Noninvasive Phenotyping of Plant–Pathogen Interaction: Consecutive In Situ Imaging of Fluorescing Pseudomonas syringae, Plant Phenolic Fluorescence, and Chlorophyll Fluorescence in Arabidopsis Leaves

Chlorophyll fluorescence is a non-invasive measurement of photosystem II (PSII) activity and is a commonly used technique in plant physiology. The sensitivity of PSII activity to abiotic and biotic factors has made this a key technique not only for understanding the photosynthetic mechanisms but also as a broader indicator of how plants respond to environmental change. This, along with low cost and ease of collecting data, has resulted in the appearance of a large array of instrument types for measurement and calculated parameters which can be bewildering for the new user. Moreover, its accessibility can lead to misuse and misinterpretation when the underlying photosynthetic processes are not fully appreciated. This review is timely because it sits at a point of renewed interest in chlorophyll fluorescence where fast measurements of photosynthetic performance are now required for crop improvement purposes. Here we help the researcher make choices in terms of protocols using the equipment and expertise available, especially for field measurements. We start with a basic overview of the principles of fluorescence analysis and provide advice on best practice for taking pulse amplitude-modulated measurements. We also discuss a number of emerging techniques for contemporary crop and ecology research, where we see continual development and application of analytical techniques to meet the new challenges that have arisen in recent years. We end the review by briefly discussing the emerging area of monitoring fluorescence, chlorophyll fluorescence imaging, field phenotyping, and remote sensing of crops for yield and biomass enhancement.

Significance Plant stress identification based on visual symptoms has predominately remained a manual exercise performed by trained pathologists, primarily due to the occurrence of confounding symptoms. However, the manual rating process is tedious, is time-consuming, and suffers from inter- and intrarater variabilities. Our work resolves such issues via the concept of explainable deep machine learning to automate the process of plant stress identification, classification, and quantification. We construct a very accurate model that can not only deliver trained pathologist-level performance but can also explain which visual symptoms are used to make predictions. We demonstrate that our method is applicable to a large variety of biotic and abiotic stresses and is transferable to other imaging conditions and plants.

Pathogens get entry into host cell, reproduce there and use biological machinery of host plants which is threat to global crop production. Integrated management strategies based upon minimizing population and use of resistant cultivars can address this potential problem. In developing world farmers are less likely to adopt these approaches instead they prefer the use of chemical pesticides. Reckless use of chemical pesticides is destroying our ecosystem. That's why it is required to explore ecofriendly alternatives, like plant based metabolites to control pathogens. Studies conducted on different plant-metabolites reported that these metabolite can potentially combat plant pathogens. In this study we have also discussed some of plant secondary metabolites including alkaloids, flavonoids and phenolics. In this review we tried to highlight the new trends in utilizing secondary metabolites for controlling bacterial, viral and fungal pathogens with the hope that upcoming drugs will be human and ecosystem friendly.