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 Adaption of potato late blight prediction models for New Zealand

2004 ◽  
Vol 57 ◽  
pp. 25-29 ◽  
Author(s):  
W.R. Henshall ◽  
R.M Beresford
Keyword(s):  
Late Blight ◽  
Management Practices ◽  
Prediction Models ◽  
Disease Risk ◽  
Potato Late Blight ◽  
The United Kingdom ◽  
Infection Models ◽  
Weather Stations ◽  
Using Data ◽  
Smith Model

Current management of potato late blight (caused by Phytophthora infestans) in the Pukekohe district is by frequent application of fungicides but consumer concerns over chemical use may require more accurate targeting of spray applications to times of greatest disease risk Decision support systems incorporating potato late blight infection models have the potential to assist growers in modifying management practices Outputs of two overseas models and one developed locally were compared using data from two weather stations at Pukekohe (severe late blight area) and Lincoln (low disease risk) over the 2003/04 growing season Two of the models were too sensitive for Pukekohe conditions but the Smith model developed in the United Kingdom gave usable results No attempt was made to verify the performance of the models under field conditions

scholarly journals Modeling of Yield Losses Caused by Potato Late Blight on Eight Cultivars with Different Levels of Resistance to Phytophthora infestans

Plant Disease ◽  
2012 ◽  
Vol 96 (7) ◽  
pp. 935-942 ◽  
Author(s):  
Toky Rakotonindraina ◽  
Jean-Éric Chauvin ◽  
Roland Pellé ◽  
Robert Faivre ◽  
Catherine Chatot ◽  
...  
Keyword(s):  
Late Blight ◽  
Phytophthora Infestans ◽  
Relative Yield ◽  
Weather Conditions ◽  
Potato Late Blight ◽  
Yield Losses ◽  
Predictive Quality ◽  
Wide Range ◽  
Cultivar Susceptibility

The Shtienberg model for predicting yield loss caused by Phytophthora infestans in potato was developed and parameterized in the 1990s in North America. The predictive quality of this model was evaluated in France for a wide range of epidemics under different soil and weather conditions and on cultivars different than those used to estimate its parameters. A field experiment was carried out in 2006, 2007, 2008, and 2009 in Brittany, western France to assess late blight severity and yield losses. The dynamics of late blight were monitored on eight cultivars with varying types and levels of resistance. The model correctly predicted relative yield losses (efficiency = 0.80, root mean square error of prediction = 13.25%, and bias = –0.36%) as a function of weather and the observed disease dynamics for a wide range of late blight epidemics. In addition to the evaluation of the predictive quality of the model, this article provides a dataset that describes the development of various late blight epidemics on potato as a function of weather conditions, fungicide regimes, and cultivar susceptibility. Following this evaluation, the Shtienberg model can be used with confidence in research and development programs to better manage potato late blight in France.

scholarly journals Disease Risk Assessment in Potato Cultures

2012 ◽  
Vol 69 (2) ◽  
Author(s):  
Andrei FLEȘERIU ◽  
Ioan OROIAN ◽  
Ioan BRAȘOVEAN ◽  
Constantin MIHAI - OROIAN ◽  
Daniela BORDEA
Keyword(s):  
Risk Assessment ◽  
Risk Management ◽  
Late Blight ◽  
Disease Risk ◽  
Climatic Factors ◽  
Treatment Strategies ◽  
Environmentally Friendly ◽  
Potato Late Blight ◽  
Assessment And Evaluation ◽  
Three Stages

The aim of our study was to elaborate a system of risk analyse against Phytophtora infestans  (Mont) de Bary attack degree in potato, in connection with climatic factors, in Transylvanian Plane. The risk assessment for potato late blight attack was conducted in tree experimental points located in the counties of Transylvanian Plane: Alba, Cluj, and Mureș. The data were statistically processed using STATISTICA v. 7.0 programme. The analyse of phytosanitary risk assessment consisted in three stages: initiation of the risk assessment, evaluation of the risk analyse, and risk management. Initiation of the risk assessment and evaluation of the ris analyses were performed. The risk management against Phytophtora infestans (Mont) de Bary attack degree in potato initiated involves treatment strategies, using environmentally friendly products, combined to culture appropriate works and strategies, as culture rotations, disinfestations of equipments, and appropriate storage of tubers used for seeding.

scholarly journals Climatic risk for potato late blight in the Andes region of Venezuela

2008 ◽  
Vol 65 (spe) ◽  
pp. 32-39 ◽  
Author(s):  
Beatriz Ibet Lozada Garcia ◽  
Paulo Cesar Sentelhas ◽  
Luciano Roberto Tapia ◽  
Gerd Sparovek
Keyword(s):  
Late Blight ◽  
Disease Risk ◽  
Risk Index ◽  
Disease Model ◽  
Great Part ◽  
Potato Late Blight ◽  
Geographical Information ◽  
Climatic Risk ◽  
The Andes ◽  
Andes Region

