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

Hyperspectral Imaging in the UV Range Allows for Differentiation of Sugar Beet Diseases Based on Changes in Secondary Plant Metabolites

Fungal infections trigger defense or signaling responses in plants, leading to various changes in plant metabolites. The changes in metabolites, for example chlorophyll or flavonoids, have long been detectable using time-consuming destructive analytical methods including high-performance liquid chromatography or photometric determination. Recent plant phenotyping studies have revealed that hyperspectral imaging (HSI) in the UV range can be used to link spectral changes with changes in plant metabolites. To compare established destructive analytical methods with new nondestructive hyperspectral measurements, the interaction between sugar beet leaves and the pathogens Cercospora beticola, which causes Cercospora leaf spot disease (CLS), and Uromyces betae, which causes sugar beet rust (BR), was investigated. With the help of destructive analyses, we showed that both diseases have different effects on chlorophylls, carotenoids, flavonoids, and several phenols. Nondestructive hyperspectral measurements in the UV range revealed different effects of CLS and BR on plant metabolites resulting in distinct reflectance patterns. Both diseases resulted in specific spectral changes that allowed differentiation between the two diseases. Machine learning algorithms enabled the differentiation between the symptom classes and recognition of the two sugar beet diseases. Feature importance analysis identified specific wavelengths important to the classification, highlighting the utility of the UV range. The study demonstrates that HSI in the UV range is a promising, nondestructive tool to investigate the influence of plant diseases on plant physiology and biochemistry.

Current State of Hyperspectral Remote Sensing for Early Plant Disease Detection: A Review

The development of hyperspectral remote sensing equipment, in recent years, has provided plant protection professionals with a new mechanism for assessing the phytosanitary state of crops. Semantically rich data coming from hyperspectral sensors are a prerequisite for the timely and rational implementation of plant protection measures. This review presents modern advances in early plant disease detection based on hyperspectral remote sensing. The review identifies current gaps in the methodologies of experiments. A further direction for experimental methodological development is indicated. A comparative study of the existing results is performed and a systematic table of different plants' disease detection by hyperspectral remote sensing is presented, including important wave bands and sensor model information.

Volatile-mediated plant-plant interactions: volatile organic compounds as modulators of receiver plant defence, growth, and reproduction

It is firmly established that plants respond to biotic and abiotic stimuli by emitting volatile organic compounds (VOCs). These VOCs provide information on the physiological status of the emitter plant and are available for detection by the whole community. In the context of plant-plant interactions, research has focused mostly on the defence-related responses of receiver plants. However, responses may span hormone signalling and both primary and secondary metabolism, and ultimately affect plant fitness. Here we present a synthesis of plant-plant interactions, focusing on the effects of VOC exposure on receiver plants. An overview of the important chemical cues, the uptake and conversion of VOCs, and the adsorption of VOCs to plant surfaces is presented. This is followed by a review of the substantial VOC-induced changes to receiver plants affecting both primary and secondary metabolism and influencing plant growth and reproduction. Further research should consider whole-plant responses for the effective evaluation of the mechanisms and fitness consequences of exposure of the receiver plant to VOCs.

Early infestations by arthropod pests induce unique changes in plant compositional traits and leaf reflectance

BACKGROUND

With steadily growing interest in the use of remote-sensing technologies to detect and diagnose pest infestations in crops, it is important to investigate and characterize possible associations between crop leaf reflectance and unique pest-induced changes in plant compositional traits. Accordingly, we compiled plant compositional traits from chrysanthemum and gerbera plants in four treatments: non-infested, or infested with mites, thrips or whiteflies, and we acquired hyperspectral leaf reflectance data from the same plants over time (0-14 days).

RESULTS

Plant compositional traits changed significantly in response to arthropod infestations, and individual chrysanthemum and gerbera plants were classified with 78% and 80% accuracy, respectively. Based on leaf reflectance, individual plants from the four treatments were classified with moderate accuracy levels of 76% (gerbera) and 73% (chrysanthemum) but with a clear distinction between non-infested and infested plants. Accurate and consistent diagnosis of biotic stressors was not achieved.

CONCLUSION

To our knowledge, this is the first study in which infestations by multiple economically important arthropod pests are directly compared and associated with leaf reflectance responses and changes in plant compositional traits. It is important to highlight that imposed stress levels were low, period of infestation was short, and hyperspectral remote-sensing data were acquired at four time points with analyses based on large data sets (3826 leaf reflectance profiles for chrysanthemum and 4041 for gerbera). This study provides novel insight into crop responses to different biotic stressors and into possible associations between plant compositional traits and hyperspectral leaf reflectance data acquired from crop leaves.

