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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| Veröffentlicht in: | IET image processing Jg. 15; H. 10; S. 2157 - 2168 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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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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| Cites_doi | 10.3390/app9224898 10.13031/trans.11700 10.1109/ICMLA.2016.0178 10.1016/j.compag.2018.07.032 10.1016/j.dib.2019.104340 10.1016/j.micpro.2021.104027 10.1109/CDMA47397.2020.00031 10.1007/s11263-013-0620-5 10.1016/j.suscom.2019.08.002 10.3390/su12125037 10.32604/cmc.2020.012945 10.1016/j.compag.2018.04.023 10.1016/j.compind.2018.02.005 10.1002/jemt.23320 10.1007/s12571-012-0200-5 10.1007/978-3-319-59063-9_44 10.1111/exsy.12746 10.1109/ICPR.2006.479 10.1111/exsy.12569 10.1007/s11760-015-0821-1 10.1155/2017/2917536 10.1007/s11042-020-08726-8 10.1016/j.compag.2018.01.009 10.1016/j.compag.2018.10.013 10.3390/sym10010011 10.1109/ACCESS.2019.2908040 10.1007/978-3-319-27863-6_59 10.32604/cmc.2020.012257 10.1007/s10658-016-1007-6 10.1109/ICIIP.2013.6707647 10.1109/CVPR.2009.5206848 10.1155/2017/7361042 10.1109/ACCESS.2020.3034217 10.3389/fpls.2016.01419 10.1016/j.neunet.2017.10.009 10.1016/j.measurement.2014.05.033 10.1109/CVPR.2017.106 10.1109/ACCESS.2019.2914929 |
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| References_xml | – volume: 66 start-page: 1949 year: 2021 end-page: 1962 article-title: Deep learning‐based classification of fruit diseases: An application for precision agriculture publication-title: CMC‐Computers Materials & Continua – start-page: 248 year: 2009 end-page: 255 article-title: Imagenet: A large‐scale hierarchical image database – volume: 155 start-page: 220 year: 2018 end-page: 236 article-title: CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features publication-title: Comput. Electron. Agric. – volume: 82 start-page: 1542 year: 2019 end-page: 1556 article-title: Intelligent microscopic approach for identification and recognition of citrus deformities publication-title: Microsc. Res. Tech. – volume: 2017 start-page: 1 year: 2017 end-page: 8 article-title: Automatic image‐based plant disease severity estimation using deep learning publication-title: Comput. Intell. Neurosci. – volume: 98 start-page: 23 year: 2018 end-page: 33 article-title: Visual features based boosted classification of weeds for real‐time selective herbicide sprayer systems publication-title: Comput. Ind. – start-page: 521 year: 2013 end-page: 526 article-title: Image processing for smart farming: Detection of disease and fruit grading – year: 2019 article-title: Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection publication-title: Expert Systems – start-page: 2117 year: 2017 end-page: 2125 article-title: Feature pyramid networks for object detection – start-page: 850 year: 2006 end-page: 855 article-title: Efficient non‐maximum suppression – volume: 24 start-page: 475 issue: 4 year: 2018 end-page: 487 article-title: Real time path finding for assisted living using deep learning publication-title: Journal of Universal Computer Science – volume: 55 start-page: 512 year: 2014 end-page: 519 article-title: Potential of radial basis function‐based support vector regression for apple disease detection publication-title: Measurement – volume: 7 start-page: 1419 year: 2016 article-title: Using deep learning for image‐based plant disease detection publication-title: Front. Plant Sci. – volume: 2017 start-page: 1 year: 2017 end-page: 6 article-title: Deep learning for plant identification in natural environment publication-title: Comput. Intell. and Neuroscience – volume: 12 start-page: 5037 year: 2020 article-title: A sustainable deep learning framework for object recognition using multi‐layers deep features fusion and selection publication-title: Sustainability – volume: 60 start-page: 303 year: 2017 end-page: 312 article-title: Detection of apple marssonina blotch disease using particle swarm optimization publication-title: Trans. ASABE – year: 2012 – volume: 10 start-page: 74 year: 2017 end-page: 83 article-title: Apple leaf disease identification using genetic algorithm and correlation based feature selection method publication-title: International