Comparing Machine Learning Methods for Classifying Plant Drought Stress from Leaf Reflectance Spectra in Arabidopsis thaliana

Abstract

Plant breeders and plant physiologists are deeply committed to high throughput plant phenotyping for drought tolerance. A combination of artificial intelligence with reflectance spectroscopy was tested, as a non-invasive method, for the automatic classification of plant drought stress. Arabidopsis thaliana plants (ecotype Col-0) were subjected to different levels of slowly imposed dehydration (S0, control; S1, moderate stress; S2, severe stress). The reflectance spectra of fully expanded leaves were recorded with an Ocean Optics USB4000 spectrometer and the soil water content (SWC, %) of each pot was determined. The entire data set of the reflectance spectra (intensity vs. wavelength) was given to different machine learning (ML) algorithms, namely decision trees, random forests and extreme gradient boosting. The performance of different methods in classifying the plants in one of the three drought stress classes (S0, S1 and S2) was measured and compared. All algorithms produced very high evaluation scores (F1 > 90%) and agree on the features with the highest discriminative power (reflectance at ~670 nm). Random forests was the best performing method and the most robust to random sampling of training data, with an average F1-score of 0.96 ± 0.05. This classification method is a promising tool to detect plant physiological responses to drought using high-throughput pipelines.

A Perspective on Plant Phenomics: Coupling Deep Learning and Near-Infrared Spectroscopy

“…Consistent with previous studies ( Le, 2020 ; Barradas et al, 2021 ; Mishra and Passos, 2021 ), our results show that NIRS coupled with deep learning is a powerful tool to assess phenotypic variations in plants. Using 15 functional and metabolomic traits, we show that deep learning methods outperform classical analytical techniques such as PLSR ( Supplementary Table S3 ).…”

Evaluation of Total Nitrogen in Water via Airborne Hyperspectral Data: Potential of Fractional Order Discretization Algorithm and Discrete Wavelet Transform Analysis

“…Bagging is a prototype of the parallel integrated learning method that is directly based on the selfsampling method taking randomized bootstrapping with put-back sampling [66]. This approach ensures high model performance and a statistically reliable estimation of the generalization ability of the model without the risk of overfitting [67,68]. XGBoost is extensively applied in the field of data mining thanks to its unique advantages (efficient, flexible and lightweight) [69].…”

Integration of Phenomics and Metabolomics Datasets Reveals Different Mode of Action of Biostimulants Based on Protein Hydrolysates in Lactuca sativa L. and Solanum lycopersicum L. Under Salinity

“…For that, we used a random forest classifier. Such a tool is mainly used in plant science for machine learning approaches applied in image analysis (Barradas et al, 2021;Singh et al, 2016), but it has never been used for characterizing the biostimulant mode of action. Apart from a powerful classification method, the random forest has the advantage of revealing the significance of the traits used for identifying (classifying) treatments.…”