Universal Image Segmentation with Arbitrary Granularity for Efficient Pest Monitoring
Guardat en:
| Publicat a: | Horticulturae vol. 11, no. 12 (2025), p. 1462-1481 |
|---|---|
| Autor principal: | |
| Altres autors: | , , , , , , |
| Publicat: |
MDPI AG
|
| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text + Graphics Full Text - PDF |
| Etiquetes: |
Sense etiquetes, Sigues el primer a etiquetar aquest registre!
|
MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3286299865 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2311-7524 | ||
| 024 | 7 | |a 10.3390/horticulturae11121462 |2 doi | |
| 035 | |a 3286299865 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a Minh, Dang L |u Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam | |
| 245 | 1 | |a Universal Image Segmentation with Arbitrary Granularity for Efficient Pest Monitoring | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Accurate and timely pest monitoring is essential for sustainable agriculture and effective crop protection. While recent deep learning-based pest recognition systems have significantly improved accuracy, they are typically trained for fixed label sets and narrowly defined tasks. In this paper, we present RefPestSeg, a universal, language-promptable segmentation model specifically designed for pest monitoring. RefPestSeg can segment targets at any semantic level, such as species, genus, life stage, or damage type, conditioned on flexible natural language instructions. The model adopts a symmetric architecture with self-attention and cross-attention mechanisms to tightly align visual features with language embeddings in a unified feature space. To further enhance performance in challenging field conditions, we integrate an optimized super-resolution module to improve image quality and employ diverse data augmentation strategies to enrich the training distribution. A lightweight postprocessing step refines segmentation masks by suppressing highly overlapping regions and removing noise blobs introduced by cluttered backgrounds. Extensive experiments on a challenging pest dataset show that RefPestSeg achieves an Intersection over Union (IoU) of 69.08 while maintaining robustness in real-world scenarios. By enabling language-guided pest segmentation, RefPestSeg advances toward more intelligent, adaptable monitoring systems that can respond to real-time agricultural demands without costly model retraining. | |
| 653 | |a Language | ||
| 653 | |a Plant protection | ||
| 653 | |a Datasets | ||
| 653 | |a Image resolution | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Attention | ||
| 653 | |a Architecture | ||
| 653 | |a Monitoring systems | ||
| 653 | |a Image processing | ||
| 653 | |a Monitoring | ||
| 653 | |a Machine learning | ||
| 653 | |a Localization | ||
| 653 | |a Deep learning | ||
| 653 | |a Natural language | ||
| 653 | |a Sustainable agriculture | ||
| 653 | |a Agriculture | ||
| 653 | |a Data augmentation | ||
| 653 | |a Pests | ||
| 653 | |a Image segmentation | ||
| 653 | |a Linguistics | ||
| 653 | |a Image quality | ||
| 653 | |a Real time | ||
| 653 | |a Natural language processing | ||
| 653 | |a Morphology | ||
| 653 | |a Semantics | ||
| 653 | |a Environmental | ||
| 700 | 1 | |a Danish Sufyan |u Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea; sufyandanish@sju.ac.kr (S.D.); muhammadfayaz@sju.ac.kr (M.F.); asmakhan28@sju.ac.kr (A.K.); arzurabani@sju.ac.kr (G.E.A.); liliatightiz@sejong.ac.kr (L.T.) | |
| 700 | 1 | |a Fayaz Muhammad |u Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea; sufyandanish@sju.ac.kr (S.D.); muhammadfayaz@sju.ac.kr (M.F.); asmakhan28@sju.ac.kr (A.K.); arzurabani@sju.ac.kr (G.E.A.); liliatightiz@sejong.ac.kr (L.T.) | |
| 700 | 1 | |a Khan, Asma |u Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea; sufyandanish@sju.ac.kr (S.D.); muhammadfayaz@sju.ac.kr (M.F.); asmakhan28@sju.ac.kr (A.K.); arzurabani@sju.ac.kr (G.E.A.); liliatightiz@sejong.ac.kr (L.T.) | |
| 700 | 1 | |a Arzu, Gul E |u Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea; sufyandanish@sju.ac.kr (S.D.); muhammadfayaz@sju.ac.kr (M.F.); asmakhan28@sju.ac.kr (A.K.); arzurabani@sju.ac.kr (G.E.A.); liliatightiz@sejong.ac.kr (L.T.) | |
| 700 | 1 | |a Tightiz Lilia |u Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea; sufyandanish@sju.ac.kr (S.D.); muhammadfayaz@sju.ac.kr (M.F.); asmakhan28@sju.ac.kr (A.K.); arzurabani@sju.ac.kr (G.E.A.); liliatightiz@sejong.ac.kr (L.T.) | |
| 700 | 1 | |a Song Hyoung-Kyu |u Department of Information and Communication Engineering and Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea; songhk@sejong.ac.kr | |
| 700 | 1 | |a Moon Hyeonjoon |u Department of Computer Science and Engineering, Sejong University, Seoul 05006, Republic of Korea; sufyandanish@sju.ac.kr (S.D.); muhammadfayaz@sju.ac.kr (M.F.); asmakhan28@sju.ac.kr (A.K.); arzurabani@sju.ac.kr (G.E.A.); liliatightiz@sejong.ac.kr (L.T.) | |
| 773 | 0 | |t Horticulturae |g vol. 11, no. 12 (2025), p. 1462-1481 | |
| 786 | 0 | |d ProQuest |t Agriculture Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3286299865/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3286299865/fulltextwithgraphics/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3286299865/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch |