ArticleScientific reports2019
Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Hyperspectral imaging meets 3D Gaussian Splatting: A novel approach beyond 3D plant morphology.Plant phenomics (Washington, D.C.) · 2026Article
- Classification of high-throughput phenotyping data for differentiation among nutrient deficiency in common bean.Frontiers in plant science · 2022Article
- Robust High-Throughput Phenotyping with Deep Segmentation Enabled by a Web-Based Annotator.Plant phenomics (Washington, D.C.) · 2022Article
- Review: Application of Artificial Intelligence in Phenomics.Sensors (Basel, Switzerland) · 2021Review
- A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion.Frontiers in plant science · 2021Article
- Neural Network-Based Mapping Mining of Image Style Transfer in Big Data Systems.Computational intelligence and neuroscience · 2021Article
- Interactive machine learning for soybean seed and seedling quality classification.Scientific reports · 2020Article
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Authors and funding
3 authors.
Funding
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Abstract
Statistical data-mining (DM) and machine learning (ML) are promising tools to assist in the analysis of complex dataset. In recent decades, in the precision of agricultural development, plant phenomics study is crucial for high-throughput phenotyping of local crop cultivars. Therefore, integrated or a new analytical approach is needed to deal with these phenomics data. We proposed a statistical framework for the analysis of phenomics data by integrating DM and ML methods. The most popular supervised ML methods; Linear Discriminant Analysis (LDA), Random Forest (RF), Support Vector Machine with linear (SVM-l) and radial basis (SVM-r) kernel are used for classification/prediction plant status (stress/non-stress) to validate our proposed approach. Several simulated and real plant phenotype datasets were analyzed. The results described the significant contribution of the features (selected by our proposed approach) throughout the analysis. In this study, we showed that the proposed approach removed phenotype data analysis complexity, reduced computational time of ML algorithms, and increased prediction accuracy.
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Registered trials
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