ArticleFrontiers in plant science2021
A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- HCDT 2.0: A Highly Confident Drug-Target Database for Experimentally Validated Genes, RNAs, and Pathways.Scientific data · 2025Article
- Hyperspectral Imaging and Machine Learning for Huanglongbing Detection on Leaf-Symptoms.Plants (Basel, Switzerland) · 2025Article
- scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data.Life science alliance · 2023Article
- A genome-wide association study reveals that epistasis underlies the pathogenicity ofMicrobiology spectrum · 2023Article
- Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach.Plants (Basel, Switzerland) · 2023Article
- A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection.Plant phenomics (Washington, D.C.) · 2023Article
- A tea bud segmentation, detection and picking point localization based on the MDY7-3PTB model.Frontiers in plant science · 2023Article
- Deep Metric Learning-Based Strawberry Disease Detection With Unknowns.Frontiers in plant science · 2022Article
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Authors and funding
6 authors.
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Abstract
Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.
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