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.
Highlights
Climate change, scarcity of resources and growing world population increasingly jeopardize global food and nutritional security [1]
Scarcity of resources and growing world population increasingly jeopardize global food and nutritional security [1]. To respond to this challenge, we need to accelerate plant breeding, provide new cultivars adapted to the changing environmental conditions, and optimize agricultural practices, decreasing the ecological footprint of food production [2]
We explore the ability of these three machine learning algorithms to classify the water stress of the model plant Arabidopsis thaliana, based on leaf reflectance spectra
Summary
Scarcity of resources and growing world population increasingly jeopardize global food and nutritional security [1] To respond to this challenge, we need to accelerate plant breeding, provide new cultivars adapted to the changing environmental conditions, and optimize agricultural practices, decreasing the ecological footprint of food production [2]. The collected information (images and/or spectra) must be processed by automated high-throughput pipelines, and translated to biologically relevant information, including their water status, since it is expected to be critically affected by recurrent drought episodes [6]. This challenge has renewed the interest in the application of in vivo spectroscopic techniques in plant sciences, including reflectance spectroscopy. The use of reflectance spectroscopy to infer the constitutive and functional characteristics of plant tissues, from leaves [7] to fruits [8], is well established
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