26 research outputs found
Callose synthase and xyloglucan endotransglucosylase gene expression over time in Citrus × clementina and Citrus × sinensis infected with citrus tristeza virus
Citrus tristeza virus (CTV) is a virus that already caused great losses in citrus producing regions. The cell wall of plant cells plays an important role in the defence response to viruses. Following several studies indicating that cell wall enzyme transcripts of callose synthase 7 (calS7) and xyloglucan endotransglucosylase 9 (xth9) are modified during a viral infection, transcript expression of calS7 isoform x5 (calS7x5) and xth9 was evaluated over time in Citrus x sinensis 'Valencia Late' (VL) and Citrus x clementina 'Fina' (CL), infected with the severe CTV isolate T318A, by quantitative (q) PCR. qPCR analysis of healthy and CTV infected citrus was performed at 15 days, 10 months and at 31 months post-inoculation (dpi/mpi), respectively. The CTV titer, evaluated at the three time-points by qPCR, increased over time in bark tissues, with VL plants exhibiting a titer about 5 times higher than CL 31 mpi. CTV infection did not cause significant changes in calS7x5 gene expression over time in both citrus cultivars. However, CTV infection was associated with significant up-regulation of xth9 in VL compared to controls 31 mpi. This study highlights that CTV infection can affect the expression of specific cell wall-associated genes over time and that this influence was distinct for VL and CL. This study provides further insight into the CTV-citrus host interaction, with the long-term response of VL to a severe CTV isolate involving a high expression of the xth9 gene.DL57/2016/CP1361/project CT0015; ALG01–0145-FEDER-30957;info:eu-repo/semantics/publishedVersio
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.info:eu-repo/semantics/publishedVersio
Advances in plant disease detection and monitoring: From traditional assays to in-field diagnostics
none7noHuman activities significantly contribute to worldwide spread of phytopathological adversities. Pathogen-related food losses are today responsible for a reduction in quantity and quality of yield and decrease value and financial returns. As a result, “early detection” in combination with “fast, accurate, and cheap” diagnostics have also become the new mantra in plant pathology, especially for emerging diseases or challenging pathogens that spread thanks to asymptomatic individuals with subtle initial symptoms but are then difficult to face. Furthermore, in a globalized market sensitive to epidemics, innovative tools suitable for field-use represent the new frontier with respect to diagnostic laboratories, ensuring that the instruments and techniques used are suitable for the operational contexts. In this framework, portable systems and interconnection with Internet of Things (IoT) play a pivotal role. Here we review innovative diagnostic methods based on nanotechnologies and new perspectives concerning information and communication technology (ICT) in agriculture, resulting in an improvement in agricultural and rural development and in the ability to revolutionize the concept of “preventive actions”, making the difference in fighting against phytopathogens, all over the world.openBuja I.; Sabella E.; Monteduro A.G.; Chiriaco M.S.; De Bellis L.; Luvisi A.; Maruccio G.Buja, I.; Sabella, E.; Monteduro, A. G.; Chiriaco, M. S.; De Bellis, L.; Luvisi, A.; Maruccio, G
Abstracts of invited talks, oral and poster presentations given at the 15th Congress of the Mediterranean Phytopathological Union, June 20–23, 2017, in Córdoba, Spain
All abstracts from oral and poster presentation
Abstracts of invited talks, oral and poster presentations given at the 15th Congress of the Mediterranean Phytopathological Union, June 20–23, 2017, in Córdoba, Spain
All abstracts from oral and poster presentation
Predicting plant environmental exposure using remote sensing
Wheat is one of the most important crops globally with 776.4 million tonnes produced in
2019 alone. However, 10% of all wheat yield is predicted to be lost to Septoria Tritici
Blotch (STB) caused by Zymoseptoria tritici (Z. tritici). Throughout Europe farmers spend
ÂŁ0.9 billion annually on preventative fungicide regimes to protect wheat against Z. tritici. A
preventative fungicide regime is used as Z. tritici has a 9-16 day asymptomatic latent phase
which makes it difficult to detect before symptoms develop, after which point fungicide
intervention is ineffective.
