Real-Time Sugarcane Bud Detection Using Efficient Lightweight Convolutional Blocks.
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| Title: | Real-Time Sugarcane Bud Detection Using Efficient Lightweight Convolutional Blocks. |
|---|---|
| Authors: | Kumar, Pushpendra1,2 (AUTHOR) pushpendra22csd010@ncuindia.edu, Arora, Shraddha1 (AUTHOR) chaudhary.shraddha18@gmail.com, Arora, Shaveta1 (AUTHOR) shavetaarora@ncuindia.edu |
| Source: | Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). May2026, Vol. 51 Issue 9, p12047-12071. 25p. |
| Subjects: | Convolutional neural networks, Object recognition (Computer vision), Precision farming, Detection algorithms, Machine learning |
| Abstract: | This article addresses limitations in YOLO-based sugarcane bud detection by balancing model size, speed, and robustness in complex agricultural settings. This article introduces EfficientConv, a lightweight, drop-in convolutional block that is 9% less computationally expensive than standard Conv layers used in YOLO models. By combining Ghost and Shuffle Convolutions with Squeeze-and-Excitation and channel-wise attention, it reduces computation without compromising accuracy. EfficientConv is version-independent and integrates seamlessly into the YOLO architecture. We evaluate YOLOv8n to YOLOv12n models with and without EfficientConv on a proprietary sugarcane bud dataset and test generalization on the VisDrone benchmark. Real-time performance is benchmarked on edge devices such as Jetson-Nano and Raspberry Pi-4B. The performance gains varied with the number of Conv blocks in each YOLO variant. Since YOLOv8n, YOLOv11n, and YOLOv12n have a large number of Conv blocks (six Conv blocks in each variant) located at different stages, they benefited the most from EfficientConv integration, making both the detection and the computing efficacy significantly improved. On the contrary, YOLOv9n and YOLOv10n have only one and two Conv blocks, respectively, and exhibit comparatively modest improvements. YOLOv8n with EfficientConv achieved the best results: 73.5% mAP@50, 49.8% mAP@95, and a 12.4% GFLOPs reduction. YOLOv11n and YOLOv12n also used 9.1% and 7.6% fewer GFLOPs while maintaining or improving accuracy. This article advances precision agriculture by introducing a fast, reliable, and efficient sugarcane bud detection. The proposed EfficientConv block reduces GFLOPs and model size while preserving or enhancing accuracy, supporting automation in sugarcane farming. The source code is available at: source [ABSTRACT FROM AUTHOR] |
| Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) 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: 193807419 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Real-Time Sugarcane Bud Detection Using Efficient Lightweight Convolutional Blocks. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kumar%2C+Pushpendra%22">Kumar, Pushpendra</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> pushpendra22csd010@ncuindia.edu</i><br /><searchLink fieldCode="AR" term="%22Arora%2C+Shraddha%22">Arora, Shraddha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> chaudhary.shraddha18@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Arora%2C+Shaveta%22">Arora, Shaveta</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> shavetaarora@ncuindia.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Arabian+Journal+for+Science+%26+Engineering+%28Springer+Science+%26+Business+Media+B%2EV%2E+%29%22">Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. )</searchLink>. May2026, Vol. 51 Issue 9, p12047-12071. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Object+recognition+%28Computer+vision%29%22">Object recognition (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Precision+farming%22">Precision farming</searchLink><br /><searchLink fieldCode="DE" term="%22Detection+algorithms%22">Detection algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This article addresses limitations in YOLO-based sugarcane bud detection by balancing model size, speed, and robustness in complex agricultural settings. This article introduces EfficientConv, a lightweight, drop-in convolutional block that is 9% less computationally expensive than standard Conv layers used in YOLO models. By combining Ghost and Shuffle Convolutions with Squeeze-and-Excitation and channel-wise attention, it reduces computation without compromising accuracy. EfficientConv is version-independent and integrates seamlessly into the YOLO architecture. We evaluate YOLOv8n to YOLOv12n models with and without EfficientConv on a proprietary sugarcane bud dataset and test generalization on the VisDrone benchmark. Real-time performance is benchmarked on edge devices such as Jetson-Nano and Raspberry Pi-4B. The performance gains varied with the number of Conv blocks in each YOLO variant. Since YOLOv8n, YOLOv11n, and YOLOv12n have a large number of Conv blocks (six Conv blocks in each variant) located at different stages, they benefited the most from EfficientConv integration, making both the detection and the computing efficacy significantly improved. On the contrary, YOLOv9n and YOLOv10n have only one and two Conv blocks, respectively, and exhibit comparatively modest improvements. YOLOv8n with EfficientConv achieved the best results: 73.5% mAP@50, 49.8% mAP@95, and a 12.4% GFLOPs reduction. YOLOv11n and YOLOv12n also used 9.1% and 7.6% fewer GFLOPs while maintaining or improving accuracy. This article advances precision agriculture by introducing a fast, reliable, and efficient sugarcane bud detection. The proposed EfficientConv block reduces GFLOPs and model size while preserving or enhancing accuracy, supporting automation in sugarcane farming. The source code is available at: source [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) 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/s13369-025-10906-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 12047 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Object recognition (Computer vision) Type: general – SubjectFull: Precision farming Type: general – SubjectFull: Detection algorithms Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Real-Time Sugarcane Bud Detection Using Efficient Lightweight Convolutional Blocks. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kumar, Pushpendra – PersonEntity: Name: NameFull: Arora, Shraddha – PersonEntity: Name: NameFull: Arora, Shaveta IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 2193567X Numbering: – Type: volume Value: 51 – Type: issue Value: 9 Titles: – TitleFull: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) Type: main |
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