Early Identification of Root Rot Disease by Using Hyperspectral Reflectance: The Case of Pathosystem Grapevine/Armillaria

Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications

Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.

Tropical altitudinal gradient soil organic carbon and nitrogen estimation using Specim IQ portable imaging spectrometer

The largest actively cycling terrestrial carbon pool, soil, has been disturbed during latest centuries by human actions through reduction of woody land cover. Soil organic carbon (SOC) content can reliably be estimated in laboratory conditions, but more cost-efficient and mobile techniques are needed for large-scale monitoring of SOC e.g. in remote areas. We demonstrate the capability of a mobile hyperspectral camera operating in the visible-near infrared wavelength range for practical estimation of soil organic carbon (SOC) and nitrogen content, to support efficient monitoring of soil properties. The 191 soil samples were collected in Taita Taveta County, Kenya representing an altitudinal gradient comprising five typical land use types: agroforestry, cropland, forest, shrubland and sisal estate. The soil samples were imaged using a Specim IQ hyperspectral camera under controlled laboratory conditions, and their carbon and nitrogen content was determined with a combustion analyzer. We use machine learning for estimating SOC and N content based on the spectral images, studying also automatic selection of informative wavelengths and quantification of prediction uncertainty. Five alternative methods were all found to perform well with a cross-validated R² of approximately 0.8 and an RMSE of one percentage point, demonstrating feasibility of the proposed imaging setup and computational pipeline.

Inoculum production of Phytophthora medicaginis can be used to screen for partial resistance in chickpea genotypes

Phytophthora root rot caused by Phytophthora medicaginis is an important disease of chickpeas (Cicer arietinum) in Australia with limited management options, increasing reliance on breeding for improved levels of genetic resistance. Resistance based on chickpea-Cicer echinospermum crosses is partial with a quantitative genetic basis provided by C. echinospermum and some disease tolerance traits originating from C. arietinum germplasm. Partial resistance is hypothesised to reduce pathogen proliferation, while tolerant germplasm may contribute some fitness traits, such as an ability to maintain yield despite pathogen proliferation. To test these hypotheses, we used P. medicaginis DNA concentrations in the soil as a parameter for pathogen proliferation and disease assessments on lines of two recombinant inbred populations of chickpea-C. echinospermum crosses to compare the reactions of selected recombinant inbred lines and parents. Our results showed reduced inoculum production in a C. echinospermum backcross parent relative to the C. arietinum variety Yorker. Recombinant inbred lines with consistently low levels of foliage symptoms had significantly lower levels of soil inoculum compared to lines with high levels of visible foliage symptoms. In a separate experiment, a set of superior recombinant inbred lines with consistently low levels of foliage symptoms was tested for soil inoculum reactions relative to control normalised yield loss. The in-crop P. medicaginis soil inoculum concentrations across genotypes were significantly and positively related to yield loss, indicating a partial resistance-tolerance spectrum. Disease incidence and the rankings for in-crop soil inoculum were correlated strongly to yield loss. These results indicate that soil inoculum reactions may be useful to identify genotypes with high levels of partial resistance.

Hyperspectral reflectance imaging for nondestructive evaluation of root rot in Korean ginseng (Panax ginseng Meyer)

Root rot of Panax ginseng caused by Cylindrocarpon destructans, a soil-borne fungus is typically diagnosed by frequently checking the ginseng plants or by evaluating soil pathogens in a farm, which is a time- and cost-intensive process. Because this disease causes huge economic losses to ginseng farmers, it is important to develop reliable and non-destructive techniques for early disease detection. In this study, we developed a non-destructive method for the early detection of root rot. For this, we used crop phenotyping and analyzed biochemical information collected using the HSI technique. Soil infected with root rot was divided into sterilized and infected groups and seeded with 1-year-old ginseng plants. HSI data were collected four times during weeks 7-10 after sowing. The spectral data were analyzed and the main wavelengths were extracted using partial least squares discriminant analysis. The average model accuracy was 84% in the visible/near-infrared region (29 main wavelengths) and 95% in the short-wave infrared (19 main wavelengths). These results indicated that root rot caused a decrease in nutrient absorption, leading to a decline in photosynthetic activity and the levels of carotenoids, starch, and sucrose. Wavelengths related to phenolic compounds can also be utilized for the early prediction of root rot. The technique presented in this study can be used for the early and timely detection of root rot in ginseng in a non-destructive manner.

