1,362,600 research outputs found
In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale
No full textThe application of hyperspectral imaging technology for plant disease detection in the field is still challenging. Existing equipment and analysis algorithms are adapted to highly controlled environmental conditions in the laboratory. However, only real time information from the field scale is able to guide plant protection measures and to optimize the use of resources. At the field scale, many parameters such as the optimal measurement distance, informative feature sets, and suitable algorithms have not been investigated. In this study, the hyperspectral detection and quantification of yellow rust in wheat was evaluated using two measurement platforms: a ground-based vehicle and an unmanned aerial vehicle (UAV). Different disease development stages and disease severities were provided in a plot-based field experiment. Measurements were performed weekly during the vegetation period. Data analysis was performed by three prediction algorithms with a focus on the selection of optimal feature sets. In this context, the across-scale application of optimized feature sets, an approach of information transfer between scales, was also evaluated. Relevant aspects for an on-line disease assessment in the field integrating affordable sensor technology, sensor spatial resolution, compact analysis models, and fast evaluation have been outlined and reflected upon. For the first time, a hyperspectral imaging observation experiment of a plant disease was comparatively performed at two scales, ground canopy and UAV
Hyperspectral Sensors and Imaging Technologies in Phytopathology: State of the Art
No full textPlant disease detection represents a tremendous challenge for research and practical applications. Visual assessment by human raters is time-consuming, expensive, and error prone. Disease rating and plant protection need new and innovative techniques to address forthcoming challenges and trends in agricultural production that require more precision than ever before. Within this context, hyperspectral sensors and imaging techniques—intrinsically tied to efficient data analysis approaches—have shown an enormous potential to provide new insights into plant-pathogen interactions and for the detection of plant diseases. This article provides an overview of hyperspectral sensors and imaging technologies for assessing compatible and incompatible plant-pathogen interactions. Within the progress of digital technologies, the vision, which is increasingly discussed in the society and industry, includes smart and intuitive solutions for assessing plant features in plant phenotyping or for making decisions on plant protection measures in the context of precision agriculture
Joshua Davis: Author of Spare Parts
No full textCitation: K-State First (2016). Joshua Davis: Author of Spare Parts [Flier]. Manhattan, Kansas: K-State First.Flyer advertising Joshua Davis's author talk at Kansas State University
Prediction of the kiwifruit decline syndrome in diseased orchards by remote sensing
No full textEight years after the first record in Italy, Kiwifruit Decline (KD), a destructive disease causing root rot, has already affected more than 25% of the area under kiwifruit cultivation in Italy. Diseased plants are characterised by severe decay of the fine roots and sudden wilting of the canopy, which is only visible after the season's first period of heat (July-August). The swiftness of symptom appearance prevents correct timing and positioning for sampling of the disease, and is therefore a barrier to aetiological studies. The aim of this study is to test the feasibility of thermal and multispectral imaging for the detection of KD using an unsupervised classifier. Thus, RGB, multispectral and thermal data from a kiwifruit orchard, with healthy and diseased plants, were acquired simultaneously during two consecutive growing seasons (2017-2018) using an Unmanned Aerial Vehicle (UAV) platform. Data reduction was applied to the clipped areas of the multispectral and thermal data from the 2017 survey. Reduced data were then classified with two unsupervised algorithms, a K-means and a hierarchical method. The plant vigour (canopy size and presence/absence of wilted leaves) and the health shifts exhibited by asymptomatic plants between 2017 and 2018 were evaluated from RGB data via expert assessment and used as the ground truth for cluster interpretation. Multispectral data showed a high correlation with plant vigour, while temperature data demonstrated a good potential use in predicting health shifts, especially in highly vigorous plants that were asymptomatic in 2017 and became symptomatic in 2018. The accuracy of plant vigour assessment was above 73% when using multispectral data, while clustering of the temperature data allowed the prediction of disease outbreak one year in advance, with an accuracy of 71%. Based on our results, the unsupervised clustering of remote sensing data could be a reliable tool for the identification of sampling areas, and can greatly improve aetiological studies of this new disease in kiwifruit
