Evaluation of water-use efficiency in foxtail millet (Setaria italica) using visible-near infrared and thermal spectral sensing techniques

Drought stress prediction and propagation using time series modeling on multimodal plant image sequences

The paper introduces two novel algorithms for predicting and propagating drought stress in plants using image sequences captured by cameras in two modalities, i.e., visible light and hyperspectral. The first algorithm, VisStressPredict, computes a time series of holistic phenotypes, e.g., height, biomass, and size, by analyzing image sequences captured by a visible light camera at discrete time intervals and then adapts dynamic time warping (DTW), a technique for measuring similarity between temporal sequences for dynamic phenotypic analysis, to predict the onset of drought stress. The second algorithm, HyperStressPropagateNet, leverages a deep neural network for temporal stress propagation using hyperspectral imagery. It uses a convolutional neural network to classify the reflectance spectra at individual pixels as either stressed or unstressed to determine the temporal propagation of stress in the plant. A very high correlation between the soil water content, and the percentage of the plant under stress as computed by HyperStressPropagateNet on a given day demonstrates its efficacy. Although VisStressPredict and HyperStressPropagateNet fundamentally differ in their goals and hence in the input image sequences and underlying approaches, the onset of stress as predicted by stress factor curves computed by VisStressPredict correlates extremely well with the day of appearance of stress pixels in the plants as computed by HyperStressPropagateNet. The two algorithms are evaluated on a dataset of image sequences of cotton plants captured in a high throughput plant phenotyping platform. The algorithms may be generalized to any plant species to study the effect of abiotic stresses on sustainable agriculture practices.

Multi-omics intervention in Setaria to dissect climate-resilient traits: Progress and prospects

Millets constitute a significant proportion of underutilized grasses and are well known for their climate resilience as well as excellent nutritional profiles. Among millets, foxtail millet (Setaria italica) and its wild relative green foxtail (S. viridis) are collectively regarded as models for studying broad-spectrum traits, including abiotic stress tolerance, C₄ photosynthesis, biofuel, and nutritional traits. Since the genome sequence release, the crop has seen an exponential increase in omics studies to dissect agronomic, nutritional, biofuel, and climate-resilience traits. These studies have provided first-hand information on the structure, organization, evolution, and expression of several genes; however, knowledge of the precise roles of such genes and their products remains elusive. Several open-access databases have also been instituted to enable advanced scientific research on these important crops. In this context, the current review enumerates the contemporary trend of research on understanding the climate resilience and other essential traits in Setaria, the knowledge gap, and how the information could be translated for the crop improvement of related millets, biofuel crops, and cereals. Also, the review provides a roadmap for studying other underutilized crop species using Setaria as a model.

Application of NIR spectroscopy and multivariate analysis for Non-destructive evaluation of apple moisture content during ultrasonic drying

Direct-contact ultrasonic drying is a novel approach to dehydrate fruits and vegetables to reduce microbial growth and post-harvest loss while preserving nutrients and the quality of the final product. Moisture content is a critical component for food behavior during drying, and its accurate evaluation in real-time is essential for food quality control. This study conveys the potential implementation of portable near-infrared spectroscopy (NIRS) combined with multivariate analysis for real-time assessment of moisture content in apple slices during direct-contact ultrasonic drying. Partial least squares regression (PLSR) and Gaussian process regression (GPR) models were developed, and their performances for different pre-treatments methods and data partitioning algorithms were evaluated with both internal cross-validation and an external dataset. Three wavelengths were selected by SPA (1359, 1517, and 1594 nm) which were then used to introduce a closed-form equation for moisture content prediction with R²_p = 0.99 and RMSEP = 3.32%. The results revealed that portable NIRS combined with multivariate analysis is quite promising for monitoring and evaluating the moisture content during ultrasonic drying.

Remote Sensing for Irrigation of Horticultural Crops

This paper reviews the literature on applications of remote sensing for monitoring soil- and crop- water status for irrigation purposes. The review is organized into two main sections: (1) sensors and platforms applied to irrigation studies and (2) remote sensing approaches for precision irrigation to estimate crop water status, evapotranspiration, infrared thermography, soil and crop characteristics methods. Recent literature reports several remote sensing (RS) approaches to monitor crop water status in the cultivated environment. Establishing the right amount of water to supply for different irrigation strategies (maximization of yield or water use efficiency (WUE)) for a large number of crops is a problem that remains unresolved. For each crop, it will be necessary to create a stronger connection between crop-water status and crop yield.

Relationship of leaf oxygen and carbon isotopic composition with transpiration efficiency in the C4 grasses Setaria viridis and Setaria italica

Leaf carbon and oxygen isotope ratios can potentially provide a time-integrated proxy for stomatal conductance (gs) and transpiration rate (E), and can be used to estimate transpiration efficiency (TE). In this study, we found significant relationships of bulk leaf carbon isotopic signature (δ13CBL) and bulk leaf oxygen enrichment above source water (Δ18OBL) with gas exchange and TE in the model C4 grasses Setaria viridis and S. italica. Leaf δ13C had strong relationships with E, gs, water use, biomass, and TE. Additionally, the consistent difference in δ13CBL between well-watered and water-limited plants suggests that δ13CBL is effective in separating C4 plants with different availability of water. Alternatively, the use of Δ18OBL as a proxy for E and TE in S. viridis and S. italica was problematic. First, the oxygen isotopic composition of source water, used to calculate leaf water enrichment (Δ18OLW), was variable with time and differed across water treatments. Second, water limitations changed leaf size and masked the relationship of Δ18OLW and Δ18OBL with E. Therefore, the data collected here suggest that δ13CBL but not Δ18OBL may be an effective proxy for TE in C4 grasses.