scholarly journals Challenges in Adaptation of Plant Disease Warning Systems to New Locations: Re-Appraisal of Billing's Integrated System for Predicting Fire Blight in a Warm Dry Environment

2007 ◽  
Vol 97 (9) ◽  
pp. 1036-1039 ◽  
Author(s):  
Eve Billing
Keyword(s):  
Risk Assessment ◽  
Plant Disease ◽  
Fire Blight ◽  
Weather Conditions ◽  
Integrated System ◽  
Warning Systems ◽  
New Locations ◽  
New Location

The purpose of this letter is to describe approaches and possible pitfalls when a fire blight model developed in one climatic area is evaluated in a new location where weather conditions are markedly different. A case is described where a modified form of Billing's integrated system, BIS95, which was developed in a cool moist climate, was tested in a country where weather is warmer and drier. Prior to this, some features of fire blight epidemiology, management, and risk assessment are outlined.

scholarly journals Mechanistically Compatible Mixtures of Bacterial Antagonists Improve Biological Control of Fire Blight of Pear

2011 ◽  
Vol 101 (1) ◽  
pp. 113-123 ◽  
Author(s):  
V. O. Stockwell ◽  
K. B. Johnson ◽  
D. Sugar ◽  
J. E. Loper
Keyword(s):  
Biological Control ◽  
Plant Disease ◽  
Fire Blight ◽  
Biological Control Agent ◽  
Field Trials ◽  
Control Agent ◽  
Extracellular Protease ◽  
Peptide Antibiotics ◽  
Biological Control Agents ◽  
Control Activity

Mixtures of biological control agents can be superior to individual agents in suppressing plant disease, providing enhanced efficacy and reliability from field to field relative to single biocontrol strains. Nonetheless, the efficacy of combinations of Pseudomonas fluorescens A506, a commercial biological control agent for fire blight of pear, and Pantoea vagans strain C9-1 or Pantoea agglomerans strain Eh252 rarely exceeds that of individual strains. A506 suppresses growth of the pathogen on floral colonization and infection sites through preemptive exclusion. C9-1 and Eh252 produce peptide antibiotics that contribute to disease control. In culture, A506 produces an extracellular protease that degrades the peptide antibiotics of C9-1 and Eh252. We hypothesized that strain A506 diminishes the biological control activity of C9-1 and Eh252, thereby reducing the efficacy of biocontrol mixtures. This hypothesis was tested in five replicated field trials comparing biological control of fire blight using strain A506 and A506 aprX::Tn5, an extracellular protease-deficient mutant, as individuals and combined with C9-1 or Eh252. On average, mixtures containing A506 aprX::Tn5 were superior to those containing the wild-type strain, confirming that the extracellular protease of A506 diminished the biological control activity of C9-1 and Eh252 in situ. Mixtures of A506 aprX::Tn5 and C9-1 or Eh252 were superior to oxytetracycline or single biocontrol strains in suppressing fire blight of pear. These experiments demonstrate that certain biological control agents are mechanistically incompatible, in that one strain interferes with the mechanism by which a second strain suppresses plant disease. Mixtures composed of mechanistically compatible strains of biological control agents can suppress disease more effectively than individual biological control agents.

scholarly journals Cars as next generation mobile weather stations: an initial investigation

2021 ◽  
Author(s):  
Giuliano Andrea Pagani ◽  
Marcel Molendijk ◽  
Jan Willem Noteboom
Keyword(s):  
Air Temperature ◽  
Weather Conditions ◽  
Ground Temperature ◽  
Warning Systems ◽  
Traction Control ◽  
The Future ◽  
Weather Stations ◽  
Temporal And Spatial ◽  
Positive Correlation Coefficient ◽  
Digital Equipment

<p>Modern automobiles are becoming more and more “computers on the wheels” having lots of digital equipment on board. Such equipment is both for the comfort and entertainment of the passengers and for their safety. Sensors play a key role in measuring several parameters of the car performance (e.g., traction control, anti-lock breaking system) and also environmental  parameters are observed directly (e.g., air temperature) or can be somehow inferred (e.g., precipitation via windscreen wipers activity/speed).</p><p>KNMI has been provided air temperature recorded every 10 minutes by thousands of vehicles driving in the Netherlands for the period January-October 2020. We have performed an initial exploratory temporal and spatial analysis to understand the most promising periods of the day and areas where sufficient data is available to perform a more thorough data analysis in the future. Furthermore, we have performed a correlation analysis between the outside temperature measured by cars and air and ground temperature observed by official weather station sensors placed at one location on the Dutch highways. The correlation results for three randomly selected days (with different weather conditions) show a good positive correlation coefficient ranging from 0.93 to 0.76 for car and station air temperature and from 0.91 to 0.67 for car temperature and station ground temperature.</p><p>This initial exploration paves the way to the use of (OEM) car data as (mobile) weather stations. We foresee in the future to use a combination of sensed variables from cars such as air temperature, traction control, windscreen wipers activity for example to improve observations of road slipperiness and related warning systems that are not restricted to Dutch highways only.</p>

scholarly journals RISK FORECAST SYSTEM FOR MOORED SHIPS

2020 ◽  
pp. 37
Author(s):  
Liliana PINHEIRO ◽  
Conceicao FORTES ◽  
Maria Teresa REIS ◽  
Joao SANTOS ◽  
Carlos GUEDES SOARES
Keyword(s):  
Risk Assessment ◽  
Weather Conditions ◽  
Economic Losses ◽  
Forecast System ◽  
Mooring Forces ◽  
Risk Forecast

