Determination of rice ( Oryza sativa L.) drought stress levels based on chlorophyll a fluorescence through independent component analysis.

Abstract

Sensing rice drought stress is crucial for agriculture, and chlorophyll a fluorescence (ChlF) is often used. However, existing techniques usually rely on defined feature points on the OJIP induction curve, which ignores the rich physiological information in the entire curve. Independent Component Analysis (ICA) can effectively preserve independent features, making it suitable for capturing drought-induced physiological changes. This study applies ICA and Support Vector Machine (SVM) to classify drought levels using the entire OJIP curve. The results show that the 20-dimensional ChlF features obtained by ICA provide superior classification performance, with Accuracy , Precision , Recall , F1 - score , and Kappa coefficient improving by 18.15%, 0.18, 0.17, 0.17, and 0.22, respectively, compared to the entire curve. This work provides a rice drought stress levels determination method and highlights the importance of applying dimension reduction methods for ChlF analysis. This work is expected to enhance stress detection using ChlF.

Plant phenotyping relevance

ChlF全曲線からICAとSVMでイネの干ばつストレス状態を推定する方法が研究の中心であり、植物の生理状態を直接評価するフェノタイピング手法に該当する。

Code and data availability

The article describes rice chlorophyll a fluorescence (OJIP) measurements and ICA/SVM analysis, but contains no data availability statement, no public dataset deposit, and no author code repository or URL. The 'Associated Data' section lists only unspecified 'Supplementary Files' with no link or content description, so

No evidence-backed public reproduction asset is currently recorded.