Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning
The increasing gap between the demand and productivity of maize crop is a point of concern for the food industry, and farmers. Its' susceptibility to diseases such as Turcicum Leaf Blight, and Rust is a major cause for reducing its production. Manual detection, and classification of these disea...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2022-01-01
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| Series: | Artificial Intelligence in Agriculture |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589721722000204 |
| _version_ | 1811292754668421120 |
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| author | Nidhi Kundu Geeta Rani Vijaypal Singh Dhaka Kalpit Gupta Siddaiah Chandra Nayaka Eugenio Vocaturo Ester Zumpano |
| author_facet | Nidhi Kundu Geeta Rani Vijaypal Singh Dhaka Kalpit Gupta Siddaiah Chandra Nayaka Eugenio Vocaturo Ester Zumpano |
| author_sort | Nidhi Kundu |
| collection | DOAJ |
| description | The increasing gap between the demand and productivity of maize crop is a point of concern for the food industry, and farmers. Its' susceptibility to diseases such as Turcicum Leaf Blight, and Rust is a major cause for reducing its production. Manual detection, and classification of these diseases, calculation of disease severity, and crop loss estimation is a time-consuming task. Also, it requires expertise in disease detection. Thus, there is a need to find an alternative for automatic disease detection, severity prediction, and crop loss estimation. The promising results of machine learning, and deep learning algorithms in pattern recognition, object detection, and data analysis motivate researchers to employ these techniques for disease detection, classification, and crop loss estimation in maize crop. The research works available in literature, have proven their potential in automatic disease detection using machine learning, and deep learning models. But, there is a lack none of these works a reliable and real-life labelled dataset for training these models. Also, none of the existing works focus on severity prediction, and crop loss estimation. The authors in this manuscript collect the real-life dataset labelled by plant pathologists. They propose a deep learning-based framework for pre-processing of dataset, automatic disease detection, severity prediction, and crop loss estimation. It uses the K-Means clustering algorithm for extracting the region of interest. Next, they employ the customized deep learning model ‘MaizeNet’ for disease detection, severity prediction, and crop loss estimation. The model reports the highest accuracy of 98.50%. Also, the authors perform the feature visualization using the Grad-CAM. Now, the proposed model is integrated with a web application to provide a user-friendly interface. The efficacy of the model in extracting the relevant features, a smaller number of parameters, low training time, high accuracy favors its importance as an assisting tool for plant pathology experts.The copyright for the associated web application ‘Maize-Disease-Detector’ is filed with diary number: 17006/2021-CO/SW. |
| first_indexed | 2024-04-13T04:50:09Z |
| format | Article |
| id | doaj.art-de201854748b4df7b5a14071b3b3770b |
| institution | Directory Open Access Journal |
| issn | 2589-7217 |
| language | English |
| last_indexed | 2024-04-13T04:50:09Z |
| publishDate | 2022-01-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Artificial Intelligence in Agriculture |
| spelling | doaj.art-de201854748b4df7b5a14071b3b3770b2022-12-22T03:01:42ZengKeAi Communications Co., Ltd.Artificial Intelligence in Agriculture2589-72172022-01-016276291Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learningNidhi Kundu0Geeta Rani1Vijaypal Singh Dhaka2Kalpit Gupta3Siddaiah Chandra Nayaka4Eugenio Vocaturo5Ester Zumpano6Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India.Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India.; Corresponding author.Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India.Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur, India.ICAR DOS in Biotechnology, University of Mysore Manasagangotri, Mysore 570005, IndiaDIMES (Department of Computer Engineering, Modeling, Electronics and Systems), University of Calabria, Italy; Nanotec, Italian National Research Council, 87036 Rende, CS, ItalyDIMES (Department of Computer Engineering, Modeling, Electronics and Systems), University of Calabria, Italy; Nanotec, Italian National Research Council, 87036 Rende, CS, ItalyThe increasing gap between the demand and productivity of maize crop is a point of concern for the food industry, and farmers. Its' susceptibility to diseases such as Turcicum Leaf Blight, and Rust is a major cause for reducing its production. Manual detection, and classification of these diseases, calculation of disease severity, and crop loss estimation is a time-consuming task. Also, it requires expertise in disease detection. Thus, there is a need to find an alternative for automatic disease detection, severity prediction, and crop loss estimation. The promising results of machine learning, and deep learning algorithms in pattern recognition, object detection, and data analysis motivate researchers to employ these techniques for disease detection, classification, and crop loss estimation in maize crop. The research works available in literature, have proven their potential in automatic disease detection using machine learning, and deep learning models. But, there is a lack none of these works a reliable and real-life labelled dataset for training these models. Also, none of the existing works focus on severity prediction, and crop loss estimation. The authors in this manuscript collect the real-life dataset labelled by plant pathologists. They propose a deep learning-based framework for pre-processing of dataset, automatic disease detection, severity prediction, and crop loss estimation. It uses the K-Means clustering algorithm for extracting the region of interest. Next, they employ the customized deep learning model ‘MaizeNet’ for disease detection, severity prediction, and crop loss estimation. The model reports the highest accuracy of 98.50%. Also, the authors perform the feature visualization using the Grad-CAM. Now, the proposed model is integrated with a web application to provide a user-friendly interface. The efficacy of the model in extracting the relevant features, a smaller number of parameters, low training time, high accuracy favors its importance as an assisting tool for plant pathology experts.The copyright for the associated web application ‘Maize-Disease-Detector’ is filed with diary number: 17006/2021-CO/SW.http://www.sciencedirect.com/science/article/pii/S2589721722000204Disease detectionCrop lossSeverityDeep learningMaize |
| spellingShingle | Nidhi Kundu Geeta Rani Vijaypal Singh Dhaka Kalpit Gupta Siddaiah Chandra Nayaka Eugenio Vocaturo Ester Zumpano Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning Artificial Intelligence in Agriculture Disease detection Crop loss Severity Deep learning Maize |
| title | Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning |
| title_full | Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning |
| title_fullStr | Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning |
| title_full_unstemmed | Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning |
| title_short | Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning |
| title_sort | disease detection severity prediction and crop loss estimation in maizecrop using deep learning |
| topic | Disease detection Crop loss Severity Deep learning Maize |
| url | http://www.sciencedirect.com/science/article/pii/S2589721722000204 |
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