Remote Sensing of Diseases

Australian Cool-Season Pulse Seed-Borne Virus Research: 1. Alfalfa and Cucumber Mosaic Viruses and Less Important Viruses

Here, we review the research undertaken since the 1950s in Australia's grain cropping regions on seed-borne virus diseases of cool-season pulses caused by alfalfa mosaic virus (AMV) and cucumber mosaic virus (CMV). We present brief background information about the continent's pulse industry, virus epidemiology, management principles and future threats to virus disease management. We then take a historical approach towards all past investigations with these two seed-borne pulse viruses in the principal cool-season pulse crops grown: chickpea, faba bean, field pea, lentil, narrow-leafed lupin and white lupin. With each pathosystem, the main focus is on its biology, epidemiology and management, placing particular emphasis on describing field and glasshouse experimentation that enabled the development of effective phytosanitary, cultural and host resistance control strategies. Past Australian cool-season pulse investigations with AMV and CMV in the less commonly grown species (vetches, narbon bean, fenugreek, yellow and pearl lupin, grass pea and other Lathyrus species) and those with the five less important seed-borne pulse viruses also present (broad bean stain virus, broad bean true mosaic virus, broad bean wilt virus, cowpea mild mottle virus and peanut mottle virus) are also summarized. The need for future research is emphasized, and recommendations are made regarding what is required.

Nanotechnology and E-Sensing for Food Chain Quality and Safety

Nowadays, it is well known that sensors have an enormous impact on our life, using streams of data to make life-changing decisions. Every single aspect of our day is monitored via thousands of sensors, and the benefits we can obtain are enormous. With the increasing demand for food quality, food safety has become one of the main focuses of our society. However, fresh foods are subject to spoilage due to the action of microorganisms, enzymes, and oxidation during storage. Nanotechnology can be applied in the food industry to support packaged products and extend their shelf life. Chemical composition and sensory attributes are quality markers which require innovative assessment methods, as existing ones are rather difficult to implement, labour-intensive, and expensive. E-sensing devices, such as vision systems, electronic noses, and electronic tongues, overcome many of these drawbacks. Nanotechnology holds great promise to provide benefits not just within food products but also around food products. In fact, nanotechnology introduces new chances for innovation in the food industry at immense speed. This review describes the food application fields of nanotechnologies; in particular, metal oxide sensors (MOS) will be presented.

Characterization of Rice-Magnaporthe oryzae Interactions by Hyperspectral Imaging

Hyperspectral imaging has the potential to detect, characterize, and quantify plant diseases objectively and nondestructively to improve phenotyping in breeding for disease resistance. In this study, leaf spectral reflectance characteristics of five rice genotypes diseased with blast caused by three Magnaporthe oryzae isolates differing in virulence were compared with visual disease ratings under greenhouse conditions. Spectral information (140 wavebands, range 450 to 850 nm) of infected leaves was recorded with a hyperspectral imaging microscope at 3, 5, and 7 days postinoculation to examine differences in symptom phenotypes and to characterize the compatibility of host-pathogen interactions. Depending on the rice genotype × M. oryzae genotype interaction, blast symptoms varied from tiny necrosis to enlarged lesions with symptom subareas differing in tissue coloration and indicated gene-for-gene-specific interactions. The blast symptom types were differentiated based on their spectral characteristics in the visible/near-infrared range. Symptom-specific spectral signatures and differences in the composition of leaf blast symptom type(s) resulted in unique spectral and spatial patterns of the rice × M. oryzae interactions based on the size, shape, and color of the symptom subareas. Spectral angle mapper classification of spectra enabled (i) discrimination between healthy (green) and diseased tissue of rice genotypes, (ii) classification and quantification of different blast symptom subareas, and (iii) grading of the host-pathogen compatibility (low - intermediate - high). Hyperspectral imaging was more sensitive to small changes in disease resistance than visual disease assessments and enabled the characterization of various types of resistance/susceptibility reactions of tissue subjected to M. oryzae infection.

Integrated Nematode Management in a World in Transition: Constraints, Policy, Processes, and Technologies for the Future

Plant-parasitic nematodes are one of the most insidious pests limiting agricultural production, parasitizing mostly belowground and occasionally aboveground plant parts. They are an important and underestimated component of the estimated 30% yield loss inflicted on crops globally by biotic constraints. Nematode damage is intensified by interactions with biotic and abiotic factors constraints: soilborne pathogens, soil fertility degradation, reduced soil biodiversity, climate variability, and policies influencing the development of improved management options. This review focuses on the following topics: (a) biotic and abiotic constraints, (b) modification of production systems, (c) agricultural policies, (d) the microbiome, (e) genetic solutions, and (f) remote sensing. Improving integrated nematode management (INM) across all scales of agricultural production and along the Global North-Global South divide, where inequalities influence access to technology, is discussed. The importance of the integration of technological development in INM is critical to improving food security and human well-being in the future.

