Integrating Spectroscopy with Potato Disease Management

Non-Destructive Monitoring of Foliar Fungicide Efficacy with Hyperspectral Sensing in Grapevine

Frequent fungicide applications are required to manage grapevine powdery mildew (Erysiphe necator). However, this practice is costly and has led to widespread fungicide resistance. A method of monitoring in-field fungicide efficacy could help growers maximize spray-interval length, thereby reducing costs and the rate of fungicide resistance emergence. The goal of this study was to evaluate if hyperspectral sensing in the visible to shortwave infrared range (400 to 2,400 nm) can quantify foliar fungicide efficacy on grape leaves. Commercial formulations of metrafenone, Bacillus mycoides isolate J (BmJ), and sulfur were applied on Chardonnay grapevines in vineyard or greenhouse settings. Foliar reflectance was measured with handheld hyperspectral spectroradiometers at multiple days post-application. Fungicide efficacy was estimated as a proxy for fungicide residue and disease control measured with the Blackbird microscopy imaging robot. Treatments could be differentiated from the untreated control with an accuracy of 73.06% for metrafenone, 67.76% for BmJ, and 94.10% for sulfur. The change in spectral reflectance was moderately correlated with the cube root of the area under the disease progress curve for metrafenone- and sulfur-treated samples (R2 = 0.38 and 0.36, respectively) and with sulfur residue (R2 = 0.42). BmJ treatment impacted foliar physiology by enhancing the leaf mass/area and reducing the nitrogen and total phenolic content as estimated from spectral reflectance. The results suggest that hyperspectral sensing can be used to monitor in-situ fungicide efficacy, and the prediction accuracy depends on the fungicide and the time point measured. The ability to monitor in-situ fungicide efficacy could facilitate more strategic fungicide applications and promote sustainable grapevine protection. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Effects of Nighttime Applications of Germicidal Ultraviolet Light Upon Powdery Mildew (Erysiphe necator), Downy Mildew (Plasmopara viticola), and Sour Rot of Grapevine

Nighttime applications of germicidal ultraviolet were evaluated as a means to suppress three diseases of grapevine. In laboratory studies, UV-C light (peak 254 nm, FWHM 5 nm) applied during darkness strongly inhibited the germination of conidia of Erysiphe necator, and at a dose of 200 J/m², germination was zero. Reciprocity of irradiance and duration of exposure with respect to conidial germination was confirmed for UV-C doses between 0 and 200 J/m² applied at 4 or 400 s. When detached grapevine leaves were exposed during darkness to UV-C at 100 J/m² up to 7 days before they were inoculated with zoospores of Plasmopara viticola, infection and subsequent sporulation was reduced by over 70% compared to untreated control leaves, indicating an indirect suppression of the pathogen exerted through the host. A hemicylindrical array of low-pressure discharge UV-C lamps configured for trellised grapevines was designed and fitted to both a tractor-drawn carriage and a fully autonomous robotic carriage for vineyard applications. In 2019, in a Chardonnay research vineyard with a history of high inoculum and severe disease, weekly nighttime applications of UV-C suppressed E. necator on leaves and fruit at doses of 100 and 200 J/m². In the same vineyard in 2020, UV-C was applied once or twice weekly at doses of 70, 100, or 200 J/m², and severity of E. necator on both leaves and fruit was significantly reduced compared to untreated controls; twice-weekly applications at 200 J/m² provided suppression equivalent to a standard fungicide program. None of the foregoing UV-C treatments significantly reduced the severity of P. viticola on Chardonnay vines compared to the untreated control in 2020. However, twice-weekly applications of UV-C at 200 J/m² to the more downy mildew-resistant Vitis interspecific hybrid cultivar Vignoles in 2021 significantly suppressed foliar disease severity. In commercial Chardonnay vineyards with histories of excellent disease control in Dresden, NY, E. necator remained at trace levels on foliage and was zero on fruit following weekly nighttime applications of UV-C at 200 J/m² in 2020 and after weekly or twice-weekly application of UV-C at 100 or 200 J/m² in 2021. In 2019, weekly nighttime applications of UV-C at 200 J/m² also significantly reduced the severity of sour rot, a decay syndrome of complex etiology, on fruit of 'Vignoles' but not the severity of bunch rot caused by Botrytis cinerea. A similar level of suppression of sour rot was observed on 'Vignoles' vines treated twice-weekly with UV-C at 200 J/m² in 2021. Nighttime UV-C applications did not produce detectable indications of metabolic abnormalities, phytotoxicity, growth reduction, or reductions of fruit yield or quality parameters, even at the highest doses and most frequent intervals employed.