Steven Johnson Author Talk Poster
No full textK-State Book NetworkA poster advertising an author talk by Steven Johnson at Kansas State University on September 3, 2014. Steven Johnson's book "The Ghost Map" was the 2014-2015 common book
Hyperspectral quantification of wheat resistance to Fusarium head blight: comparison of two Fusarium species
No full textInteractions of Fusarium species with different wheat varieties differ in their temporal dynamics and symptom appearance. Reliable and objective approaches for monitoring processes during infection are demanded for plant phenotyping and disease rating. This study presents an automated method to phenotype wheat varieties to Fusarium head blight (FHB) using hyperspectral sensors. In time-series experiments, the optical properties of spikes infected with F. graminearum or F. culmorum were recorded. Two hyperspectral cameras, in visible and near-infrared (VIS-NIR, 400–1000 nm) and shortwave-infrared (SWIR, 1000–2500 nm) captured the most relevant bands for pigments, cell structure, water and further compounds. Correlations between disease severity (DS), spike weight, spectral bands and vegetation indices were investigated. Following, the detectability of infections was assessed by Support Vector Machine (SVM) classifier. A variety ranking based on AUDPC was performed and compared to a fully-automated approach using Non-metric Multi-Dimensional Scaling (NMDS). High correlation was found between the spectral signature and DS in 430–525 nm, 560–710 nm and 1115–2500 nm. All indices from the VIS-NIR showed high correlation with DS and, for the first time, this was also confirmed for three indices from the SWIR: NDNI, CAI and MSI. Using SVM, differentiation between healthy and infected spikes was possible (acc. > 0.76). Furthermore, the possibility to differentiate between F. graminearum and F. culmorum infected spikes has been verified. The NMDS approach was able to reproduce accurately the variety ranking and outlines the potential of hyperspectral imaging to phenotype the variety susceptibility for improved breeding processes
Fusarium Head Blight on Wheat: Biology, Modern Detection and Diagnosis and Integrated Disease Management
No full textFusarium head blight (FHB) is a major threat for wheat production worldwide. Most reviews focus on Fusarium graminearum as a main causal agent of FHB. However, different Fusarium species are involved in this disease complex. These species differ in their geographic adaptation and mycotoxin profile. The incidence of FHB epidemics is highly correlated with weather conditions, especially rainy days with warm temperatures at anthesis and an abundance of primary inoculum. Yield losses due to the disease can reach up to 80% of the crop. This review summarizes the Fusarium species involved in the FHB disease complex with the corresponding mycotoxin profiles, disease cycle, diagnostic methods, the history of FHB epidemics, and the management strategy of the disease. In addition, it discusses the role of remote sensing technology in the integrated management of the disease. This technology can accelerate the phenotyping process in the breeding programs aiming at FHB-resistant varieties. Moreover, it can support the decision-making strategies to apply fungicides via monitoring and early detection of the diseases under field conditions. It can also be used for selective harvest to avoid mycotoxin-contaminated plots in the field
Elucidation of turnip yellows virus (TuYV) spectral reflectance pattern in Nicotiana benthamiana by non-imaging sensor technology
No full textTurnip yellows virus (TuYV), belonging to the genus Polerovirus of the family Solemoviridae, is an aphid transmissible, pathogenic virus causing considerable yield losses in oilseed rape (Brassica napus subsp. napus) cultivation. Virus detection in infected plants is difficult due to the phloem limitation and the irregular distribution of the virions within the host plant tissue. The spectral reflectance of leaves following TuYV inoculation in the model plant Nicotiana benthamiana was determined with the help of a handheld and portable non-imaging spectrometer under greenhouse conditions. Virus infection and a classification of virus content groups “very low, low, medium and high” were possible to be predicted with a mean average of 87% accuracy compared with the non-inoculated control plants when referenced to enzyme-linked immunosorbent assay (ELISA) data. Different spectral