In the second chapter of my thesis I use hyperspectral sensing and imaging techniques,
analysed with machine learning to detect and predict symptomatic Z. tritici infection in
winter wheat, in UK based field trials, with high accuracy. This has the potential to
improve detection and monitoring of symptomatic Z. tritici infection and could facilitate
precision agriculture methods, to use in the subsequent growing season, that optimise
fungicide use and increase yield.
In the third chapter of my thesis, I develop a multispectral imaging system which can detect
and utilise none visible shifts in plant leaf reflectance to distinguish plants based on the
nitrogen source applied. Currently, plants are treated with nitrogen sources to increase
growth and yield, the most common being calcium ammonium nitrate. However, some
nitrogen sources are used in illicit activities. Ammonium nitrate is used in explosive
manufacture and ammonium sulphate in the cultivation and extraction of the narcotic
cocaine from Erythroxylum spp. In my third chapter I show that hyperspectral sensing,
multispectral imaging, and machine learning image analysis can be used to visualise and
differentiate plants exposed to different nefarious nitrogen sources. Metabolomic analysis
of leaves from plants exposed to different nitrogen sources reveals shifts in colourful
metabolites that may contribute to altered reflectance signatures. This suggests that
different nitrogen feeding regimes alter plant secondary metabolism leading to changes in
plant leaf reflectance detectable via machine learning of multispectral data but not the
naked eye. These results could facilitate the development of technologies to monitor illegal
activities involving various nitrogen sources and further inform nitrogen application
requirements in agriculture.
In my fourth chapter I implement and adapt the hyperspectral sensing, multispectral
imaging and machine learning image analysis developed in the third chapter to detect
asymptomatic (and symptomatic) Z. tritici infection in winter wheat, in UK based field
trials, with high accuracy. This has the potential to improve detection and monitoring of all
stages of Z. tritici infection and could facilitate precision agriculture methods to be used
during the current growing season that optimise fungicide use and increase yield.Open Acces
Sensors and biosensors for pathogen and pest detection in agricultural systems : recent trends and oportunities
Pathogen and pest-linked diseases across agriculture and ecosystems are a major issue towards enhancing current thresholds in terms of farming yields and food security. Recent developments in nanotechnology allowed the designing of new generation sensors and biosensors in order to detect and mitigate these biological hazards. However, there are still important challenges concerning its respective applications in agricultural systems, typically related to point-of-care testing, cost reduction and real-time analysis. Thus, an important question arises: what are the current state-of-the-art trends and relationships among sensors and biosensors for pathogen and pest detection in agricultural systems? Targeted to meet this gap, a comparative study is performed by a literature review of the past decade and further data mining analysis. With the majority of the results coming from recent studies, leading trends towards new technologies were reviewed and identified, along with its respective agricultural application and target pathogens, such as bacteria, viruses, fungi, as well as pests like insects and parasites. Results have indicated lateral flow assay, lab-on-a-chip technologies and infrared thermography (both fixed and aerial) as the most promising categories related to sensors and biosensors driven to the detection of several different pathogenic varieties. The main existing interrelations between the results are especially associated to cereals, fruits and nuts, meat and dairy along with vegetables and legumes, mostly caused by bacterial and fungal infections. Additional results also presented and discussed, providing a fertile groundwork for decision-making and further developments in modern smart farming and IoT-based agriculture
Detection of grapevine viral diseases in Australian vineyards using remote sensing and hyperspectral technology
Grapevine viral diseases cause substantial productivity and economic losses in the
Australian viticulture industry. Two economically significant grapevine viral diseases -
Grapevine Leafroll Disease (GLD) and Shiraz Disease (SD) - affect numerous
vineyards across major wine regions in Australia. Accurate and quick diagnosis of the
virus infection would greatly assist disease management for growers. Current
detection methods include visual assessment and laboratory-based tests that are
expensive and labour-intensive. Low-cost and rapid alternative methods are desirable
in the industry. Recent advances in low-altitude remote sensing platforms such as
unmanned aerial vehicles (UAVs or “drones”) in conjunction with high-resolution multiand
hyper-spectral cameras now enable large spatial-scale surveillance of plant
stresses. My thesis therefore focuses on developing fast and reliable methods for GLD
and SD detection on a vineyard scale using optical sensors including RGB and
hyperspectral and low-altitude remote sensing technology.