Close-Range Thermography and Reflectance Spectroscopy Support In Vitro and In Vivo Characterization of Colletotrichum spp. Isolates from Mango Fruits

Colletotrichum causing anthracnose in mango is known for its variable virulence that may have an effect on disease development and efficacy of management strategies. In this study, we characterized Colletotrichum spp. isolated from mango fruits under in vitro and in vivo conditions using close-range thermography and reflectance spectroscopy. Twenty-six isolates were phylogenetically characterized to ascertain species using the internal transcribed spacer sequence. Virulence, spectral (in vivo and in vitro), and thermographic responses (in vivo) of these isolates were analyzed. Isolates were grouped into the Colletotrichum gloeosporioides species complex and classified into eight morphotypes. Mycelial growth, conidia production, sporulation abundance, and area under disease progress curve (AUDPC) varied largely among isolates. Disease symptoms were observed 4 days after inoculation (dai), and, for most morphotypes, changes in tissue temperature were registered at 11 dai, with the greatest decrease at 14 dai with pathogen sporulation. In vitro and in vivo morphotypes shared changes in the spectrum range, and main variations were found in the number of informative spectral bands. In vivo average gross reflectance was higher in disease-inoculated tissue than in healthy uninoculated tissue. Morphotype responses varied depending on AUDPC values and postinoculation time. Discriminant analysis of the spectral response using principal component analysis and partial least squares regression explained 94 to 96.3 and 98 to 99.9% of the variance from in vitro and in vivo tests, respectively. Spectral markers were obtained for four distinct morphotype groups. We found three (550 to 650, 650.1 to 790, and 1,300 to 1,400 nm) and two (520 to 830 and 1,100 to 1,450 nm) regions with highly (P < 0.05) discriminant spectral bands for diseased fruits and morphotype characterization.

A review of remote sensing for potato traits characterization in precision agriculture

Potato is one of the most significant food crops globally due to its essential role in the human diet. The growing demand for potato, coupled with severe environmental losses caused by extensive farming activities, implies the need for better crop protection and management practices. Precision agriculture is being well recognized as the solution as it deals with the management of spatial and temporal variability to improve agricultural returns and reduce environmental impact. As the initial step in precision agriculture, the traditional methods of crop and field characterization require a large input in labor, time, and cost. Recent developments in remote sensing technologies have facilitated the process of monitoring crops and quantifying field variations. Successful applications have been witnessed in the area of precision potato farming. Thus, this review reports the current knowledge on the applications of remote sensing technologies in precision potato trait characterization. We reviewed the commonly used imaging sensors and remote sensing platforms with the comparisons of their strengths and limitations and summarized the main applications of the remote sensing technologies in potato. As a result, this review could update potato agronomists and farmers with the latest approaches and research outcomes, as well as provide a selective list for those who have the intentions to apply remote sensing technologies to characterize potato traits for precision agriculture.

Experimental data manipulations to assess performance of hyperspectral classification models of crop seeds and other objects

BACKGROUND

Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge.

METHODS

As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0-10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)].

RESULTS

For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2).

CONCLUSION

We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.