Past and Future of Plant Stress Detection: An Overview From Remote Sensing to Positron Emission Tomography

Plant stress detection is considered one of the most critical areas for the improvement of crop yield in the compelling worldwide scenario, dictated by both the climate change and the geopolitical consequences of the Covid-19 epidemics. A complicated interconnection of biotic and abiotic stressors affect plant growth, including water, salt, temperature, light exposure, nutrients availability, agrochemicals, air and soil pollutants, pests and diseases. In facing this extended panorama, the technology choice is manifold. On the one hand, quantitative methods, such as metabolomics, provide very sensitive indicators of most of the stressors, with the drawback of a disruptive approach, which prevents follow up and dynamical studies. On the other hand qualitative methods, such as fluorescence, thermography and VIS/NIR reflectance, provide a non-disruptive view of the action of the stressors in plants, even across large fields, with the drawback of a poor accuracy. When looking at the spatial scale, the effect of stress may imply modifications from DNA level (nanometers) up to cell (micrometers), full plant (millimeters to meters), and entire field (kilometers). While quantitative techniques are sensitive to the smallest scales, only qualitative approaches can be used for the larger ones. Emerging technologies from nuclear and medical physics, such as computed tomography, magnetic resonance imaging and positron emission tomography, are expected to bridge the gap of quantitative non-disruptive morphologic and functional measurements at larger scale. In this review we analyze the landscape of the different technologies nowadays available, showing the benefits of each approach in plant stress detection, with a particular focus on the gaps, which will be filled in the nearby future by the emerging nuclear physics approaches to agriculture.

Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale

Background

The use of hyperspectral cameras is well established in the field of plant phenotyping, especially as a part of high-throughput routines in greenhouses. Nevertheless, the workflows used differ depending on the applied camera, the plants being imaged, the experience of the users, and the measurement set-up.

Results

This review describes a general workflow for the assessment and processing of hyperspectral plant data at greenhouse and laboratory scale. Aiming at a detailed description of possible error sources, a comprehensive literature review of possibilities to overcome these errors and influences is provided. The processing of hyperspectral data of plants starting from the hardware sensor calibration, the software processing steps to overcome sensor inaccuracies, and the preparation for machine learning is shown and described in detail. Furthermore, plant traits extracted from spectral hypercubes are categorized to standardize the terms used when describing hyperspectral traits in plant phenotyping. A scientific data perspective is introduced covering information for canopy, single organs, plant development, and also combined traits coming from spectral and 3D measuring devices.

Conclusions

This publication provides a structured overview on implementing hyperspectral imaging into biological studies at greenhouse and laboratory scale. Workflows have been categorized to define a trait-level scale according to their metrological level and the processing complexity. A general workflow is shown to outline procedures and requirements to provide fully calibrated data of the highest quality. This is essential for differentiation of the smallest changes from hyperspectral reflectance of plants, to track and trace hyperspectral development as an answer to biotic or abiotic stresses.

Drones: Innovative Technology for Use in Precision Pest Management

Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers.

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

Rice diseases are major threats to rice yield and quality. Rapid and accurate detection of rice diseases is of great importance for precise disease prevention and treatment. Various spectroscopic techniques have been used to detect plant diseases. To rapidly and accurately detect three different rice diseases [leaf blight (Xanthomonas oryzae pv. Oryzae), rice blast (Pyricularia oryzae), and rice sheath blight (Rhizoctonia solani)], three spectroscopic techniques were applied, including visible/near-infrared hyperspectral imaging (HSI) spectra, mid-infrared spectroscopy (MIR), and laser-induced breakdown spectroscopy (LIBS). Three different levels of data fusion (raw data fusion, feature fusion, and decision fusion) fusing three different types of spectral features were adopted to categorize the diseases of rice. Principal component analysis (PCA) and autoencoder (AE) were used to extract features. Identification models based on each technique and different fusion levels were built using support vector machine (SVM), logistic regression (LR), and convolution neural network (CNN) models. Models based on HSI performed better than those based on MIR and LIBS, with the accuracy over 93% for the test set based on PCA features of HSI spectra. The performance of rice disease identification varied with different levels of fusion. The results showed that feature fusion and decision fusion could enhance identification performance. The overall results illustrated that the three techniques could be used to identify rice diseases, and data fusion strategies have great potential to be used for rice disease detection.

Optical imaging spectroscopy for plant research: more than a colorful picture

Optical imaging is a routine and indispensable tool in plant research. Here we review different emerging spectrally resolved optical imaging approaches and the wealth of information they can be used to obtain pertaining to the underlying chemistry, structure and mechanics of plants.

Comparative study of the mycorrhizal root transcriptomes of wild and cultivated rice in response to the pathogen Magnaporthe oryzae

BACKGROUND

Rice, which serves as a staple food for more than half of the world's population, is very susceptible to the pathogenic fungus, Magnaporthe oryzae. However, common wild rice (Oryza rufipogon), which is the ancestor of Asian cultivated rice (O. sativa), has significant potential as a genetic source of resistance to M. oryzae. Recent studies have shown that the domestication of rice has altered its relationship to symbiotic arbuscular mycorrhizae. A comparative response of wild and domestic rice inhabited by mycorrhizae to infection by M. oryzae has not been documented.

