scholarly journals Remote sensing and interpolation methods can obtain weather data for disease prediction

2010 ◽  
Vol 63 ◽  
pp. 182-186
Author(s):  
K.S. Kim ◽  
G.N. Hill ◽  
R.M. Beresford
Keyword(s):  
Remote Sensing ◽  
Spatial Interpolation ◽  
Disease Risk ◽  
Disease Outbreaks ◽  
Weather Conditions ◽  
Weather Data ◽  
Climatic Data ◽  
Weather Station ◽  
Weather Information ◽  
Crop Disease

The risk of the appearance or the intensification of a crop disease can be assessed using information about the weather the pathogen or the crop Weather data for use in disease risk prediction can be obtained from measurements at a nearby weather station While weather measurements can represent accurate weather conditions at the site where the weather station is located these data are representative only of a small area near the station To obtain weather information over a larger area spatial interpolation and remote sensing can be used to estimate the likely weather conditions in other locations It is crucial to obtain weather data at an appropriate temporal resolution (eg daily or hourly) for a given disease in order to predict the disease A weather database system is being constructed to provide highquality climatic data (eg daily temperature humidity and rainfall) which can be used to quantify the link between weather conditions and disease outbreaks

scholarly journals A costloss analysis to evaluate the value of weather estimates as inputs to a disease risk model

2008 ◽  
Vol 61 ◽  
pp. 296-300
Author(s):  
K.S. Kim ◽  
R.M. Beresford ◽  
W.R. Henshall
Keyword(s):  
Spatial Interpolation ◽  
Risk Model ◽  
Disease Risk ◽  
Weather Data ◽  
Nearest Neighbour ◽  
Spray Application ◽  
Bunch Rot ◽  
The Cost

To assess the value of weather estimates the expenses including the cost of spray application and the losses incurred because of a disease were analysed using disease risk simulations In this case study the risk of botrytis bunch rot was simulated using weather estimates as inputs to a disease risk model Those estimates were obtained using spatial interpolation and nearest neighbour methods Possible cost was calculated based on 2 by 2 matrices under the assumption that fungicide spray decisions were made using the disease risk model with estimated weather data The expenses associated with each combination of decisions on spray application and outcome were compared between weather estimates and measurements

scholarly journals Effect of Meteorological and Geographical Factors on the Epidemics of Hand, Foot, and Mouth Disease in Island-Type Territory, East Asia

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Chang-Chun David Lee ◽  
Jia-Hong Tang ◽  
Jing-Shiang Hwang ◽  
Mika Shigematsu ◽  
Ta-Chien Chan
Keyword(s):  
East Asia ◽  
Disease Risk ◽  
Meteorological Factors ◽  
Foot And Mouth Disease ◽  
Weather Conditions ◽  
Mouth Disease ◽  
Dew Point ◽  
Weather Data ◽  
Public Health Issue ◽  
Foot And Mouth

Hand, foot, and mouth disease (HFMD) has threatened East Asia for more than three decades and has become an important public health issue owing to its severe sequelae and mortality among children. The lack of effective treatment and vaccine for HFMD highlights the urgent need for efficiently integrated early warning surveillance systems in the region. In this study, we try to integrate the available surveillance and weather data in East Asia to elucidate possible spatiotemporal correlations and weather conditions among different areas from low to high latitude. The general additive model (GAM) was applied to understand the association between HFMD and latitude, as well as meteorological factors for islands in East Asia, namely, Japan, Taiwan, Hong Kong, and Singapore, from 2012 to 2014. The results revealed that latitude was the most important explanatory factor associated with the timing and amplitude of HFMD epidemics (P<0.0001). Meteorological factors including higher dew point, lower visibility, and lower wind speed were significantly associated with the rise of epidemics (P<0.01). In summary, weather conditions and geographic location could play some role in affecting HFMD epidemics. Regional integrated surveillance of HFMD in East Asia is needed for mitigating the disease risk.

scholarly journals Comparing Kriging Estimators Using Weather Station Data and Local Greenhouse Sensors

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1853
Author(s):  
Pei-Fen Kuo ◽  
Tzu-En Huang ◽  
I Gede Brawiswa Putra
Keyword(s):  
Weather Conditions ◽  
Environmental Data ◽  
Weather Station ◽  
Weather Information ◽  
Station Data ◽  
Weather Station Data ◽  
Real Value ◽  
Weather Stations ◽  
Spatio Temporal ◽  
Central Weather Bureau

In order to minimize the impacts of climate change on various crops, farmers must learn to monitor environmental conditions accurately and effectively, especially for plants that are particularly sensitive to the weather. On-site sensors and weather stations are two common methods for collecting data and observing weather conditions. Although sensors are capable of collecting accurate weather information on-site, they can be costly and time-consuming to install and maintain. An alternative is to use the online weather stations, which are usually government-owned and free to the public; however, their accuracy is questionable because they are frequently located far from the farmers’ greenhouses. Therefore, we compared the accuracy of kriging estimators using the weather station data (collected by the Central Weather Bureau) to local sensors located in the greenhouse. The spatio-temporal kriging method was used to interpolate temperature data. The real value at the central point of the greenhouse was used for comparison. According to our results, the accuracy of the weather station estimator was slightly lower than that of the local sensor estimator. Farmers can obtain accurate estimators of environmental data by using on-site sensors; however, if they are unavailable, using a nearby weather station estimator is also acceptable.

