ArticleSensors (Basel, Switzerland)2026
YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision.
Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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
2 citing papers in PubMed.
- Bridging the Scale Gap: A Multi-Scale Feature Enhancement Framework for UAV Aerial Image Object Detection.Sensors (Basel, Switzerland) · 2026Article
- Fusion of MLP, XGBoost, and QAT-Optimized PointNet++ for Predicting Short-Term Dendrometer-Derived Stem Dynamics: An Edge-Oriented Computational Framework.Sensors (Basel, Switzerland) · 2026Article
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Authors and funding
9 authors.
Funding
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Abstract
Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., "
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Registered trials
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