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.

Research Status and Application Prospects of the Medicinal Mushroom Armillaria mellea

Armillaria is one of the most common diseases underlying chronic root rot in woody plants. Although there is no particularly effective way to prevent it, soil disinfection is a common effective protective measure. However, Armillaria itself has important medicinal value and is a symbiotic fungus in the cultivation of Gastrodia elata and Polyporus umbellatus. Therefore, researching Armillaria is of great practical significance. In this review, the biological characteristics, cultivation methods, chemical components, food and medicinal value and efficacy of Armillaria were all reviewed, and its development and utilization direction were analyzed and discussed.

A non-destructive testing method for early detection of ginseng root diseases using machine learning technologies based on leaf hyperspectral reflectance

Ginseng is an important medicinal plant benefiting human health for thousands of years. Root disease is the main cause of ginseng yield loss. It is difficult to detect ginseng root disease by manual observation on the changes of leaves, as it takes a long time until symptoms appear on leaves after the infection on roots. In order to detect root diseases at early stages and limit their further spread, an efficient and non-destructive testing (NDT) method is urgently needed. Hyperspectral remote sensing technology was performed in this study to discern whether ginseng roots were diseased. Hyperspectral reflectance of leaves at 325-1,075 nm were collected from the ginsengs with no symptoms on leaves at visual. These spectra were divided into healthy and diseased groups according to the symptoms on roots after harvest. The hyperspectral data were used to construct machine learning classification models including random forest, extreme random tree (ET), adaptive boosting and gradient boosting decision tree respectively to identify diseased ginsengs, while calculating the vegetation indices and analyzing the region of specific spectral bands. The precision rates of the ET model preprocessed by savitzky golay method for the identification of healthy and diseased ginsengs reached 99% and 98%, respectively. Combined with the preliminary analysis of band importance, vegetation indices and physiological characteristics, 690-726 nm was screened out as a specific band for early detection of ginseng root diseases. Therefore, underground root diseases can be effectively detected at an early stage by leaf hyperspectral reflectance. The NDT method for early detection of ginsengs root diseases is proposed in this study. The method is helpful in the prevention and control of root diseases of ginsengs to prevent the reduction of ginseng yield.

First Report of Fusarium commune Associated with a Root Rot of Grapevine in China

During last decade, species belonging to Fusarium, Rosellinia, Armillaria and Dactylonectria were confirmed as phytopathogens causing grapevine root diseases (Highet and Nair 1995; Teixeira et al. 1995; Calamit et al. 2021; Ye et al. 2021). From 2020 to 2021, grapevine decline was observed in several vineyards in Beijing region, China. Leaves turned yellow with brown necrotic patches and roots were poorly developed, which was suggesting that a root disease was affecting the vines. The disease incidence was up to 10-15% of the vineyard for sample collection. Symptomatic root samples (cv. 'Red Globe') were collected and tissue fragments were excised at the margin of the symptomatic tissue in order to isolate the potential pathogen. The surface was sterilized using 1.5% sodium hypochlorite for 3 min, followed by 70% ethanol for 30 sec, and rinsed three times with sterile distilled water (Ye et al. 2020). Tissues were dried and placed onto potato dextrose agar (PDA) plates, followed by incubation at 25°C under dark conditions for 3 d. Hyphal tips of fungi growing from the samples were transferred onto new PDA plates and incubated until they produced conidia. Next, single spores were transferred onto new PDA plates and incubated at 25°C for 7 d. Eight isolates numbered with JZB3110172 to JZB3110179 were obtained and their culture characters were identical, and the re-isolation percentage was 100%. Colonies were white to orange, with abundant fluffy aerial mycelium. Macroconidia were fusiform with a slightly curved apical cell and a foot-shaped basal cell, and measured 16.2-43.2 μm × 2.7-4.9 μm (n=50); microconidia were cylindrical, straight to slightly curved, 5.1-13 × 2.1-3.9 μm (n=50). Morphological characters of the isolates resembled to Fusarium commune (Skovgaard et al. 2003). For phylogenetic analysis, genomic DNA of the eight isolates was extracted with a DNA extraction kit (DNeasy plant Mini Kit). PCR amplifications of two phylogenetic markers (EF-1α and RPB2) were performed using the primers EF-1/EF-2 (Geiser et al. 2004) and RPB2-5F2/RPB2-7cR (Liu et al. 1995), respectively. The sequences were deposited in GenBank ON457645 to ON457660. Comparison of base pairs on Maximum likelihood (ML) phylogenetic analysis was conducted using the RAxML-HPC2 tool on XSEDE on the CIPRES Science Gateway platform (http://www.phylo.org/). The sequences of EF-1α and RPB2 of the eight isolates showed 99 to 100% similarity to the reference isolates of F. commune. In the phylogenetic tree, the isolates from this study clustered with the representative strains of F. commune (NRRL 52764, NRRL 28387 and NRRL 52744). Based on morphological characters and the phylogenetic results, all of isolates were identified as F. commune. Koch's postulates were conducted on healthy, 3-month-old grapevine 'Marselan'. Plant roots were trimmed with sterile scissors and then soaked in a spore suspension (1.0 × 106 spores mL-1) or sterile water (as the control) for 30 min. The inoculated grapevines were transplanted into pots and kept in the greenhouse at 25°C. After 14 days, all the inoculated plants developed necrosis and turned yellow. No symptoms were observed on the control. Koch's postulates were fulfilled by re-isolating the fungus from necrotic root tissues. The isolates obtained from the artificially infected tissue were identified again as F. commune based on morphological and molecular analyses. Overall, this is the first report of F. commune associated with a grapevine root rot globally, which lays a foundation for further study and developing disease control methods.