scholarly journals Weather Analysis of Flight Delay at Husein Sastranegara Airport

2019 ◽  
Vol 4 (2) ◽  
pp. 89-98
Author(s):  
Yedi Dermadi ◽  
Shinta Devi Lukitasari ◽  
Annisaa Nurhayati
Keyword(s):  
Weather Conditions ◽  
Air Transportation ◽  
Weather Data ◽  
Flight Delays ◽  
Weather Forecasts ◽  
Weather Information ◽  
Directorate General ◽  
Flight Delay ◽  
Flight Cancellations ◽  
Bad Weather

Flight is an activity that is very vulnerable to weather conditions. The accuracy of weather information strongly supports flight activities. The effects of bad weather on flights include flight delays and flight cancellations. Based on data on flight delays from the Directorate General of Air Transportation of the Ministry of Transportation from January to March 2019 at Husein Sastranegara Airport, it is known that 20-30% of flight delays are caused by weather constraints. To estimate flight delays based on weather forecasts, weather data analysis is carried out to determine the type of weather that is endangering flights and causing flight delays. The analysis was carried out using the K-NN and Random Forest algorithms

scholarly journals SENSITIVITY ANALYSIS OF AQUACROP EVAPOTRANSPIRATION TO WEATHER STATION DISTANCE

AGROFOR ◽  
2016 ◽  
Vol 1 (3) ◽  
Author(s):  
Claire SIMONIS ◽  
Bernard TYCHON ◽  
Françoise GELLENSMEULENBERGHS
Keyword(s):  
Sensitivity Analysis ◽  
Water Availability ◽  
Weather Conditions ◽  
Actual Evapotranspiration ◽  
Weather Data ◽  
Grass Cover ◽  
Weather Station ◽  
Grid Data ◽  
The Impact ◽  
Wheat Plot

Water balance calculation is essential for reliable agricultural management, and theactual evapotranspiration (ET) is the most complicated balance term to estimate. Inagriculture, the most common method used is based on Penman-Monteith referenceevaporation is determined from weather conditions for an unstressed grass cover,further multiplied by crop specific and soil water availability coefficients to obtainthe actual evapotranspiration. This approach is also used in the AquaCrop model.This model has proven to be accurate when all weather data are locally available.However, in many cases, weather data can’t be collected on the site due to thelimited number of stations and the vast region covered by each of them. Instead,data are often collected at many kilometers from the study site. The question wewant to study is: how does evapotranspiration accuracy evolves with respect toweather station distance? A winter wheat plot in Lonzée (Belgium) was studiedduring the 2014-2015 agricultural seasons. Actual evapotranspiration wassimulated with AquaCrop thanks to the weather data collected at 3 differentdistances from the study site: on the site (data collected by a fluxnet station), 20km, 50 km and 70km from the site. The non-on-site weather data were derivedfrom spatially interpolated 10 km grid data. These results were then compared tothe fluxnet station evapotranspiration measurements to assess the impact of theweather station distance. Substantial differences, which were found between thefour cases, evoking the importance of assimilating satellite derived ET products(e.g. MSG) into AquaCrop.

Accuracy of Weather Data in Long-Term Pavement Performance Program Database

2000 ◽  
Vol 1699 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Chung-Lung Wu ◽  
Gonzalo R. Rada ◽  
Aramis Lopez ◽  
Yingwu Fang
Keyword(s):  
Weather Conditions ◽  
Test Site ◽  
Climatic Conditions ◽  
Weighting Scheme ◽  
Pavement Performance ◽  
Weather Data ◽  
Climatic Data ◽  
National Climatic Data Center ◽  
Weather Stations

To provide accurate climatic data for pavements under the Long-Term Pavement Performance (LTPP) Program, a climatic database was developed in 1992 and subsequently revised and expanded in 1998. In the development of this database, up to five nearby weather stations were selected for each test site. Pertinent weather data for the selected weather stations were obtained from the U.S. National Climatic Data Center and the Canadian Climatic Center. With a 1/ R2 weighting scheme, site-specific climatic data were derived from the nearby weather station data. The derived data were referred to as “virtual”weather data. To evaluate the effect of environmental factors on pavement performance and design, automated weather stations (AWS) were installed at LTPP Specific Pavement Study Projects 1, 2, and 8 to collect on-site weather data. Since the virtual weather data were developed for all LTPP test sites and will be used for future pavement performance studies, it is essential that the derived virtual data be accurate and representative of the actual onsite climatic conditions. The availability of the AWS weather data has provided an opportunity to evaluate whether virtual weather data can be used to represent on-site weather conditions. Daily temperature data and monthly temperature and precipitation data were used in this experiment. On the basis of the comparisons made between the virtual and onsite measured (AWS) data, it appears that climatic data derived from nearby weather stations using the 1/R2 weighting scheme estimate the actual weather data reasonably well and thus can be used to represent on-site weather conditions in pavement research and design.