Journal of Agricultural and Biol. Eng. – start-page: 638 year: 2015 end-page: 645 article-title: Basic study of automated diagnosis of viral plant diseases using convolutional neural networks – volume: 10 start-page: 819 year: 2016 end-page: 826 article-title: Apple disease classification using color, texture and shape features from images publication-title: Signal, Image and Video Processing – volume: 7 issue: 2 year: 2020 article-title: Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing publication-title: PeerJ Computer Science – volume: 13 start-page: 45 issue: 2 year: 2016 end-page: 60 article-title: A novel neural networks‐based texture image processing algorithm for orange defects classification publication-title: International Journal of Computer Science and Applications – volume: 150 start-page: 220 year: 2018 end-page: 234 article-title: Detection and classification of citrus diseases in agriculture based on optimized weighted segmentation and feature selection publication-title: Comput. Electron. Agric. – year: 2021 article-title: Recognition of apple leaf diseases using deep learning and variances‐controlled features reduction publication-title: Microprocess. Microsyst. – volume: 7 start-page: 46261 year: 2019 end-page: 46277 article-title: An optimized method for segmentation and classification of apple diseases based on strong correlation and genetic algorithm based feature selection publication-title: IEEE Access – volume: 26 year: 2019 article-title: A citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning publication-title: Data in Brief – year: 2017 publication-title: arXiv:1711.04325 – start-page: 989 year: 2016 end-page: 992 article-title: Basic investigation on a robust and practical plant diagnostic system – volume: 153 start-page: 12 year: 2018 end-page: 32 article-title: An automated detection and classification of citrus plant diseases using image processing techniques: A review publication-title: Comput. Electron. Agric. – volume: 79 start-page: 18627 year: 2020 end-page: 18656 article-title: An automated system for cucumber leaf diseased spot detection and classification using improved saliency method and deep features selection publication-title: Multimedia Tools and Applications – volume: 9 start-page: 4898 year: 2019 article-title: Automated identification of wood veneer surface defects using faster region‐based convolutional neural network with data augmentation and transfer learning publication-title: Appl. Sci. – volume: 24 year: 2019 article-title: Diagnosis and recognition of grape leaf diseases: An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion publication-title: Sustainable Computing: Informatics and Systems – volume: 10245 start-page: 497 year: 2017 end-page: 506 article-title: The image classification with different types of image features publication-title: Lect. Notes Comput. Sci. – start-page: 2017 year: 2015 end-page: 2025 – volume: 98 start-page: 16 year: 2018 end-page: 33 article-title: Adaptive neuro‐heuristic hybrid model for fruit peel defects detection publication-title: Neural Networks – year: 2017 – year: 2020 article-title: Entropy‐controlled deep features selection framework for grape leaf diseases recognition publication-title: Expert Systems – volume: 7 start-page: 59069 year: 2019 end-page: 59080 article-title: Real‐time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks publication-title: IEEE Access – volume: 66 start-page: 2199 year: 2021 end-page: 2216 article-title: Severity recognition of aloe vera diseases using AI in tensor flow domain publication-title: CMC‐Computers Materials & Continua – volume: 147 start-page: 349 year: 2017 end-page: 364 article-title: A new automatic method for disease symptom segmentation in digital photographs of plant leaves publication-title: European Journal of Plant Pathol. – volume: 10 start-page: 11 year: 2018 article-title: Identification of apple leaf diseases based on deep convolutional neural networks publication-title: Symmetry – year: 2021 article-title: Cassava disease recognition from low quality images using enhanced data augmentation model and deep learning publication-title: Expert Systems – volume: 104 start-page: 154 year: 2013 end-page: 171 article-title: Selective search for object recognition publication-title: