A Stage-Aware Cascaded Detection-Segmentation Framework for Leaf Phenotyping and Leaf Dry Biomass Estimation of Pepper Seedlings.

Han Li, Dongyuan Shi, Hui Shi, Ming Li, Ming Diao

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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

5 authors.

Han LiCollege of Agriculture, Shihezi University, Shihezi 832003, China.

Dongyuan ShiCollege of Agriculture, Shihezi University, Shihezi 832003, China.ORCID 0009-0000-0392-9497

Hui ShiResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.

Ming LiResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.ORCID 0000-0003-3098-5093

Ming DiaoCollege of Agriculture, Shihezi University, Shihezi 832003, China.ORCID 0009-0002-1926-0315

Funding

National Key Research and Development Program of China 2023YFD1401200Shihezi University QS2023012

6 · The paper itself

Abstract

Quantitative phenotyping of pepper seedlings is important for greenhouse plug tray seedling cultivation, but it remains constrained by inefficient manual monitoring, complex greenhouse backgrounds, and growth-stage-dependent discrepancies between two-dimensional image traits and actual leaf biomass. In this study, a cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping. The framework integrated Visual Dynamic Momentum YOLO (VDM-YOLO) for individual seedling localization and growth-stage recognition, Variance Guided Strip Ghost Gated UNet (VSG-UNet) for lightweight, high-resolution leaf segmentation, and a stage-aware correction model for leaf dry biomass estimation. In performance evaluation, VDM-YOLO achieved a mean average precision at an intersection over union threshold of 0.5 (mAP

Indexed as

deep learningleaf dry biomass estimationpepper seedlingsphenotypic correctionsemantic segmentation

Identifiers

PMID42357231

DOI10.3390/plants15121912

PMCPMC13306788

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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.