Data-mining Techniques for Image-based Plant Phenotypic Traits Identification and Classification

Abstract

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.

Plant phenotyping relevance

植物フェノミクスデータを対象とした統計・機械学習解析フレームワークの提案と、シミュレーションおよび実データによる検証が中心であり、植物状態の推定手法に該当する。

Code and data availability

The paper's real-data analysis is based on a public quantitative barley phenomics dataset downloaded from the IAP G2P site (iapg2p.sourceforge.net/modeling/#dataset), which is a paper-specific, publicly actionable asset. The authors' R analysis code is only 'available upon request', so it qualifies as a request-only,非-