MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases
The presence of various maize plant leaf diseases has significantly decreased both the quality and quantity of crop production. In order to take the appropriate steps to prevent the occurrence of plant leaf diseases, it is essential to track and recognize such infections during the planting period....
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10136691/ |
| _version_ | 1797785494895984640 |
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| author | Momina Masood Marriam Nawaz Tahira Nazir Ali Javed Reem Alkanhel Hela Elmannai Sami Dhahbi Sami Bourouis |
| author_facet | Momina Masood Marriam Nawaz Tahira Nazir Ali Javed Reem Alkanhel Hela Elmannai Sami Dhahbi Sami Bourouis |
| author_sort | Momina Masood |
| collection | DOAJ |
| description | The presence of various maize plant leaf diseases has significantly decreased both the quality and quantity of crop production. In order to take the appropriate steps to prevent the occurrence of plant leaf diseases, it is essential to track and recognize such infections during the planting period. However, correct recognition of numerous maize diseases is difficult to achieve because the currently employed automated solutions are operationally complicated or only effective on samples with plain backgrounds. While real-world scenarios are suffering from huge sample distortions like the effect of noise, clutter in the background, and blurring of the leaf regions that increase the complexity of the recognition procedure. To alleviate the above-listed problems, a deep learning (DL) approach called the MaizeNet is proposed for the correct localization and classification of various maize crop leaf disorders. We have presented an improved Faster-RCNN approach that utilizes the ResNet-50 model with spatial-channel attention as its base network for the computation of deep keypoints which are then localized and categorized into various classes. The proposed work is tested on a standard database named Corn Disease and Severity that contains samples from three different classes of maize plant diseases which are captured under diverse conditions such as complex background, brightness, and color and size variations. The MaizeNet model attains an average accuracy score of 97.89% along with mAP value of 0.94, which is showing the effectiveness of our approach for locating and classifying the numerous types of maize leaf infections. |
| first_indexed | 2024-03-13T00:55:51Z |
| format | Article |
| id | doaj.art-1e1dfc79d3034f2c98457d3347159f51 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T00:55:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1e1dfc79d3034f2c98457d3347159f512023-07-06T23:00:14ZengIEEEIEEE Access2169-35362023-01-0111528625287610.1109/ACCESS.2023.328026010136691MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf DiseasesMomina Masood0https://orcid.org/0000-0003-1977-1481Marriam Nawaz1https://orcid.org/0000-0002-2238-4645Tahira Nazir2https://orcid.org/0000-0001-8130-3721Ali Javed3https://orcid.org/0000-0002-1290-1477Reem Alkanhel4https://orcid.org/0000-0001-6395-4723Hela Elmannai5https://orcid.org/0000-0003-2571-1848Sami Dhahbi6Sami Bourouis7https://orcid.org/0000-0002-6638-7039Department of Computer Science, University of Engineering and Technology at Taxila, Taxila, PakistanDepartment of Software Engineering, University of Engineering and Technology at Taxila, Taxila, PakistanDepartment of Computer Science, Faculty of Computing, Riphah International University Gulberg Green Campus, Islamabad, PakistanDepartment of Software Engineering, University of Engineering and Technology at Taxila, Taxila, PakistanDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science, College of Science and Art at Muhayil Aseer, King Khalid University, Muhayil Aseer, Saudi ArabiaDepartment of Information Technology, College of Computers and Information Technology, Taif University, Taif, Saudi ArabiaThe presence of various maize plant leaf diseases has significantly decreased both the quality and quantity of crop production. In order to take the appropriate steps to prevent the occurrence of plant leaf diseases, it is essential to track and recognize such infections during the planting period. However, correct recognition of numerous maize diseases is difficult to achieve because the currently employed automated solutions are operationally complicated or only effective on samples with plain backgrounds. While real-world scenarios are suffering from huge sample distortions like the effect of noise, clutter in the background, and blurring of the leaf regions that increase the complexity of the recognition procedure. To alleviate the above-listed problems, a deep learning (DL) approach called the MaizeNet is proposed for the correct localization and classification of various maize crop leaf disorders. We have presented an improved Faster-RCNN approach that utilizes the ResNet-50 model with spatial-channel attention as its base network for the computation of deep keypoints which are then localized and categorized into various classes. The proposed work is tested on a standard database named Corn Disease and Severity that contains samples from three different classes of maize plant diseases which are captured under diverse conditions such as complex background, brightness, and color and size variations. The MaizeNet model attains an average accuracy score of 97.89% along with mAP value of 0.94, which is showing the effectiveness of our approach for locating and classifying the numerous types of maize leaf infections.https://ieeexplore.ieee.org/document/10136691/Attentionclassificationdeep learningfaster-RCNNlocalizationMaize disease |
| spellingShingle | Momina Masood Marriam Nawaz Tahira Nazir Ali Javed Reem Alkanhel Hela Elmannai Sami Dhahbi Sami Bourouis MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases IEEE Access Attention classification deep learning faster-RCNN localization Maize disease |
| title | MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases |
| title_full | MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases |
| title_fullStr | MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases |
| title_full_unstemmed | MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases |
| title_short | MaizeNet: A Deep Learning Approach for Effective Recognition of Maize Plant Leaf Diseases |
| title_sort | maizenet a deep learning approach for effective recognition of maize plant leaf diseases |
| topic | Attention classification deep learning faster-RCNN localization Maize disease |
| url | https://ieeexplore.ieee.org/document/10136691/ |
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