RESULTS

In the current study, roots of wild and cultivated rice colonized with the arbuscular mycorrhizal (AM) fungus (AMF) Rhizoglomus intraradices were used to compare the transcriptomic responses of the two species to infection by M. oryzae. Phenotypic analysis indicated that the colonization of wild and cultivated rice with R. intraradices improved the resistance of both genotypes to M. oryzae. Wild AM rice, however, was more resistant to M. oryzae than the cultivated AM rice, as well as nonmycorrhizal roots of wild rice. Transcriptome analysis indicated that the mechanisms regulating the responses of wild and cultivated AM rice to M. oryzae invasion were significantly different. The expression of a greater number of genes was changed in wild AM rice than in cultivated AM rice in response to the pathogen. Both wild and cultivated AM rice exhibited a shared response to M. oryzae which included genes related to the auxin and salicylic acid pathways; all of these play important roles in pathogenesis-related protein synthesis. In wild AM rice, secondary metabolic and biotic stress-related analyses indicated that the jasmonic acid synthesis-related α-linolenic acid pathway, the phenolic and terpenoid pathways, as well as the phenolic and terpenoid syntheses-related mevalonate (MVA) pathway were more affected by the pathogen. Genes related to these pathways were more significantly enriched in wild AM rice than in cultivated AM rice in response to M. oryzae. On the other hand, genes associated with the 'brassinosteroid biosynthesis' were more enriched in cultivated AM rice.

CONCLUSIONS

The AMF R. intraradices-colonized rice plants exhibited greater resistance to M. oryzae than non-AMF-colonized plants. The findings of the current study demonstrate the potential effects of crop domestication on the benefits received by the host via root colonization with AMF(s), and provide new information on the underlying molecular mechanisms. In addition, results of this study can also help develop guidelines for the applications of AMF(s) when planting rice.

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

BACKGROUND

Soybean aphid, Aphis glycines Matsumura (Hemiptera: Aphididae), is a significant insect pest of soybean in North America. Accurate estimation of A. glycines densities requires costly, time-intensive weekly counts of adults and nymphs on plants. Field studies were conducted in 2013 and 2014 to assess the potential for spectral-based remote sensing to more efficiently quantify cumulative aphid-days (CADs) using soybean canopy reflectance.

RESULTS

Narrow-band wavelengths in the near-infrared spectral range were associated with CAD, but those in the visible spectral range were not associated with CAD. Simple linear regression models of CAD on reflectance were generally better than quadratic and cubic regression models. Simulated wide-band sensors centered at 740-1100 nm yielded better regression models than ones centered at 600-740 nm, regardless of bandwidth. Among the simulated wide-band sensors, increasing sensor bandwidth worsened CAD estimation or required more simulated sensors to optimize CAD estimation. Optimal combinations of spectral bands explained 83-96% of the experimentally manipulated variation in CAD.

CONCLUSION

Near-infrared wavelengths at 780 ± 50 nm can effectively estimate A. glycines abundance on soybean. Our approach of simulating wide-band multispectral sensors from ground-based hyperspectral data helped to refine spectral sensors and holds potential to reduce the cost and complexity of treat/no-treat classification tasks. This study will contribute to future research aiming to quantify insect injury using customized commercial-grade sensors for detection, quantification, and differentiation of A. glycines from other stressors. © 2018 Society of Chemical Industry.

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

As part of identifying and characterizing physiological responses and adaptations by insects, it is paramount to develop non-destructive techniques to monitor individual insects over time. Such techniques can be used to optimize the timing of when in-depth (i.e., destructive sampling of insect tissue) physiological or molecular analyses should be deployed. In this article, we present evidence that hyperspectral proximal remote sensing can be used effectively in studies of host responses to parasitism. We present time series body reflectance data acquired from individual soybean loopers (Chrysodeixis includens) without parasitism (control) or parasitized by one of two species of parasitic wasps with markedly different life histories: Microplitis demolitor, a solitary larval koinobiont endoparasitoid and Copidosoma floridanum, a polyembryonic (gregarious) egg-larval koinobiont endoparasitoid. Despite considerable temporal variation in reflectance data 1-9 days post-parasitism, the two parasitoids caused uniquely different host body reflectance responses. Based on reflectance data acquired 3-5 days post-parasitism, all three treatments (control larvae, and those parasitized by either M. demolitor or C. floridanum) could be classified with >85 accuracy. We suggest that hyperspectral proximal imaging technologies represent an important frontier in insect physiology, as they are non-invasive and can be used to account for important time scale factors, such as: minutes of exposure or acclimation to abiotic factors, circadian rhythms, and seasonal effects. Although this study is based on data from a host-parasitoid system, results may be of broad relevance to insect physiologists. Described approaches provide a non-invasive and rapid method that can provide insights into when to destructively sample tissue for more detailed mechanistic studies of physiological responses to stressors and environmental conditions.