Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture
Insects and illnesses that affect plants can have a major negative effect on both their quality and their yield. Digital image processing may be applied to diagnose plant illnesses and detect plant pests. In the field of digital image processing, recent developments have shown that more conventional...
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MDPI AG
2024-01-01
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| author | Gopalakrishnan Nagaraj Dakshinamurthy Sungeetha Mohit Tiwari Vandana Ahuja Ajit Kumar Varma Pankaj Agarwal |
| author_facet | Gopalakrishnan Nagaraj Dakshinamurthy Sungeetha Mohit Tiwari Vandana Ahuja Ajit Kumar Varma Pankaj Agarwal |
| author_sort | Gopalakrishnan Nagaraj |
| collection | DOAJ |
| description | Insects and illnesses that affect plants can have a major negative effect on both their quality and their yield. Digital image processing may be applied to diagnose plant illnesses and detect plant pests. In the field of digital image processing, recent developments have shown that more conventional methods have been eclipsed by deep learning by a wide margin. Now, researchers are concentrating their efforts on the question of how the technique of deep learning may be applied to the issue of identifying plant diseases and pests. In this paper, the difficulties that arise when diagnosing plant pathogens and pests are outlined, and the various diagnostic approaches that are currently in use are evaluated and contrasted. This article presents a summary of three perspectives, each of which is based on a different network design, in recent research on deep learning applied to the detection of plant diseases and pests. We developed a convolutional neural network (CNN)-based framework for identifying pest-borne diseases in tomato leaves using the Plant Village Dataset and the MobileNetV2 architecture. We compared the performance of our proposed MobileNetV2 model with other existing methods and demonstrated its effectiveness in pest detection. Our MobileNetV2 model achieved an impressive accuracy of 93%, outperforming some other models like GoogleNet and VGG16, which were fully trained on the pest dataset in terms of speed. |
| format | Article |
| id | doaj-art-b2f97f92dfdb44f1becdc41bc3fbe105 |
| institution | DOAJ |
| issn | 2673-4591 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
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| series | Engineering Proceedings |
| spelling | doaj-art-b2f97f92dfdb44f1becdc41bc3fbe1052025-08-20T02:42:45ZengMDPI AGEngineering Proceedings2673-45912024-01-0159120110.3390/engproc2023059201Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart AgricultureGopalakrishnan Nagaraj0Dakshinamurthy Sungeetha1Mohit Tiwari2Vandana Ahuja3Ajit Kumar Varma4Pankaj Agarwal5Department of Mechanical Engineering, Sethu Institute of Technology, Virudhunagar 626115, IndiaDepartment Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science, Chennai 602105, IndiaDepartment of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, Delhi 110063, IndiaDepartment of Computer Science and Engineering, Swami Vivekanand Institute of Engineering and Technology, Ramnagar, Banur 140601, IndiaDepartment of Pharmaceutical Sciences, Rama University, Mandhana, Kanpur 209217, IndiaSchool of Engineering and Technology, K.R. Mangalam University, Gurugram 122103, IndiaInsects and illnesses that affect plants can have a major negative effect on both their quality and their yield. Digital image processing may be applied to diagnose plant illnesses and detect plant pests. In the field of digital image processing, recent developments have shown that more conventional methods have been eclipsed by deep learning by a wide margin. Now, researchers are concentrating their efforts on the question of how the technique of deep learning may be applied to the issue of identifying plant diseases and pests. In this paper, the difficulties that arise when diagnosing plant pathogens and pests are outlined, and the various diagnostic approaches that are currently in use are evaluated and contrasted. This article presents a summary of three perspectives, each of which is based on a different network design, in recent research on deep learning applied to the detection of plant diseases and pests. We developed a convolutional neural network (CNN)-based framework for identifying pest-borne diseases in tomato leaves using the Plant Village Dataset and the MobileNetV2 architecture. We compared the performance of our proposed MobileNetV2 model with other existing methods and demonstrated its effectiveness in pest detection. Our MobileNetV2 model achieved an impressive accuracy of 93%, outperforming some other models like GoogleNet and VGG16, which were fully trained on the pest dataset in terms of speed.https://www.mdpi.com/2673-4591/59/1/201CNN (convolution neural network)mobileNetV3pests controlsmart agriculturedigital image processing |
| spellingShingle | Gopalakrishnan Nagaraj Dakshinamurthy Sungeetha Mohit Tiwari Vandana Ahuja Ajit Kumar Varma Pankaj Agarwal Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture Engineering Proceedings CNN (convolution neural network) mobileNetV3 pests control smart agriculture digital image processing |
| title | Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture |
| title_full | Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture |
| title_fullStr | Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture |
| title_full_unstemmed | Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture |
| title_short | Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture |
| title_sort | advancements in plant pests detection leveraging convolutional neural networks for smart agriculture |
| topic | CNN (convolution neural network) mobileNetV3 pests control smart agriculture digital image processing |
| url | https://www.mdpi.com/2673-4591/59/1/201 |
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