Scope of Onsite, Portable Prevention Diagnostic Strategies for Alternaria Infections in Medicinal Plants

Medicinal plants are constantly challenged by different biotic inconveniences, which not only cause yield and economic losses but also affect the quality of products derived from them. Among them, Alternaria pathogens are one of the harmful fungal pathogens in medicinal plants across the globe. Therefore, a fast and accurate detection method in the early stage is needed to avoid significant economic losses. Although traditional methods are available to detect Alternaria, they are more time-consuming and costly and need good expertise. Nevertheless, numerous biochemical- and molecular-based techniques are available for the detection of plant diseases, but their efficacy is constrained by differences in their accuracy, specificity, sensitivity, dependability, and speed in addition to being unsuitable for direct on-field studies. Considering the effect of Alternaria on medicinal plants, the development of novel and early detection measures is required to detect causal Alternaria species accurately, sensitively, and rapidly that can be further applied in fields to speed up the advancement process in detection strategies. In this regard, nanotechnology can be employed to develop portable biosensors suitable for early and correct pathogenic disease detection on the field. It also provides an efficient future scope to convert innovative nanoparticle-derived fabricated biomolecules and biosensor approaches in the diagnostics of disease-causing pathogens in important medicinal plants. In this review, we summarize the traditional methods, including immunological and molecular methods, utilized in plant-disease diagnostics. We also brief advanced automobile and efficient sensing technologies for diagnostics. Here we are proposing an idea with a focus on the development of electrochemical and/or colorimetric properties-based nano-biosensors that could be useful in the early detection of Alternaria and other plant pathogens in important medicinal plants. In addition, we discuss challenges faced during the fabrication of biosensors and new capabilities of the technology that provide information regarding disease management strategies.

Hyperspectral mapping of the response of grapevine cultivars to Plasmopara viticola infection at the tissue scale

Resistance of grapevine to Plasmopara viticola is associated with the hypersensitive reaction, accumulation of stilbenoids, and formation of callose depositions. Spectral characterization of infected leaf tissue of cvs 'Regent' and 'Solaris' with resistance genes Rpv 3-1 and Rpv 10 and Rpv 3-3, respectively, suggested that resistance is not dependent on large-scale necrotization of host tissue. Reactions of the resistant cultivars and a reference susceptible to P. viticola were studied using hyperspectral imaging (range 400-1000 nm) at the tissue level and microscopic techniques. Resistance of both cultivars was incomplete and allowed pathogen reproduction. Spectral vegetation indices characterized the host response to pathogen invasion; the vitality of infected and necrotic leaf tissue differed significantly. Resistance depended on local accumulation of polyphenols in response to haustorium formation and was more effective for cv. 'Solaris'. Although hypersensitive reaction of some cells prevented colonization of palisade parenchyma, resistance was not associated with extensive necrotization of tissue, and the biotrophic pathogen survived localized death of penetrated host cells. Hyperspectral imaging was suitable to characterize and differentiate the resistance reactions of grapevine cultivars by mapping of the cellular response to pathogen attack on the tissue level and yields useful information on host-pathogen interactions.

Symbiotic Fungi of an Ambrosia Beetle Alter the Volatile Bouquet of Cork Oak Seedlings

In Portugal, fungal symbionts of the ambrosia beetle Platypus cylindrus affect tree vigor of cork oak (Quercus suber) and are linked with the cork oak decline process. Fungal symbionts play crucial roles in the life history of bark and ambrosia beetles and recent work indicates complex interactions on the fungal and plant metabolic level. Colonized trees may respond with an array of currently unknown volatile metabolites being indicative of such interactions, acting as infochemicals with their environment. In this study, we examined volatile organic compounds (VOCs) of cork oak seedlings wound inoculated with strains of three fungal associates of P. cylindrus (Raffaelea montetyi, R. quercina, and Ceratocystiopsis sp. nov.) over a 45-day period by means of thermodesorption gas chromatography-mass spectrometry techniques. Fungal strains induced largely quantitative but species-specific changes among the 58 VOCs characterized. Overall, monoterpenes-the major volatiles of cork oak foliage-were significantly reduced, possibly a result of fungal biotransformation. Acetophenone, sulcatone, and nonanal-volatiles known for mediating ambrosia beetle behavior-increased in response to fungal inoculation. Qualitative VOC profiles of excised tissue of wood lesions (21 VOCs) and pure fungal cultures (60 VOCs) showed little overlap with seedling VOCs, indicating their plant-derived but fungal-induced origin. This chemoecological study expands on the limited knowledge of VOCs as infochemicals emitted from oak trees threatened by oak decline in relation to beetle-vectored ophiostomatoid fungi. It opens new avenues of research to clarify mutualistic or pathogenic aspects of these complex symbiotic interactions and develop new control strategies for P. cylindrus, including its mycobiota.

