Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests
Castor (Ricinus communis L.) is an important nonedible industrial crop that produces oil, which is used in the production of medicines, lubricants, and other products. However, the quality and quantity of castor oil are critical factors that can be degraded by various insect pest attacks. The tradit...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-02-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1101943/full |
| _version_ | 1828010982332432384 |
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| author | Nitin Satinder Bal Gupta RajKumar Yadav Fatemeh Bovand Pankaj Kumar Tyagi |
| author_facet | Nitin Satinder Bal Gupta RajKumar Yadav Fatemeh Bovand Pankaj Kumar Tyagi |
| author_sort | Nitin |
| collection | DOAJ |
| description | Castor (Ricinus communis L.) is an important nonedible industrial crop that produces oil, which is used in the production of medicines, lubricants, and other products. However, the quality and quantity of castor oil are critical factors that can be degraded by various insect pest attacks. The traditional method of identifying the correct category of pests required a significant amount of time and expertise. To solve this issue, automatic insect pest detection methods combined with precision agriculture can help farmers in providing adequate support for sustainable agriculture development. For accurate predictions, the recognition system requires a sufficient amount of data from a real-world situation, which is not always available. In this regard, data augmentation is a popular technique used for data enrichment. The research conducted in this investigation established an insect pest dataset of common castor pests. This paper proposes a hybrid manipulation-based approach for data augmentation to solve the issue of the lack of a suitable dataset for effective vision-based model training. The deep convolutional neural networks VGG16, VGG19, and ResNet50 are then adopted to analyze the effects of the proposed augmentation method. The prediction results show that the proposed method addresses the challenges associated with adequate dataset size and significantly improves overall performance when compared to previous methods. |
| first_indexed | 2024-04-10T09:08:17Z |
| format | Article |
| id | doaj.art-f153f583a7f743b789a388ad0eb73618 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-10T09:08:17Z |
| publishDate | 2023-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-f153f583a7f743b789a388ad0eb736182023-02-21T05:23:51ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-02-011410.3389/fpls.2023.11019431101943Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests Nitin0Satinder Bal Gupta1RajKumar Yadav2Fatemeh Bovand3Pankaj Kumar Tyagi4Department of Computer Science and Engineering, Indira Gandhi University, Meerpur, Rewari, Haryana, IndiaDepartment of Computer Science and Engineering, Indira Gandhi University, Meerpur, Rewari, Haryana, IndiaDepartment of Computer Science and Engineering, University Institute of Engineering & Technology, Maharshi Dayanand University, Rohtak, Haryana, IndiaDepartment of Agronomy and Plant Breeding, Islamic Azad University, Arak, IranDepartment of Biotechnology, Noida Institute of Engineering and Technology, Greater Noida, IndiaCastor (Ricinus communis L.) is an important nonedible industrial crop that produces oil, which is used in the production of medicines, lubricants, and other products. However, the quality and quantity of castor oil are critical factors that can be degraded by various insect pest attacks. The traditional method of identifying the correct category of pests required a significant amount of time and expertise. To solve this issue, automatic insect pest detection methods combined with precision agriculture can help farmers in providing adequate support for sustainable agriculture development. For accurate predictions, the recognition system requires a sufficient amount of data from a real-world situation, which is not always available. In this regard, data augmentation is a popular technique used for data enrichment. The research conducted in this investigation established an insect pest dataset of common castor pests. This paper proposes a hybrid manipulation-based approach for data augmentation to solve the issue of the lack of a suitable dataset for effective vision-based model training. The deep convolutional neural networks VGG16, VGG19, and ResNet50 are then adopted to analyze the effects of the proposed augmentation method. The prediction results show that the proposed method addresses the challenges associated with adequate dataset size and significantly improves overall performance when compared to previous methods.https://www.frontiersin.org/articles/10.3389/fpls.2023.1101943/fullprecision agriculturedata augmentationmachine visiondeep learninginsect pests classificationcastor |
| spellingShingle | Nitin Satinder Bal Gupta RajKumar Yadav Fatemeh Bovand Pankaj Kumar Tyagi Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests Frontiers in Plant Science precision agriculture data augmentation machine vision deep learning insect pests classification castor |
| title | Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests |
| title_full | Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests |
| title_fullStr | Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests |
| title_full_unstemmed | Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests |
| title_short | Developing precision agriculture using data augmentation framework for automatic identification of castor insect pests |
| title_sort | developing precision agriculture using data augmentation framework for automatic identification of castor insect pests |
| topic | precision agriculture data augmentation machine vision deep learning insect pests classification castor |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1101943/full |
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