Port terminals downtimes lead to large economic losses and largely affect the port's overall competitiveness. In the majority of cases, port activities such as ships' approach maneuvers and loading/unloading operations, are conditioned or suspended, based solely on weather or wave forecasts. These forecasts do not always result in effective hazardous conditions for the ships. Additionally, moored ships often experience problems of excessive movements and mooring forces in apparent good weather conditions. If, instead, one could forecast the ships' movements and mooring forces, risk assessment would be much more accurate. This would allow selecting an appropriate reinforced mooring arrangement and thus minimizing effective terminal downtime. In this paper, the development of a risk forecast system for moored ships, that takes into account all of the moored ship's system, is detailed and an illustration on how it applies to real ports is presented.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/ugDN9Tqno3E

scholarly journals IoTSAS: An Integrated System for Real-Time Semantic Annotation and Interpretation of IoT Sensor Stream Data

Computers ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 127
Author(s):  
Besmir Sejdiu ◽  
Florije Ismaili ◽  
Lule Ahmedi
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Contextual Information ◽  
Semantic Annotation ◽  
Weather Conditions ◽  
Water Quality Monitoring ◽  
Integrated System ◽  
Quality Monitoring ◽  
Stream Data ◽  
Semantic Annotations

Sensors and other Internet of Things (IoT) technologies are increasingly finding application in various fields, such as air quality monitoring, weather alerts monitoring, water quality monitoring, healthcare monitoring, etc. IoT sensors continuously generate large volumes of observed stream data; therefore, processing requires a special approach. Extracting the contextual information essential for situational knowledge from sensor stream data is very difficult, especially when processing and interpretation of these data are required in real time. This paper focuses on processing and interpreting sensor stream data in real time by integrating different semantic annotations. In this context, a system named IoT Semantic Annotations System (IoTSAS) is developed. Furthermore, the performance of the IoTSAS System is presented by testing air quality and weather alerts monitoring IoT domains by extending the Open Geospatial Consortium (OGC) standards and the Sensor Observations Service (SOS) standards, respectively. The developed system provides information in real time to citizens about the health implications from air pollution and weather conditions, e.g., blizzard, flurry, etc.

scholarly journals Rainfall Threshold Estimation and Landslide Forecasting for Kalimpong, India Using SIGMA Model

Water ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 1195 ◽  
Author(s):  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Sai Kushal ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan ◽  
...  
Keyword(s):  
Early Warning ◽  
Sigma Model ◽  
Warning System ◽  
Daily Rainfall ◽  
Early Warning Systems ◽  
Rainfall Threshold ◽  
Integrated System ◽  
Warning Systems ◽  
Rainfall Forecasting ◽  
Rainfall Thresholds

Rainfall-induced landslides are among the most devastating natural disasters in hilly terrains and the reduction of the related risk has become paramount for public authorities. Between the several possible approaches, one of the most used is the development of early warning systems, so as the population can be rapidly warned, and the loss related to landslide can be reduced. Early warning systems which can forecast such disasters must hence be developed for zones which are susceptible to landslides, and have to be based on reliable scientific bases such as the SIGMA (sistema integrato gestione monitoraggio allerta—integrated system for management, monitoring and alerting) model, which is used in the regional landslide warning system developed for Emilia Romagna in Italy. The model uses statistical distribution of cumulative rainfall values as input and rainfall thresholds are defined as multiples of standard deviation. In this paper, the SIGMA model has been applied to the Kalimpong town in the Darjeeling Himalayas, which is among the regions most affected by landslides. The objectives of the study is twofold: (i) the definition of local rainfall thresholds for landslide occurrences in the Kalimpong region; (ii) testing the applicability of the SIGMA model in a physical setting completely different from one of the areas where it was first conceived and developed. To achieve these purposes, a calibration dataset of daily rainfall and landslides from 2010 to 2015 has been used; the results have then been validated using 2016 and 2017 data, which represent an independent dataset from the calibration one. The validation showed that the model correctly predicted all the reported landslide events in the region. Statistically, the SIGMA model for Kalimpong town is found to have 92% efficiency with a likelihood ratio of 11.28. This performance was deemed satisfactory, thus SIGMA can be integrated with rainfall forecasting and can be used to develop a landslide early warning system.

scholarly journals Solving Current Limitations of Deep Learning Based Approaches for Plant Disease Detection

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 939 ◽  
Author(s):  
Marko Arsenovic ◽  
Mirjana Karanovic ◽  
Srdjan Sladojevic ◽  
Andras Anderla ◽  
Darko Stefanovic
Keyword(s):  
Deep Learning ◽  
Plant Disease ◽  
Real Life ◽  
Weather Conditions ◽  
Plant Diseases ◽  
Disease Classification ◽  
Generative Adversarial Networks ◽  
Disease Detection ◽  
Single Leaf ◽  
The Impact

Plant diseases cause great damage in agriculture, resulting in significant yield losses. The recent expansion of deep learning methods has found its application in plant disease detection, offering a robust tool with highly accurate results. The current limitations and shortcomings of existing plant disease detection models are presented and discussed in this paper. Furthermore, a new dataset containing 79,265 images was introduced with the aim to become the largest dataset containing leaf images. Images were taken in various weather conditions, at different angles, and daylight hours with an inconsistent background mimicking practical situations. Two approaches were used to augment the number of images in the dataset: traditional augmentation methods and state-of-the-art style generative adversarial networks. Several experiments were conducted to test the impact of training in a controlled environment and usage in real-life situations to accurately identify plant diseases in a complex background and in various conditions including the detection of multiple diseases in a single leaf. Finally, a novel two-stage architecture of a neural network was proposed for plant disease classification focused on a real environment. The trained model achieved an accuracy of 93.67%.

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