Climate Change Effects on Pathogen Emergence: Artificial Intelligence to Translate Big Data for Mitigation

Plant pathology has developed a wide range of concepts and tools for improving plant disease management, including models for understanding and responding to new risks from climate change. Most of these tools can be improved using new advances in artificial intelligence (AI), such as machine learning to integrate massive data sets in predictive models. There is the potential to develop automated analyses of risk that alert decision-makers, from farm managers to national plant protection organizations, to the likely need for action and provide decision support for targeting responses. We review machine-learning applications in plant pathology and synthesize ideas for the next steps to make the most of these tools in digital agriculture. Global projects, such as the proposed global surveillance system for plant disease, will be strengthened by the integration of the wide range of new data, including data from tools like remote sensors, that are used to evaluate the risk ofplant disease. There is exciting potential for the use of AI to strengthen global capacity building as well, from image analysis for disease diagnostics and associated management recommendations on farmers' phones to future training methodologies for plant pathologists that are customized in real-time for management needs in response to the current risks. International cooperation in integrating data and models will help develop the most effective responses to new challenges from climate change.

The Viral Threat in Cotton: How New and Emerging Technologies Accelerate Virus Identification and Virus Resistance Breeding

Cotton (Gossypium spp. L., Malvaceae) is the world's largest source of natural fibers. Virus outbreaks are fast and economically devasting regarding cotton. Identifying new viruses is challenging as virus symptoms usually mimic nutrient deficiency, insect damage, and auxin herbicide injury. Traditional viral identification methods are costly and time-consuming. Developing new resistant cotton lines to face viral threats has been slow until the recent use of molecular virology, genomics, new breeding techniques (NBT), remote sensing, and artificial intelligence (AI). This perspective article demonstrates rapid, sensitive, and cheap technologies to identify viral diseases and propose their use for virus resistance breeding.

Remote Sensing in Studies of the Growing Season: A Bibliometric Analysis

Analyses of climate change based on point observations indicate an extension of the plant growing season, which may have an impact on plant production and functioning of natural ecosystems. Analyses involving remote sensing methods, which have added more detail to results obtained in the traditional way, have been carried out only since the 1980s. The paper presents the results of a bibliometric analysis of papers related to the growing season published from 2000–2021 included in the Web of Science database. Through filtering, 285 publications were selected and subjected to statistical processing and analysis of their content. This resulted in the identification of author teams that mostly focused their research on vegetation growth and in the selection of the most common keywords describing the beginning, end, and duration of the growing season. It was found that most studies on the growing season were reported from Asia, Europe, and North America (i.e., 32%, 28%, and 28%, respectively). The analyzed articles show the advantage of satellite data over low-altitude and ground-based data in providing information on plant vegetation. Over three quarters of the analyzed publications focused on natural plant communities. In the case of crops, wheat and rice were the most frequently studied plants (i.e., they were analyzed in over 30% and over 20% of publications, respectively).

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.

Biosensor Technologies for Early Detection and Quantification of Plant Pathogens

Plant pathogens are a major reason of reduced crop productivity and may lead to a shortage of food for both human and animal consumption. Although chemical control remains the main method to reduce foliar fungal disease incidence, frequent use can lead to loss of susceptibility in the fungal population. Furthermore, over-spraying can cause environmental contamination and poses a heavy financial burden on growers. To prevent or control disease epidemics, it is important for growers to be able to detect causal pathogen accurately, sensitively, and rapidly, so that the best practice disease management strategies can be chosen and enacted. To reach this goal, many culture-dependent, biochemical, and molecular methods have been developed for plant pathogen detection. However, these methods lack accuracy, specificity, reliability, and rapidity, and they are generally not suitable for in-situ analysis. Accordingly, there is strong interest in developing biosensing systems for early and accurate pathogen detection. There is also great scope to translate innovative nanoparticle-based biosensor approaches developed initially for human disease diagnostics for early detection of plant disease-causing pathogens. In this review, we compare conventional methods used in plant disease diagnostics with new sensing technologies in particular with deeper focus on electrochemical and optical biosensors that may be applied for plant pathogen detection and management. In addition, we discuss challenges facing biosensors and new capability the technology provides to informing disease management strategies.