The thesis is constituted by a review article and three result parts, it begins with a
general introduction for the background and is followed by the research goals and
significance of the project that is described in Chapter 1. In order to be familiar with
all possible technologies that can be potentially used for GLD and SD detection,
Chapter 2 includes a comprehensive overview of methodologies for the detection of
any plant viruses reviewed from laboratory-based, destructive molecular and
serological assays, to state-of-the-art non-destructive methods using optical sensors
and machine vision, including use of hyperspectral cameras. A key contribution of the
review is that, for the first time, a detailed economic analysis or cost comparison of the
various detection methodologies for plant viruses is provided.
In my research, various detection methods with different degrees of complexity were
attempted for GLD and SD detection. Firstly, a simple and novel detection method
using the projected leaf area (PLA) calculated from UAV RGB images is proposed in
Chapter 3 for the disease symptom that alters the growth of the vine such as SD in
Shiraz. The PLA is closely related to the canopy size. There are significant differences
in PLA between healthy and SD-infected vines in spring due to retarded growth caused by SD, which offers a simple, rapid and practical method to detect SD in Shiraz
vineyards. However, for diseases that cannot be easily detected by RGB images such
as GLD in the white grape cultivars, different approaches are needed.
Hyperspectral technology provides a wide spectrum of light with hundreds of narrow
bands compared to RGB sensors. The advanced technology can detect imperceptible
spectral changes from the disease and is particularly valuable for asymptomatic
disease detection. A new approach using proximal hyperspectral sensing is described
in Chapter 4. Using a handheld passive (sunlight is the radiation source) hyperspectral
sensor to detect GLD in the vineyard presents a simple and rapid measurement
method to detect the diseases using the spectral information from the canopy. An
assessment was done for the disease's spectral reflectance throughout the grape
growing season for both red and white cultivars. The partial least squares-discriminant
analysis (PLS-DA) was used to build a classification model to predict the disease.
Prediction accuracies of 96% and 76% were achieved for Pinot Noir and Chardonnay,
respectively. The proximal hyperspectral sensing technique is readily applicable to a
low-altitude remote sensing method to capture high-resolution hyperspectral images
for large-scale viral disease surveillance in vineyards. The subsequent study in
Chapter 5 presents an advanced method to quickly detect disease using an UAV
carried hyperspectral sensor. The study evaluated the feasibility of UAV-based
hyperspectral sensing in the visible and near-infrared (VNIR) spectral bands to detect
GLD and SD in four popular wine grapevine cultivars in Australian vineyards. The
method combined the spectral and spatial analysis to classify disease for individual
pixels from the hyperspectral image. The model predictions for red- and white-berried
grapevine cultivars achieved accuracies of 98% and 75%, respectively. For each viral
disease, unique spectral regions and optimal detection times during the growing
season were identified. The spectral difference between virus-infected and healthy
vines closely matched the spectral signal from the proximal sensing method in Chapter
4, which demonstrated the reliability of the low-altitude hyperspectral sensing for
grapevine disease detection.
Lastly, a summary of the outcomes and remaining challenges and limitations of the
existing technology is discussed in Chapter 6, followed by suggestions for further
research for further improvement.Thesis (Ph.D.) -- University of Adelaide, School of Agriculture, Food & Wine, 202