Int. J. Comput. Vision – volume: 145 start-page: 311 year: 2018 end-page: 318 article-title: Deep learning models for plant disease detection and diagnosis publication-title: Comput. Electron. Agric. – start-page: 146 year: 2020 end-page: 151 article-title: Advanced Machine Learning Algorithm Based System for Crops Leaf Diseases Recognition – volume: 8 start-page: 197969 year: 2020 end-page: 197981 article-title: StomachNet: Optimal deep learning features fusion for stomach abnormalities classification publication-title: IEEE Access – ident: e_1_2_5_18_1 doi: 10.3390/app9224898 – ident: e_1_2_5_32_1 doi: 10.13031/trans.11700 – ident: e_1_2_5_46_1 doi: 10.1109/ICMLA.2016.0178 – ident: e_1_2_5_6_1 doi: 10.1016/j.compag.2018.07.032 – ident: e_1_2_5_9_1 doi: 10.1016/j.dib.2019.104340 – volume: 13 start-page: 45 issue: 2 year: 2016 ident: e_1_2_5_11_1 article-title: A novel neural networks‐based texture image processing algorithm for orange defects classification publication-title: International Journal of Computer Science and Applications – ident: e_1_2_5_14_1 doi: 10.1016/j.micpro.2021.104027 – volume: 24 start-page: 475 issue: 4 year: 2018 ident: e_1_2_5_26_1 article-title: Real time path finding for assisted living using deep learning publication-title: Journal of Universal Computer Science – ident: e_1_2_5_15_1 doi: 10.1109/CDMA47397.2020.00031 – volume: 10 start-page: 74 year: 2017 ident: e_1_2_5_7_1 article-title: Apple leaf disease identification using genetic algorithm and correlation based feature selection method publication-title: International Journal of Agricultural and Biol. Eng. – ident: e_1_2_5_37_1 doi: 10.1007/s11263-013-0620-5 – year: 2017 ident: e_1_2_5_40_1 publication-title: arXiv:1711.04325 – ident: e_1_2_5_10_1 doi: 10.1016/j.suscom.2019.08.002 – ident: e_1_2_5_25_1 doi: 10.3390/su12125037 – ident: e_1_2_5_27_1 doi: 10.32604/cmc.2020.012945 – ident: e_1_2_5_5_1 doi: 10.1016/j.compag.2018.04.023 – ident: e_1_2_5_13_1 doi: 10.1016/j.compind.2018.02.005 – ident: e_1_2_5_29_1 doi: 10.1002/jemt.23320 – ident: e_1_2_5_2_1 doi: 10.1007/s12571-012-0200-5 – ident: e_1_2_5_17_1 doi: 10.1007/978-3-319-59063-9_44 – ident: e_1_2_5_19_1 doi: 10.1111/exsy.12746 – ident: e_1_2_5_38_1 doi: 10.1109/ICPR.2006.479 – ident: e_1_2_5_20_1 doi: 10.1111/exsy.12569 – ident: e_1_2_5_30_1 doi: 10.1007/s11760-015-0821-1 – volume: 2017 start-page: 1 year: 2017 ident: e_1_2_5_47_1 article-title: Automatic image‐based plant disease severity estimation using deep learning publication-title: Comput. Intell. Neurosci. doi: 10.1155/2017/2917536 – ident: e_1_2_5_16_1 doi: 10.1007/s11042-020-08726-8 – volume: 7 issue: 2 year: 2020 ident: e_1_2_5_4_1 article-title: Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing publication-title: PeerJ Computer Science – start-page: 2017 volume-title: Advances in Neural Information Processing Systems year: 2015 ident: e_1_2_5_39_1 – ident: e_1_2_5_22_1 doi: 10.1016/j.compag.2018.01.009 – ident: e_1_2_5_3_1 doi: 10.1016/j.compag.2018.10.013 – ident: e_1_2_5_35_1 – ident: e_1_2_5_45_1 doi: 10.3390/sym10010011 – ident: e_1_2_5_8_1 doi: 10.1109/ACCESS.2019.2908040 – ident: e_1_2_5_44_1 doi: 10.1007/978-3-319-27863-6_59 – ident: e_1_2_5_28_1 doi: 10.32604/cmc.2020.012257 – ident: e_1_2_5_33_1 doi: 10.1007/s10658-016-1007-6 – ident: e_1_2_5_43_1 doi: 10.1109/ICIIP.2013.6707647 – ident: e_1_2_5_41_1 doi: 10.1109/CVPR.2009.5206848 – volume: 2017 start-page: 1 year: 2017 ident: e_1_2_5_23_1 article-title: Deep learning for plant identification in natural environment publication-title: Comput. Intell. and Neuroscience doi: 10.1155/2017/7361042 – ident: e_1_2_5_24_1 doi: 10.1109/ACCESS.2020.3034217 – ident: e_1_2_5_42_1 doi: 10.3389/fpls.2016.01419 – ident: e_1_2_5_12_1 doi: 10.1016/j.neunet.2017.10.009 – ident: e_1_2_5_31_1 doi: 10.1016/j.measurement.2014.05.033 – ident: e_1_2_5_36_1 doi: 10.1109/CVPR.2017.106 – ident: e_1_2_5_21_1 doi: 10.1109/ACCESS.2019.2914929 – year: 2019 ident: e_1_2_5_34_1 article-title: Skin lesion segmentation and classification: A unified framework of deep neural network features fusion and selection publication-title: Expert Systems |
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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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