reflectance patterns (380–2500 nm) according to the specific virus content group of different leaf levels along the plant axis were mapped. Furthermore, machine learning allowed identifying the importance of specific areas in the spectral signature following TuYV infection. Between “control” and “medium” a specific peak within the visible area around 700 nm wavelength and between “control” and “high” the importance of the spectral area > 750 nm were identified. These data can serve as a basis to characterize metabolic or cytochemical changes after virus infection in host plants. Furthermore, spectral sensing devices enable non-destructive and informative dissection of leaf physiological properties and provide valuable information for monitoring virus spread in plants in greenhouse applications or on the field scale
Infrared Thermography as a Non-Invasive Tool to Explore Differences in the Musculoskeletal System of Children with Hemophilia Compared to an Age-Matched Healthy Group
No full textRecurrent joint bleeds and silent bleeds are the most common clinical feature in patients with hemophilia. Every bleed causes an immediate inflammatory response and is the leading cause of chronic crippling arthropathy. With the help of infrared thermography we wanted to detect early differences between a group of clinical non-symptomatic children with hemophilia (CWH) with no history of clinically detected joint bleeds and a healthy age-matched group of children. This could help to discover early inflammation and help implement early treatment and preventative strategies. It could be demonstrated that infrared thermography is sensitive enough to detect more signs of early inflammatory response in the CWH than in healthy children. It seems to detect more side differences in temperature than clinical examination of silent symptoms detects tender points. Silent symptoms/tender points seem to be combined with early local inflammation. Using such a non-invasive and sensor-based early detection, prevention of overloading and bleeding might be achieved
Screening of Barley Resistance Against Powdery Mildew by Simultaneous High-Throughput Enzyme Activity Signature Profiling and Multispectral Imaging
No full textMolecular marker analysis allow for a rapid and advanced pre-selection and resistance screenings in plant breeding processes. During the phenotyping process, optical sensors have proved their potential to determine and assess the function of the genotype of the breeding material. Thereby, biomarkers for specific disease resistance traits provide valuable information for calibrating optical sensor approaches during early plant-pathogen interactions. In this context, the combination of physiological, metabolic phenotyping and phenomic profiles could establish efficient identification and quantification of relevant genotypes within breeding processes. Experiments were conducted with near-isogenic lines of H. vulgare (susceptible, mildew locus o (mlo) and Mildew locus a (Mla) resistant). Multispectral imaging of barley plants was daily conducted 0–8 days after inoculation (dai) in a high-throughput facility with 10 wavelength bands from 400 to 1,000 nm. In parallel, the temporal dynamics of the activities of invertase isoenzymes, as key sink specific enzymes that irreversibly cleave the transport sugar sucrose into the hexose monomers, were profiled in a semi high-throughput approach. The activities of cell wall, cytosolic and vacuole invertase revealed specific dynamics of the activity signatures for susceptible genotypes and genotypes with mlo and Mla based resistances 0–120 hours after inoculation (hai). These patterns could be used to differentiate between interaction types and revealed an early influence of Blumeria graminis f.sp. hordei (Bgh) conidia on the specific invertase activity already 0.5 hai. During this early powdery mildew pathogenesis, the reflectance intensity increased in the blue bands and at 690 nm. The Mla resistant plants showed an increased reflectance at 680 and 710 nm and a decreased reflectance in the near infrared bands from 3 dai. Applying a Support Vector Machine classification as a supervised machine learning approach, the pixelwise identification and quantification of powdery mildew diseased barley tissue and hypersensitive response spots were established. This enables an automatic identification of the barley-powdery mildew interaction. The study established a proof-of-concept for plant resistance phenotyping with multispectral imaging in high-throughput. The combination of invertase analysis and multispectral imaging showed to be a complementing validation system. This will provide a deeper understanding of optical data and its implementation into disease resistance screening
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