Greater functional diversity and redundancy of coral endolithic microbiomes align with lower coral bleaching susceptibility

The skeleton of reef-building coral harbors diverse microbial communities that could compensate for metabolic deficiencies caused by the loss of algal endosymbionts, i.e., coral bleaching. However, it is unknown to what extent endolith taxonomic diversity and functional potential might contribute to thermal resilience. Here we exposed Goniastrea edwardsi and Porites lutea, two common reef-building corals from the central Red Sea to a 17-day long heat stress. Using hyperspectral imaging, marker gene/metagenomic sequencing, and NanoSIMS, we characterized their endolithic microbiomes together with ¹⁵N and ¹³C assimilation of two skeletal compartments: the endolithic band directly below the coral tissue and the deep skeleton. The bleaching-resistant G. edwardsi was associated with endolithic microbiomes of greater functional diversity and redundancy that exhibited lower N and C assimilation than endoliths in the bleaching-sensitive P. lutea. We propose that the lower endolithic primary productivity in G. edwardsi can be attributed to the dominance of chemolithotrophs. Lower primary production within the skeleton may prevent unbalanced nutrient fluxes to coral tissues under heat stress, thereby preserving nutrient-limiting conditions characteristic of a stable coral-algal symbiosis. Our findings link coral endolithic microbiome structure and function to bleaching susceptibility, providing new avenues for understanding and eventually mitigating reef loss.

A bibliometric and thematic approach to agriculture 4.0

Researchers are developing digital solutions for agriculture. Humanity has perfected agriculture throughout history because this activity is fundamental to our existence. The agricultural sector is currently incorporating new technologies from other areas. This phenomenon is agriculture 4.0. However, a challenge to research is the integration of technologies from different knowledge fields, and this has caused theoretical and practical difficulties. Thus, our purpose with this study has been to understand the core agriculture 4.0 research themes. We have used a bibliometric analysis, and guided the data collection by the PRISMA protocol. VosViewer and Bibliometrix software generated the results. We found two main research fronts, one focussed on agriculture 4.0 development, and another on the impacts of agriculture 4.0, which may be positive or negative. We found 21 main keywords or topics researched in agriculture 4.0 related to these research fronts. These themes are within five different axes. We managed to establish a good understanding of the topics around agriculture 4.0. Future studies could focus on the responsible development of digital solutions for agriculture. This is because the social, environmental, and economic impacts of these new solutions may be positive or negative. We conclude that digital agriculture is the node technologies integration for the automation of agricultural activities.

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There are two main research fronts.

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Agriculture 4.0 can have both positive and negative impacts.

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Agricultural development, food supply, and climate change are high centrality and density themes.

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Crops, remote sensing, and precision agriculture are motor themes.

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Agriculture, digital agriculture, and agricultural robots are basic themes.

