Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning.

Background: Autofluorescence-based imaging has the potential to non-destructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of Brassica rapa with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research. Methods: Autofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms. The benchmark dataset consisted of time-series image sequences from three Brassica rapa genotypes (CC, R500, and VT), extreme in their morphological and physiological traits captured at the high-throughput plant phenotyping facility at the University of Nebraska-Lincoln, USA. We developed a set of machine learning-based classification models to detect the percentage of stressed tissue derived from plant images and identified the best classifier. From the analysis of the autofluorescence images, two novel stress-based image phenotypes were computed to determine the temporal variation in stressed tissue under progressive drought across different genotypes, i.e., the average percentage stress and the moving average percentage stress. Results: The study demonstrated that both the computed phenotypes consistently discriminated against stressed versus non-stressed tissue, with oilseed type (R500) being less prone to drought stress relative to the other two Brassica rapa genotypes (CC and VT). Conclusion: Autofluorescence signals from the 365/400 nm excitation/emission combination were able to segregate genotypic variation during a progressive drought treatment under a controlled greenhouse environment, allowing for the exploration of other meaningful phenotypes using autofluorescence image sequences with significance in the context of plant science.

Light, rather than circadian rhythm, regulates gas exchange in ferns and lycophytes.

Circadian regulation plays a vital role in optimizing plant responses to the environment. However, while circadian regulation has been extensively studied in angiosperms, very little is known for lycophytes and ferns, leaving a gap in our understanding of the evolution of circadian rhythms across the plant kingdom. Here, we investigated circadian regulation in gas exchange through stomatal conductance and photosynthetic efficiency in a phylogenetically broad panel of 21 species of lycophytes and ferns over a 46 h period under constant light and a selected few under more natural conditions with day-night cycles. No rhythm was detected under constant light for either lycophytes or ferns, except for two semi-aquatic species of the family Marsileaceae (Marsilea azorica and Regnellidium diphyllum), which showed rhythms in stomatal conductance. Furthermore, these results indicated the presence of a light-driven stomatal control for ferns and lycophytes, with a possible passive fine-tuning through leaf water status adjustments. These findings support previous evidence for the fundamentally different regulation of gas exchange in lycophytes and ferns compared to angiosperms, and they suggest the presence of alternative stomatal regulations in Marsileaceae, an aquatic family already well known for numerous other distinctive physiological traits. Overall, our study provides evidence for heterogeneous circadian regulation across plant lineages, highlighting the importance of broad taxonomic scope in comparative plant physiology studies.

AI-assisted image analysis and physiological validation for progressive drought detection in a diverse panel of Gossypium hirsutum L.

Introduction: Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras. Methods: In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions. Results: Two different classifiers were trained to predict the plant treatment-random forest and multilayer perceptron neural networks-finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyll a fluorescence, to provide more insights and possible explanations about the classification outputs. Discussion: Interestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.

Temporal network analysis identifies early physiological and transcriptomic indicators of mild drought in Brassica rapa.

The dynamics of local climates make development of agricultural strategies challenging. Yield improvement has progressed slowly, especially in drought-prone regions where annual crop production suffers from episodic aridity. Underlying drought responses are circadian and diel control of gene expression that regulate daily variations in metabolic and physiological pathways. To identify transcriptomic changes that occur in the crop Brassica rapa during initial perception of drought, we applied a co-expression network approach to associate rhythmic gene expression changes with physiological responses. Coupled analysis of transcriptome and physiological parameters over a two-day time course in control and drought-stressed plants provided temporal resolution necessary for correlation of network modules with dynamic changes in stomatal conductance, photosynthetic rate, and photosystem II efficiency. This approach enabled the identification of drought-responsive genes based on their differential rhythmic expression profiles in well-watered versus droughted networks and provided new insights into the dynamic physiological changes that occur during drought.

NMR (¹H) analysis of crude extracts detects light stress in Beta vulgaris and Spinacia oleracea leaves.

In highlight stress conditions, the mechanism of non-photochemical quenching (NPQ) of chlorophyll fluorescence is triggered at the chloroplast level. This process allows thermal quenching of the excessive excitation energy and it is strictly related to the efficiency of the xanthophyll cycle. Nowadays, the utilization of the nuclear magnetic resonance (NMR) spectroscopy provides a powerful complementary way for the identification and quantitative analysis of plant metabolites either in vivo or in tissue extracts. Seeing that the oxidative damage caused by light stress in plants and the consequent involvement of pigments are widely studied, NMR spectroscopy can be utilized to compare crude leaf extract at different levels of light stress, allowing an analysis of these compounds. In this paper, the identification of possible relationships between light stress and ¹H NMR signal variations is discussed. The analysis of the ¹H NMR (1D) spectra of two agronomic species (Spinacia oleracea and Beta vulgaris) exposed to different light intensities is presented. In particular, change in carotenoids and xanthophylls signals are analyzed.