Digital plant pathology: a foundation and guide to modern agriculture

Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications are complex and differ in their study design requirements. Selecting a sensor type (e.g., thermography or hyperspectral imaging), sensor platform (e.g., rovers, unmanned aerial vehicles, or satellites), and the problem-specific spatial and temporal scale adds to the challenge as all pathosystems are unique and differ in their interactions and symptoms, or lack thereof. Adding host-pathogen-environment interactions across time and space increases the complexity even further. Large data sets are necessary to enable a deeper understanding of these interactions. Therefore, modern machine learning methods are developed to realize the fast data analysis of such complex data sets. This reduces not only human effort but also enables an objective data perusal. Especially deep learning approaches show a high potential to identify probable cohesive parameters during plant-pathogen-environment interactions. Unfortunately, the performance and reliability of developed methods are often doubted by the potential user. Gaining their trust is thus needed for real field applications. Linking biological causes to machine learning features and a clear communication, even for non-experts of such results, is a crucial task that will bridge the gap between theory and praxis of a newly developed application. Therefore, we suggest a global connection of experts and data as the basis for defining a common and goal-oriented research roadmap. Such high interconnectivity will likely increase the chances of swift, successful progress in research and practice. A coordination within international excellence clusters will be useful to reduce redundancy of research while supporting the creation and progress of complementary research. With this review, we would like to discuss past research, achievements, as well as recurring and new challenges. Having such a retrospect available, we will attempt to reveal future challenges and provide a possible direction elevating the next decade of research in digital plant pathology.

Making Sense of Light: The Use of Optical Spectroscopy Techniques in Plant Sciences and Agriculture

As a result of the development of non-invasive optical spectroscopy, the number of prospective technologies of plant monitoring is growing. Being implemented in devices with different functions and hardware, these technologies are increasingly using the most advanced data processing algorithms, including machine learning and more available computing power each time. Optical spectroscopy is widely used to evaluate plant tissues, diagnose crops, and study the response of plants to biotic and abiotic stress. Spectral methods can also assist in remote and non-invasive assessment of the physiology of photosynthetic biofilms and the impact of plant species on biodiversity and ecosystem stability. The emergence of high-throughput technologies for plant phenotyping and the accompanying need for methods for rapid and non-contact assessment of plant productivity has generated renewed interest in the application of optical spectroscopy in fundamental plant sciences and agriculture. In this perspective paper, starting with a brief overview of the scientific and technological backgrounds of optical spectroscopy and current mainstream techniques and applications, we foresee the future development of this family of optical spectroscopic methodologies.

Plant Disease Sensing: Studying Plant-Pathogen Interactions at Scale

Plant disease threatens the environmental and financial sustainability of crop production, causing $220 billion in annual losses. The dire threat disease poses to modern agriculture demands tools for better detection and monitoring to prevent crop loss and input waste. The nascent discipline of plant disease sensing, or the science of using proximal and/or remote sensing to detect and diagnose disease, offers great promise to extend monitoring to previously unachievable resolutions, a basis to construct multiscale surveillance networks for early warning, alert, and response at low latency, an opportunity to mitigate loss while optimizing protection, and a dynamic new dimension to agricultural systems biology. Despite its revolutionary potential, plant disease sensing remains an underdeveloped discipline, with challenges facing both fundamental study and field application. This article offers a perspective on the current state and future of plant disease sensing, highlights remaining gaps to be filled, and presents a bold vision for the future of global agriculture.

Range-wide variations in common milkweed traits and their effect on monarch larvae

PREMISE

Leaf economic spectrum (LES) theory has historically been employed to inform vegetation models of ecosystem processes, but largely neglects intraspecific variation and biotic interactions. We attempt to integrate across environment-plant trait-herbivore interactions within a species at a range-wide scale.

METHODS

We measured traits in 53 populations spanning the range of common milkweed (Asclepias syriaca) and used a common garden to determine the role of environment in driving patterns of intraspecific variation. We used a feeding trial to determine the role of plant traits in monarch (Danaus plexippus) larval development.

RESULTS

Trait-trait relationships largely followed interspecific patterns in LES theory and persisted in a common garden when individual traits change. Common milkweed showed intraspecific variation and biogeographic clines in traits. Clines did not persist in a common garden. Larvae ate more and grew larger when fed plants with more nitrogen. A longitudinal environmental gradient in precipitation corresponded to a resource gradient in plant nitrogen, which produces a gradient in larval performance.

CONCLUSIONS

Biogeographic patterns in common milkweed traits can sometimes be predicted from LES, are largely driven by environmental conditions, and have consequences for monarch larval performance. Changes to nutrient dynamics of landscapes with common milkweed could potentially influence monarch population dynamics. We show how biogeographic trends in intraspecific variation can influence key ecological interactions, especially in common species with large distributions.

