Hyperspectral imaging to characterize plant–plant communication in response to insect herbivory

Abstract

Background In studies of plant stress signaling, a major challenge is the lack of non-invasive methods to detect physiological plant responses and to characterize plant–plant communication over time and space. ResultsWe acquired time series of phytocompound and hyperspectral imaging data from maize plants from the following treatments: (1) individual non-infested plants, (2) individual plants experimentally subjected to herbivory by green belly stink bug (no visible symptoms of insect herbivory), (3) one plant subjected to insect herbivory and one control plant in a separate pot but inside the same cage, and (4) one plant subjected to insect herbivory and one control plant together in the same pot. Individual phytocompounds (except indole-3acetic acid) or spectral bands were not reliable indicators of neither insect herbivory nor plant–plant communication. However, using a linear discrimination classification method based on combinations of either phytocompounds or spectral bands, we found clear evidence of maize plant responses.ConclusionsWe have provided initial evidence of how hyperspectral imaging may be considered a powerful non-invasive method to increase our current understanding of both direct plant responses to biotic stressors but also to the multiple ways plant communities are able to communicate. We are unaware of any published studies, in which comprehensive phytocompound data have been shown to correlate with leaf reflectance. In addition, we are unaware of published studies, in which plant–plant communication was studied based on leaf reflectance.

Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods

“…Rapid and accurate techniques for rice disease detection are of great importance for rice growth management. For the past decades, optical characteristics of plants have widely been studied (Carter and Knapp, 2001;Altangerel et al, 2017;Ribeiro et al, 2018;Liu et al, 2019;Zahid et al, 2019). Under the stress of diseases, external features such as morphology, color, and texture are changed.…”

“…HSI integrates both visible/near-infrared spectroscopy and imaging techniques. Visible/near-infrared spectroscopy has a strong relationship with biological and physiological parameters and internal structures of plants, and it is the most widely used spectroscopic technique to monitor plant growth and plant stresses ( Knauer et al, 2017 ; Asaari et al, 2018 ; Ribeiro et al, 2018 ). MIR is a spectroscopic technique to study the fundamental vibrations and associated rotational-vibrational structure of chemical bonds ( Machado et al, 2018 ; Skolik et al, 2019 ).…”

Proximal Remote Sensing to Non-destructively Detect and Diagnose Physiological Responses by Host Insect Larvae to Parasitism

“…In addition, it seems reasonable to speculate that general physiological variables, such as, larval content of water, carbohydrates, lips, protein, could partially explain why host body reflectance changes in response to parasitism. We are only aware of a few studies, in which reflectance data were directly associated with specific constituents of the imaged objects, and all of those studies were based on proximal remote sensing of plants (Nansen et al, 2013b; Lacoste et al, 2015; Ribeiro et al, 2018). Thus, future entomological research is needed, in which physiological, histological, and biochemical analyses are combined with acquisition of proximal remote sensing data from insects under different treatment regimes, including parasitism.…”

“…Despite the steadily growing number of studies, in which proximal remote sensing technologies have been used in studies of insects, associations between body reflectance signals and underlying physiological processes are largely unknown. Although based on a study of plants, a recently published study characterized associations between leaf reflectance and phytocompounds (Ribeiro et al, 2018).…”

Optimizing band selection for spectral detection of Aphis glycines Matsumura in soybean

“…The key assumption for the use of remote sensing to quantify insect‐induced injury is that plant morpho‐physiological changes would affect plant reflectance at such a level that remote sensing instruments could detect the injury . Hyperspectral data can be useful for fine‐resolution quantification of injury by providing hundreds of available spectral bands that can be chosen from to optimize quantification of injury and to simulate and cross‐calibrate sensors . For commercial applications, however, hyperspectral data may not be readily available or may not have the spatial and radiometric resolutions required for implementing spectral‐based scouting in large fields.…”