Analysis of RGB Plant Images to Identify Root Rot Disease in Korean Ginseng Plants Using Deep Learning

Ginseng is an important medicinal plant in Korea. The roots of the ginseng plant have medicinal properties; thus, it is very important to maintain the quality of ginseng roots. Root rot disease is a major disease that affects the quality of ginseng roots. It is important to predict this disease before it causes severe damage to the plants. Hence, there is a need for a non-destructive method to identify root rot disease in ginseng plants. In this paper, a method to identify the root rot disease by analyzing the RGB plant images using image processing and deep learning is proposed. Initially, plant segmentation is performed, and then the noise regions are removed in the plant images. These images are given as input to the proposed linear deep learning model to identify root rot disease in ginseng plants. Transfer learning models are also applied to these images. The performance of the proposed method is promising in identifying root rot disease.

The Optical Response of a Mediterranean Shrubland to Climate Change: Hyperspectral Reflectance Measurements during Spring

Remote sensing techniques in terms of monitoring plants’ responses to environmental constraints have gained much attention during recent decades. Among these constraints, climate change appears to be one of the major challenges in the Mediterranean region. In this study, the main goal was to determine how field spectrometry could improve remote sensing study of a Mediterranean shrubland submitted to climate aridification. We provided the spectral signature of three common plants of the Mediterranean garrigue: Cistus albidus, Quercus coccifera, and Rosmarinus officinalis. The pattern of these spectra changed depending on the presence of a neighboring plant species and water availability. Indeed, the normalized water absorption reflectance (R975/R900) tended to decrease for each species in trispecific associations (11–26%). This clearly indicates that multispecific plant communities will better resist climate aridification compared to monospecific stands. While Q. coccifera seemed to be more sensible to competition for water resources, C. albidus exhibited a facilitation effect on R. officinalis in trispecific assemblage. Among the 17 vegetation indices tested, we found that the pigment pheophytinization index (NPQI) was a relevant parameter to characterize plant–plant coexistence. This work also showed that some vegetation indices known as indicators of water and pigment contents could also discriminate plant associations, namely RGR (Red Green Ratio), WI (Water Index), Red Edge Model, NDWI1240 (Normalized Difference Water Index), and PRI (Photochemical Reflectance Index). The latter was shown to be linearly and negatively correlated to the ratio of R975/R900, an indicator of water status.

Study on the Classification Method of Rice Leaf Blast Levels Based on Fusion Features and Adaptive-Weight Immune Particle Swarm Optimization Extreme Learning Machine Algorithm

Leaf blast is a disease of rice leaves caused by the Pyricularia oryzae. It is considered a significant disease is affecting rice yield and quality and causing economic losses to food worldwide. Early detection of rice leaf blast is essential for early intervention and limiting the spread of the disease. To quickly and non-destructively classify rice leaf blast levels for accurate leaf blast detection and timely control. This study used hyperspectral imaging technology to obtain hyperspectral image data of rice leaves. The descending dimension methods got rice leaf disease characteristics of different disease classes, and the disease characteristics obtained by screening were used as model inputs to construct a model for early detection of leaf blast disease. First, three methods, ElasticNet, principal component analysis loadings (PCA loadings), and successive projections algorithm (SPA), were used to select the wavelengths of spectral features associated with leaf blast, respectively. Next, the texture features of the images were extracted using a gray level co-occurrence matrix (GLCM), and the texture features with high correlation were screened by the Pearson correlation analysis. Finally, an adaptive-weight immune particle swarm optimization extreme learning machine (AIPSO-ELM) based disease level classification method is proposed to further improve the model classification accuracy. It was also compared and analyzed with a support vector machine (SVM) and extreme learning machine (ELM). The results show that the disease level classification model constructed using a combination of spectral characteristic wavelengths and texture features is significantly better than a single disease feature in terms of classification accuracy. Among them, the model built with ElasticNet + TFs has the highest classification accuracy, with OA and Kappa greater than 90 and 87%, respectively. Meanwhile, the AIPSO-ELM proposed in this study has higher classification accuracy for leaf blast level classification than SVM and ELM classification models. In particular, the AIPSO-ELM model constructed with ElasticNet+TFs as features obtained the best classification performance, with OA and Kappa of 97.62 and 96.82%, respectively. In summary, the combination of spectral characteristic wavelength and texture features can significantly improve disease classification accuracy. At the same time, the AIPSO-ELM classification model proposed in this study has sure accuracy and stability, which can provide a reference for rice leaf blast disease detection.