Machine Learning-Based Presymptomatic Detection of Rice Sheath Blight Using Spectral Profiles

Early detection of plant diseases, prior to symptom development, can allow for targeted and more proactive disease management. The objective of this study was to evaluate the use of near-infrared (NIR) spectroscopy combined with machine learning for early detection of rice sheath blight (ShB), caused by the fungus Rhizoctonia solani. We collected NIR spectra from leaves of ShB-susceptible rice (Oryza sativa L.) cultivar, Lemont, growing in a growth chamber one day following inoculation with R. solani, and prior to the development of any disease symptoms. Support vector machine (SVM) and random forest, two machine learning algorithms, were used to build and evaluate the accuracy of supervised classification-based disease predictive models. Sparse partial least squares discriminant analysis was used to confirm the results. The most accurate model comparing mock-inoculated and inoculated plants was SVM-based and had an overall testing accuracy of 86.1% (N = 72), while when control, mock-inoculated, and inoculated plants were compared the most accurate SVM model had an overall testing accuracy of 73.3% (N = 105). These results suggest that machine learning models could be developed into tools to diagnose infected but asymptomatic plants based on spectral profiles at the early stages of disease development. While testing and validation in field trials are still needed, this technique holds promise for application in the field for disease diagnosis and management.

A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades

The detection, quantification, diagnosis, and identification of plant diseases is particularly crucial for precision agriculture. Recently, traditional visual assessment technology has not been able to meet the needs of precision agricultural informatization development, and hyperspectral technology, as a typical type of non-invasive technology, has received increasing attention. On the basis of simply describing the types of pathogens and host–pathogen interaction processes, this review expounds the great advantages of hyperspectral technologies in plant disease detection. Then, in the process of describing the hyperspectral disease analysis steps, the articles, algorithms, and methods from disease detection to qualitative and quantitative evaluation are mainly summarizing. Additionally, according to the discussion of the current major problems in plant disease detection with hyperspectral technologies, we propose that different pathogens’ identification, biotic and abiotic stresses discrimination, plant disease early warning, and satellite-based hyperspectral technology are the primary challenges and pave the way for a targeted response.

A multi-omics approach to solving problems in plant disease ecology

The swift rise of omics-approaches allows for investigating microbial diversity and plant-microbe interactions across diverse ecological communities and spatio-temporal scales. The environment, however, is rapidly changing. The introduction of invasive species and the effects of climate change have particular impact on emerging plant diseases and managing current epidemics. It is critical, therefore, to take a holistic approach to understand how and why pathogenesis occurs in order to effectively manage for diseases given the synergies of changing environmental conditions. A multi-omics approach allows for a detailed picture of plant-microbial interactions and can ultimately allow us to build predictive models for how microbes and plants will respond to stress under environmental change. This article is designed as a primer for those interested in integrating -omic approaches into their plant disease research. We review -omics technologies salient to pathology including metabolomics, genomics, metagenomics, volatilomics, and spectranomics, and present cases where multi-omics have been successfully used for plant disease ecology. We then discuss additional limitations and pitfalls to be wary of prior to conducting an integrated research project as well as provide information about promising future directions.

Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning

Understanding plant disease resistance is important in the integrated management of Phytophthora infestans, causal agent of potato late blight. Advanced field-based methods of disease detection that can identify infection before the onset of visual symptoms would improve management by greatly reducing disease potential and spread as well as improve both the financial and environmental sustainability of potato farms. In-vivo foliar spectroscopy offers the capacity to rapidly and non-destructively characterize plant physiological status, which can be used to detect the effects of necrotizing pathogens on plant condition prior to the appearance of visual symptoms. Here, we tested differences in spectral response of four potato cultivars, including two cultivars with a shared genotypic background except for a single copy of a resistance gene, to inoculation with Phytophthora infestans clonal lineage US-23 using three statistical approaches: random forest discrimination (RF), partial least squares discrimination analysis (PLS-DA), and normalized difference spectral index (NDSI). We find that cultivar, or plant genotype, has a significant impact on spectral reflectance of plants undergoing P. infestans infection. The spectral response of four potato cultivars to infection by Phytophthora infestans clonal lineage US-23 was highly variable, yet with important shared characteristics that facilitated discrimination. Early disease physiology was found to be variable across cultivars as well using non-destructively derived PLS-regression trait models. This work lays the foundation to better understand host-pathogen interactions across a variety of genotypic backgrounds, and establishes that host genotype has a significant impact on spectral reflectance, and hence on biochemical and physiological traits, of plants undergoing pathogen infection.

