SLP-Net: A Dual-Level Contrastive Learning Framework with Stripe Attention for Elongated Pepper Detection in Complex Field Environments.

Jiangquan Zeng, Jiangzhang Zhu, Guoxiong Zhou, Peng Wang

Abstract read

In one paragraph

Article in Plants (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Jiangquan ZengCollege of Computer and Mathematics, Central South University of Forestry and Technology, Changsha 410004, China.

Jiangzhang ZhuCollege of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha 410004, China.

Guoxiong ZhouCollege of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha 410004, China.ORCID 0000-0002-5142-4845

Peng WangCollege of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha 410004, China.

Funding

Department of Science and Technology of Hunan Province 2025JJ50385

6 · The paper itself

Abstract

Pepper detection in field images is difficult because the fruits can differ substantially in appearance, and many are partially covered by nearby leaves. Localization becomes less reliable when a pepper is slender or when only part of its contour is visible. SLP-Net was developed for this setting. Rather than increasing model size, it is designed to preserve shape cues that are easily weakened in cluttered field scenes. This makes the detector less sensitive to differences among pepper instances and to cases in which the visible region is incomplete. On PP-Set, SLP-Net outperforms the compared detectors, with clearer gains at higher IoU thresholds and on small targets. A similar pattern is observed on CH-Set, where disease, deformation, and stronger background interference further increase the difficulty of detection. Overall, these results indicate that SLP-Net remains more stable when pepper targets vary more strongly in geometry, surface condition, and visibility.

Indexed as

contrastive learningelongated object detectionpepper detection

Identifiers

PMID42197655

DOI10.3390/plants15101521

PMCPMC13211103

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.