ArticleFrontiers in plant science2025
Vertical stratification-enabled early monitoring of cotton Verticillium wilt using
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- A hybrid SE-ResNet50 deep learning framework for high-accuracy and explainable cotton leaf disease classification.BMC plant biology · 2026Article
- Article
- Wheat growth parameters prediction based on dual output Bayesian neural network using multi-modal information.Frontiers in plant science · 2026Article
- Lightweight architecture optimization of YOLOv12n for improved cotton verticillium wilt detection.Frontiers in plant science · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early monitoring of cotton Verticillium wilt (VW) is crucial for preventing significant yield losses and quality deterioration. Current hyperspectral approaches often overlook the bottom-up disease progression and the impact of leaf stratification on VW detection. To address this, vertical spectral traits were examined to improve early diagnosis. A total of 551
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Registered trials
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