Contact Reflectance Spectroscopy for Rapid, Accurate, and Nondestructive Phytophthora infestans Clonal Lineage Discrimination

Populations of Phytophthora infestans, the oomycete causal agent of potato late blight in the United States, are predominantly asexual, and isolates are characterized by clonal lineage or asexual descendants of a single genotype. Current tools for clonal lineage identification are time consuming and require laboratory equipment. We previously found that foliar spectroscopy can be used for high-accuracy pre- and postsymptomatic detection of P. infestans infections caused by clonal lineages US-08 and US-23. In this work, we found subtle but distinct differences in spectral responses of potato foliage infected by these clonal lineages in both growth-chamber time-course experiments (12- to 24-h intervals over 5 days) and naturally infected samples from commercial production fields. In both settings, we measured continuous visible to shortwave infrared reflectance (400 to 2,500 nm) on leaves using a portable spectrometer with contact probe. We consistently discriminated between infections caused by the two clonal lineages across all stages of disease progression using partial least squares (PLS) discriminant analysis, with total accuracies ranging from 88 to 98%. Three-class random forest differentiation between control, US-08, and US-23 yielded total discrimination accuracy ranging from 68 to 76%. Differences were greatest during presymptomatic infection stages and progressed toward uniformity as symptoms advanced. Using PLS-regression trait models, we found that total phenolics, sugar, and leaf mass per area were different between lineages. Shortwave infrared wavelengths (>1,100 nm) were important for clonal lineage differentiation. This work provides a foundation for future use of hyperspectral sensing as a nondestructive tool for pathovar differentiation.

Temporal Dynamics of Wheat Blast Epidemics and Disease Measurements Using Multispectral Imagery

Wheat blast is a devastating disease caused by the Triticum pathotype of Magnaporthe oryzae. M. oryzae Triticum is capable of infecting leaves and spikes of wheat. Although symptoms of wheat spike blast (W_SB) are quite distinct in the field, symptoms on leaves (W_LB) are rarely reported because they are usually inconspicuos. Two field experiments were conducted in Bolivia to characterize the change in W_LB and W_SB intensity over time and determine whether multispectral imagery can be used to accurately assess W_SB. Disease progress curves (DPCs) were plotted from W_LB and W_SB data, and regression models were fitted to describe the nature of W_SB epidemics. W_LB incidence and severity changed over time; however, the mean W_LB severity was inconspicuous before wheat began spike emergence. Overall, both Gompertz and logistic models helped to describe W_SB intensity DPCs fitting classic sigmoidal shape curves. Lin's concordance correlation coefficients were estimated to measure agreement between visual estimates and digital measurements of W_SB intensity and to estimate accuracy and precision. Our findings suggest that the change of wheat blast intensity in a susceptible host population over time does not follow a pattern of a monocyclic epidemic. We have also demonstrated that W_SB severity can be quantified using a digital approach based on nongreen pixels. Quantification was precise (0.96 < r> 0.83) and accurate (0.92 < ρ > 0.69) at moderately low to high visual W_SB severity levels. Additional sensor-based methods must be explored to determine their potential for detection of W_LB and W_SB at earlier stages.

Hyperspectral Measurements Enable Pre-Symptomatic Detection and Differentiation of Contrasting Physiological Effects of Late Blight and Early Blight in Potato

