A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases
IntroductionPaddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.MethodsIn this paper, infections in paddy leaves are consid...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Plant Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1234067/full |
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| author | Shweta Lamba Vinay Kukreja Junaid Rashid Thippa Reddy Gadekallu Thippa Reddy Gadekallu Thippa Reddy Gadekallu Thippa Reddy Gadekallu Thippa Reddy Gadekallu Jungeun Kim Anupam Baliyan Deepali Gupta Shilpa Saini |
| author_facet | Shweta Lamba Vinay Kukreja Junaid Rashid Thippa Reddy Gadekallu Thippa Reddy Gadekallu Thippa Reddy Gadekallu Thippa Reddy Gadekallu Thippa Reddy Gadekallu Jungeun Kim Anupam Baliyan Deepali Gupta Shilpa Saini |
| author_sort | Shweta Lamba |
| collection | DOAJ |
| description | IntroductionPaddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.MethodsIn this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered.ResultsThree infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%.DiscussionThe findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models. |
| first_indexed | 2024-03-12T02:27:36Z |
| format | Article |
| id | doaj.art-0803f7f877af446fa2dca6313b18f076 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-03-12T02:27:36Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-0803f7f877af446fa2dca6313b18f0762023-09-05T10:52:40ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-09-011410.3389/fpls.2023.12340671234067A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseasesShweta Lamba0Vinay Kukreja1Junaid Rashid2Thippa Reddy Gadekallu3Thippa Reddy Gadekallu4Thippa Reddy Gadekallu5Thippa Reddy Gadekallu6Thippa Reddy Gadekallu7Jungeun Kim8Anupam Baliyan9Deepali Gupta10Shilpa Saini11Chandigarh Engineering College, CGC Landran, Mohali, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, IndiaDepartment of Data Science, Sejong University, Seoul, Republic of KoreaDepartment of Research and Development, Zhongda Group, Jiaxing, Zhejiang, ChinaDepartment of Electrical and Computer Engineering, Lebanese American University, Byblos, LebanonSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore, IndiaCollege of Information Science and Engineering, Jiaxing University, Jiaxing, ChinaDivision of Research and Development, Lovely Professional University, Phagwara, IndiaDepartment of Software and CMPSI, Kongju National University, Cheonan, Republic of Korea0Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, IndiaChitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India0Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab, IndiaIntroductionPaddy leaf diseases have a catastrophic influence on the quality and quantity of paddy grain production. The detection and identification of the intensity of various paddy infections are critical for high-quality crop production.MethodsIn this paper, infections in paddy leaves are considered for the identification of illness severity. The dataset contains both primary and secondary data. The four online repositories used for secondary data resources are Mendeley, GitHub, Kaggle and UCI. The size of the dataset is 4,068 images. The dataset is first pre-processed using ImageDataGenerator. Then, a generative adversarial network (GAN) is used to increase the dataset size exponentially. The disease severity calculation for the infected leaf is performed using a number of segmentation methods. To determine paddy infection, a deep learning-based hybrid approach is proposed that combines the capabilities of a convolutional neural network (CNN) and support vector machine (SVM). The severity levels are determined with the assistance of a domain expert. Four degrees of disease severity (mild, moderate, severe, and profound) are considered.ResultsThree infections are considered in the categorization of paddy leaf diseases: bacterial blight, blast, and leaf smut. The model predicted the paddy disease type and intensity with a 98.43% correctness rate. The loss rate is 41.25%.DiscussionThe findings show that the proposed method is reliable and effective for identifying the four levels of severity of bacterial blight, blast, and leaf smut infections in paddy crops. The proposed model performed better than the existing CNN and SVM classification models.https://www.frontiersin.org/articles/10.3389/fpls.2023.1234067/fullseverity detectionmulti-class classificationpaddy diseasesseverity classificationgenerative adversarial network |
| spellingShingle | Shweta Lamba Vinay Kukreja Junaid Rashid Thippa Reddy Gadekallu Thippa Reddy Gadekallu Thippa Reddy Gadekallu Thippa Reddy Gadekallu Thippa Reddy Gadekallu Jungeun Kim Anupam Baliyan Deepali Gupta Shilpa Saini A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases Frontiers in Plant Science severity detection multi-class classification paddy diseases severity classification generative adversarial network |
| title | A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases |
| title_full | A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases |
| title_fullStr | A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases |
| title_full_unstemmed | A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases |
| title_short | A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases |
| title_sort | novel fine tuned deep learning based multi class classifier for severity of paddy leaf diseases |
| topic | severity detection multi-class classification paddy diseases severity classification generative adversarial network |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1234067/full |
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