scholarly journals A new potato late blight disease prediction model and its comparison with two previous models

2006 ◽  
Vol 59 ◽  
pp. 150-154 ◽  
Author(s):  
W.R. Henshall ◽  
D. Shtienberg ◽  
R.M. Beresford
Keyword(s):  
Late Blight ◽  
Prediction Models ◽  
Disease Risk ◽  
Disease Process ◽  
Weather Conditions ◽  
Potato Late Blight ◽  
Weather Data ◽  
Research Station ◽  
Disease Prediction ◽  
Risk Area

There are numerous disease prediction models for potato late blight based on recognition of weather conditions suitable for infection The models have the potential to target fungicide application to times of greatest need with a consequent reduction in chemical use The HartillYoung late blight model was developed about 20 years ago from disease and weather data recorded at the Pukekohe Research Station This paper presents the more sophisticated Shtienberg model which was developed recently from the same data but which treats components of the disease process separately The outputs of the HartillYoung and Shtienberg models and the established Fry model were analysed for the same input weather data at Pukekohe (high disease risk area) and Lincoln (low risk) over the last five growing seasons The Shtienberg model gave broadly similar results to the other two models

scholarly journals Iot Based Weather Station Using Raspberry Pi 3

2018 ◽  
Vol 7 (4.30) ◽  
pp. 145 ◽  
Author(s):  
P Y Muck ◽  
M J Homam
Keyword(s):  
Real Time ◽  
Mobile Application ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Weather Data ◽  
Raspberry Pi ◽  
Weather Station ◽  
Media Platform ◽  
Weather Parameters ◽  
Graphical Presentation

Weather is the day-to-day state of atmosphere that is hard to predict which affects the activities of mankind and has great significance in many different domains. However, the current weather station in the market is expensive and bulky which cause inconvenience. The aim of this project is to design a weather station with real time notifications for climatology monitoring, interface it to a cloud platform and analyse weather parameters. In this project, a weather station is assembled using SparkFun Weather Shield and Weather Meter and Arduino Uno R3 to collect weather parameters. Data collected from the sensors are then stored into Google Cloud SQL using Raspberry Pi 3 Model B which acts as a gateway between them and analysis of weather data are done. A website and mobile application are developed using Google Data Studio and Android Studio respectively to display the real-time weather conditions in graphical presentation which are accessible by administrator and users. Users will receive notification regarding the weather conditions at that particular place on social media platform regularly and irregularly. Weather prediction is done in short term which allows users to get themselves prepared for their future plan in the next thirty minutes.

scholarly journals Prediction of disease risk using sitespecific estimates of weather variables

2007 ◽  
Vol 60 ◽  
pp. 128-132 ◽  
Author(s):  
K.S. Kim ◽  
R.M. Beresford ◽  
W.R. Henshall
Keyword(s):  
Spatial Interpolation ◽  
Disease Risk ◽  
Interpolation Method ◽  
Voronoi Tessellation ◽  
Growing Season ◽  
Weather Data ◽  
Weather Variables ◽  
Risk Models ◽  
Bunch Rot ◽  
Spatial Interpolation Method

To improve the implementation of weatherbased disease risk models a spatial interpolation method was investigated to provide weather estimates for specific sites Two sites in the HortResearch horticultural weather station network one in Marlborough and one in Hawkes Bay were selected as validation sites Interpolated weather data were estimated for these sites from November to March in 200304 and 200405 using actual weather data from nearby stations that were selected as natural neighbours using the geometrical technique Voronoi tessellation Wetness duration was also estimated using interpolated weather data as inputs to an empirical wetness model Air temperature estimates were comparable to actual measurements but wetness duration was overestimated When interpolated and actual data were used as inputs to the grape botrytis model Bacchus predicted risks were comparable to each other for short periods rather than the whole growing season This suggests that risk of botrytis bunch rot could be predicted reliably at a specific site using the spatial interpolation method

Lands DSS

2021 ◽  
Vol 13 (1) ◽  
pp. 21-33
Author(s):  
Gianni Fenu ◽  
Francesca Maridina Malloci
Keyword(s):  
Cross Validation ◽  
Environmental Changes ◽  
Disease Risk ◽  
Risk Index ◽  
Human Action ◽  
Weather Data ◽  
Feed Forward Neural Network ◽  
Crop Disease ◽  
Fold Cross Validation

Decision support systems (DSSs) are used in precision farming to address climate and environmental changes due to human action. However, increments in the amount of data produced continuously by the latest sensor and satellite technologies have recently incentivized the integration of artificial intelligence (AI). A review of research dedicated to the application of DSSs and AI in forecasting crop disease is proposed. In this paper, the authors describe the DSS LANDS developed for monitoring the main crop productions in Sardinia and the case study conducted to forecast potato late blight. A feed-forward neural network was implemented to investigate if weather data provided by regional stations could be used to predict a disease risk index using an AI technique. The test performed by stratified k-fold cross validation achieved an accuracy of 96%.

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