked‐eye inspection of an infected region is a difficult and tedious process; therefore, it is required to have an automated system for accurate recognition of the disease. It is widely understood that low contrast images affect identification and classification accuracy. Here a parallel framework for real‐time apple leaf disease identification and classification is proposed. Initially, a hybrid contrast stretching method to increase the visual impact of an image is proposed and then the MASK RCNN is configured to detect the infected regions. In parallel, the enhanced images are utilized for training a pre‐trained CNN model for features extraction. The Kapur's entropy along MSVM (EaMSVM) approach‐based selection method is developed to select strong features for the final classification. The Plant Village dataset is employed for the experimental process and achieve the best accuracy of 96.6% on the ensemble subspace discriminant analysis (ESDA) classifier. A comparison with the previous techniques illustrates the superiority of the proposed framework. Effective recognition of fruit leaf diseases has a substantial impact on agro‐based economies. Several fruit diseases exist that badly impact the yield and quality of fruits. A naked‐eye inspection of an infected region is a difficult and tedious process; therefore, it is required to have an automated system for accurate recognition of the disease. It is widely understood that low contrast images affect identification and classification accuracy. Here a parallel framework for real‐time apple leaf disease identification and classification is proposed. Initially, a hybrid contrast stretching method to increase the visual impact of an image is proposed and then the MASK RCNN is configured to detect the infected regions. In parallel, the enhanced images are utilized for training a pre‐trained CNN model for features extraction. The Kapur's entropy along MSVM (EaMSVM) approach‐based selection method is developed to select strong features for the final classification. The Plant Village dataset is employed for the experimental process and achieve the best accuracy of 96.6% on the ensemble subspace discriminant analysis (ESDA) classifier. A comparison with the previous techniques illustrates the superiority of the proposed framework. |
| Author | Rehman, Zia ur Ahmed, Fawad Naqvi, Syed Rameez Damaševičius, Robertas Javed, Kashif Khan, Muhammad Attique Nisar, Wasif |
| Author_xml | – sequence: 1 givenname: Zia ur surname: Rehman fullname: Rehman, Zia ur organization: HITEC University Taxila – sequence: 2 givenname: Muhammad Attique surname: Khan fullname: Khan, Muhammad Attique organization: HITEC University Taxila – sequence: 3 givenname: Fawad surname: Ahmed fullname: Ahmed, Fawad organization: HITEC University Taxila – sequence: 4 givenname: Robertas surname: Damaševičius fullname: Damaševičius, Robertas email: robertas.damasevicius@polsl.pl organization: Silesian University of Technology – sequence: 5 givenname: Syed Rameez surname: Naqvi fullname: Naqvi, Syed Rameez organization: COMSATS University Islamabad – sequence: 6 givenname: Wasif surname: Nisar fullname: Nisar, Wasif organization: COMSATS University Islamabad – sequence: 7 givenname: Kashif surname: Javed fullname: Javed, Kashif organization: SMME Nust |
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| Snippet | Effective recognition of fruit leaf diseases has a substantial impact on agro‐based economies. Several fruit diseases exist that badly impact the yield and... Abstract Effective recognition of fruit leaf diseases has a substantial impact on agro‐based economies. Several fruit diseases exist that badly impact the... |
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| SubjectTerms | Agriculture Computer vision and image processing techniques Optical, image and video signal processing Other topics in statistics Products and commodities |
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| Title | Recognizing apple leaf diseases using a novel parallel real‐time processing framework based on MASK RCNN and transfer learning: An application for smart agriculture |
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