In-vivo foliar spectroscopy, also known as contact hyperspectral reflectance, enables rapid and non-destructive characterization of plant physiological status. This can be used to assess pathogen impact on plant condition both prior to and after visual symptoms appear. Challenging this capacity is the fact that dead tissue yields relatively consistent changes in leaf optical properties, negatively impacting our ability to distinguish causal pathogen identity. Here, we used in-situ spectroscopy to detect and differentiate Phytophthora infestans (late blight) and Alternaria solani (early blight) on potato foliage over the course of disease development and explored non-destructive characterization of contrasting disease physiology. Phytophthora infestans, a hemibiotrophic pathogen, undergoes an obligate latent period of two–seven days before disease symptoms appear. In contrast, A. solani, a necrotrophic pathogen, causes symptoms to appear almost immediately when environmental conditions are conducive. We found that respective patterns of spectral change can be related to these differences in underlying disease physiology and their contrasting pathogen lifestyles. Hyperspectral measurements could distinguish both P. infestans-infected and A. solani-infected plants with greater than 80% accuracy two–four days before visible symptoms appeared. Individual disease development stages for each pathogen could be differentiated from respective controls with 89–95% accuracy. Notably, we could distinguish latent P. infestans infection from both latent and symptomatic A. solani infection with greater than 75% accuracy. Spectral features important for late blight detection shifted over the course of infection, whereas spectral features important for early blight detection remained consistent, reflecting their different respective pathogen biologies. Shortwave infrared wavelengths were important for differentiation between healthy and diseased, and between pathogen infections, both pre- and post-symptomatically. This proof-of-concept work supports the use of spectroscopic systems as precision agriculture tools for rapid and early disease detection and differentiation tools, and highlights the importance of careful consideration of underlying pathogen biology and disease physiology for crop disease remote sensing.

Early Detection of Sage (Salvia officinalis L.) Responses to Ozone Using Reflectance Spectroscopy

Advancements in techniques to rapidly and non-destructively detect the impact of tropospheric ozone (O3) on crops are required. This study demonstrates the capability of full-range (350-2500 nm) reflectance spectroscopy to characterize responses of asymptomatic sage leaves under an acute O3 exposure (200 ppb for 5 h). Using partial least squares regression, spectral models were developed for the estimation of several traits related to photosynthesis, the oxidative pressure induced by O3, and the antioxidant mechanisms adopted by plants to cope with the pollutant. Physiological traits were well predicted by spectroscopic models (average model goodness-of-fit for validation (R2): 0.65-0.90), whereas lower prediction performances were found for biochemical traits (R2: 0.42-0.71). Furthermore, even in the absence of visible symptoms, comparing the full-range spectral profiles, it was possible to distinguish with accuracy plants exposed to charcoal-filtered air from those exposed to O3. An O3 effect on sage spectra was detectable from 1 to 5 h from the beginning of the exposure, but ozonated plants quickly recovered after the fumigation. This O3-tolerance was confirmed by trends of vegetation indices and leaf traits derived from spectra, further highlighting the capability of reflectance spectroscopy to early detect the responses of crops to O3.

Spectral characterization of wheat functional trait responses to Hessian fly: Mechanisms for trait-based resistance

Insect herbivores can manipulate host plants to inhibit defenses. Insects that induce plant galls are excellent examples of these interactions. The Hessian fly (HF, Mayetiola destructor) is a destructive pest of wheat (Triticum spp.) that occurs in nearly all wheat producing globally. Under compatible interactions (i.e., successful HF establishment), HF larvae alter host tissue physiology and morphology for their benefit, manifesting as the development of plant nutritive tissue that feeds the larva and ceases plant cell division and elongation. Under incompatible interactions (i.e., unsuccessful HF establishment), plants respond to larval feeding by killing the larva, permitting normal plant development. We used reflectance spectroscopy to characterize whole-plant functional trait responses during both compatible and incompatible interactions and related these findings with morphological and gene expression observations from earlier studies. Spectral models successfully characterized wheat foliar traits, with mean goodness of fit statistics of 0.84, 0.85, 0.94, and 0.69 and percent root mean square errors of 22, 10, 6, and 20%, respectively, for nitrogen and carbon concentrations, leaf mass per area, and total phenolic content. We found that larvae capable of generating compatible interactions successfully manipulated host plant chemical and morphological composition to create a more hospitable environment. Incompatible interactions resulted in lower host plant nutritional quality, thicker leaves, and higher phenolic levels. Spectral measurements successfully characterized wheat responses to compatible and incompatible interactions, providing an excellent example of the utility of Spectral phenotyping in quantifying responses of specific plant functional traits associated with insect resistance.