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.

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.

Could energy equilibrium and greenhouse gas emissions in agroecosystems play a key role in crop replacement? A case study in orange and kiwi orchards.

The development of agriculture is linked to energy resources. Consequently, energy analysis in agroecosystems could be a useful tool for monitoring some measures in the agricultural sector to mitigate greenhouse gas emissions. The objectives of this study were to (a) evaluate differences of energy indices in orange and kiwi orchards, and (b) point out whether inputs, outputs, efficiency, productivity, and carbon footprint can play a key role in crop replacement. Proportional stratified random sampling was used to select 26 orchards (10 oranges, 16 kiwis) from the Prefecture of Arta, western Greece, during 2015 and 2016. Univariate statistical methods were combined with multivariate ones. Nitrogen, Mg, Zn, herbicides, insecticides, fungicides, renewable energy inputs, fruit production, total outputs, and energy efficiency and productivity were statistically significantly high in the orange orchards. Phosphorus, Ca, irrigation, machinery, total inputs, intensity, non-renewable energy consumption, and carbon footprint were statistically significantly high in the kiwi orchards. The most important energy inputs for both fruit crops were fertilizers, fuels, irrigation, machinery, and herbicides. The orange orchards seem to be more friendly to the environment than the kiwi orchards by having low total energy inputs 32,210.3 MJ ha-1, intensity 1.4, consumption of non-renewable energy 0.7 MJ kg-1 and CO2 equivalent/fruit production 0.08 kg kg-1, and high energy outputs 105,120.0 MJ ha-1 and fruit production 53,648.0 kg ha-1. The findings of the present study show a relation between climate change and the production of farming systems, which can be a tool for decision makers. The correlation of the abovementioned parameters ensure higher profits and could help in achieving the best possible sustainable management of the agricultural ecosystems.