Streamlined attention for insect pest classification: leveraging FSAN.
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| Title: | Streamlined attention for insect pest classification: leveraging FSAN. |
|---|---|
| Authors: | Mansoor Hussain, D.1 (AUTHOR) mansoorhussain.d@vit.ac.in, Benazir Begum, A.2 (AUTHOR), Karthikeyan, N1 (AUTHOR) |
| Source: | Multimedia Tools & Applications. Nov2025, Vol. 84 Issue 37, p45733-45759. 27p. |
| Subjects: | Classification of insects, Food security, Machine learning, Crops, Image processing, Crop management |
| Abstract: | Pest insects pose a significant threat to agricultural crops, impacting food security and economic stability, which makes accurate pest classification essential. However, previous methods suffer from limited classification efficiency, overfitting due to large datasets, and reliance on costly agricultural experts. To address these issues, this study introduces the Fruitful Streamlined Attention Network (FSAN) for pest detection and classification. The proposed model was evaluated using the IP102 dataset, which includes 75,000 + images across 102 categories of insect pests affecting eight crop types (rice, corn, wheat, beet, alfalfa, Vitis, citrus, and mango). The dataset was split into 70% for training, 15% for testing, and 15% for validation. To enhance image quality, Luminous Bi-Histogram Equalization (LBHE) was applied in preprocessing, improving contrast and reducing noise. The FSAN architecture integrates seven Streamline Attention-MB (SA-MB) convolution layers with Swish activation, global average pooling, dense layers, and a softmax layer for classification. The FSAN achieved exceptional performance, with 98.88% accuracy, 98.99% precision, 98% recall, and an F1-score of 98.5%. Compared to other models, including DenseNet121, EfficientNet-B0, VGG19, ResNet-50, ResNeSt-50, and ResNeXt-50, FSAN demonstrated superior accuracy and efficiency. FSAN offers a robust and efficient solution for pest detection. This advancement enhances agricultural pest management, aiding in early identification and reducing crop damage. [ABSTRACT FROM AUTHOR] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 189934450 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Streamlined attention for insect pest classification: leveraging FSAN. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mansoor+Hussain%2C+D%2E%22">Mansoor Hussain, D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mansoorhussain.d@vit.ac.in</i><br /><searchLink fieldCode="AR" term="%22Benazir+Begum%2C+A%2E%22">Benazir Begum, A.</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Karthikeyan%2C+N%22">Karthikeyan, N</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Nov2025, Vol. 84 Issue 37, p45733-45759. 27p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Classification+of+insects%22">Classification of insects</searchLink><br /><searchLink fieldCode="DE" term="%22Food+security%22">Food security</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Crops%22">Crops</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Crop+management%22">Crop management</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Pest insects pose a significant threat to agricultural crops, impacting food security and economic stability, which makes accurate pest classification essential. However, previous methods suffer from limited classification efficiency, overfitting due to large datasets, and reliance on costly agricultural experts. To address these issues, this study introduces the Fruitful Streamlined Attention Network (FSAN) for pest detection and classification. The proposed model was evaluated using the IP102 dataset, which includes 75,000 + images across 102 categories of insect pests affecting eight crop types (rice, corn, wheat, beet, alfalfa, Vitis, citrus, and mango). The dataset was split into 70% for training, 15% for testing, and 15% for validation. To enhance image quality, Luminous Bi-Histogram Equalization (LBHE) was applied in preprocessing, improving contrast and reducing noise. The FSAN architecture integrates seven Streamline Attention-MB (SA-MB) convolution layers with Swish activation, global average pooling, dense layers, and a softmax layer for classification. The FSAN achieved exceptional performance, with 98.88% accuracy, 98.99% precision, 98% recall, and an F1-score of 98.5%. Compared to other models, including DenseNet121, EfficientNet-B0, VGG19, ResNet-50, ResNeSt-50, and ResNeXt-50, FSAN demonstrated superior accuracy and efficiency. FSAN offers a robust and efficient solution for pest detection. This advancement enhances agricultural pest management, aiding in early identification and reducing crop damage. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-025-20954-4 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 45733 Subjects: – SubjectFull: Classification of insects Type: general – SubjectFull: Food security Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Crops Type: general – SubjectFull: Image processing Type: general – SubjectFull: Crop management Type: general Titles: – TitleFull: Streamlined attention for insect pest classification: leveraging FSAN. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mansoor Hussain, D. – PersonEntity: Name: NameFull: Benazir Begum, A. – PersonEntity: Name: NameFull: Karthikeyan, N IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 37 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
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