Potato is an important crop for Venezuelan agriculture. However, its production is highly affected by late blight (Phytophtora infestans), since weather is commonly favorable for this disease. The aim of this study was to determine the sowing dates of low climatic risk for potato late blight in the Andes region of Venezuela, with an agrometeorological disease model and geographical information system (GIS) tools. The disease model used in this study was developed by Hyre (1954) which requires daily rainfall and temperature data which were obtained from 106 weather stations, located at the States of Mérida, Táchira, and Trujillo, for a period of 31 years. Hyre's model was applied for all stations obtainig the following variables: number of disease favorable days (DFD); number of periods with ten consecutive favorable days, named disease occurrence (O); and number of sprays required for disease control (S). These variables were used to calculate the Maximum Risk Index (MRI) and the Probable Risk Index (PRI). The interpolation of these indexes was used to generate maps of climatic risk for each sowing date. MRI and PRI maps showed that the highest climatic risk for potato late blight occurrence was during the rainy season, from May to July, decreasing during dry and mid seasons. However, high disease risk variability was observed for all seasons. The maps generated by coupling an agrometeorological disease model and GIS also show that in great part of potato areas of Andes region the number of sprays could be reduced, but more investigations about that must be carried out.

scholarly journals A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

2020 ◽  
Vol 8 (7) ◽  
pp. 09-17
Author(s):  
Varsha M., Dr. Poornima B.
Keyword(s):  
Prediction Model ◽  
Minimum Temperature ◽  
Blast Disease ◽  
Weather Data ◽  
Maximum Temperature ◽  
Research Station ◽  
Disease Prediction ◽  
Features Selection ◽  
Ensemble Classifiers ◽  
Epidemic Disease

Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather  data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifier based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers  4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved  accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease.

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 Regional spore dispersal as a factor in disease risk warnings for potato late blight: A proof of concept

2009 ◽  
Vol 149 (3-4) ◽  
pp. 419-430 ◽  
Author(s):  
P. Skelsey ◽  
G.J.T. Kessel ◽  
A.A.M. Holtslag ◽  
A.F. Moene ◽  
W. van der Werf
Keyword(s):  
Late Blight ◽  
Disease Risk ◽  
Potato Late Blight ◽  
Proof Of Concept ◽  
Spore Dispersal

scholarly journals An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data

2021 ◽  
Vol 13 (21) ◽  
pp. 11893
Author(s):  
Abdul Rauf Bhatti ◽  
Ahmed Bilal Awan ◽  
Walied Alharbi ◽  
Zainal Salam ◽  
Abdullah S. Bin Humayd ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Prediction Models ◽  
Mean Squared Error ◽  
Weather Conditions ◽  
Energy System ◽  
Weather Data ◽  
Training Performance ◽  
Proposed Model ◽  
Artificial Neural Network Ann

In this work, an improved approach to enhance the training performance of an Artificial Neural Network (ANN) for prediction of the output of renewable energy systems is proposed. Using the proposed approach, a significant reduction of the Mean Squared Error (MSE) in training performance is achieved, specifically from 4.45 × 10−7 to 3.19 × 10−10. Moreover, a simplified application of the already trained ANN is introduced through which photovoltaic (PV) output can be predicted without the availability of real-time current weather data. Moreover, unlike the existing prediction models, which ask the user to apply multiple inputs in order to forecast power, the proposed model requires only the set of dates specifying forecasting period as the input for prediction purposes. Moreover, in the presence of the historical weather data this model is able to predict PV power for different time spans rather than only for a fixed period. The prediction accuracy of the proposed model has been validated by comparing the predicted power values with the actual ones under different weather conditions. To calculate actual power, the data were obtained from the National Renewable Energy Laboratory (NREL), USA and from the Universiti Teknologi Malaysia (UTM), Malaysia. It is envisaged that the proposed model can be easily handled by a non-technical user to assess the feasibility of the photovoltaic solar energy system before its installation.

scholarly journals Climatic Trends and Potato Late Blight Risk in the Upper Great Lakes Region

2005 ◽  
Vol 15 (3) ◽  
pp. 510-518 ◽  
Author(s):  
Kathleen M. Baker ◽  
William W. Kirk ◽  
Jeffrey M. Stein ◽  
Jeffrey A. Andresen
Keyword(s):  
Disease Management ◽  
Late Blight ◽  
Great Lakes ◽  
Disease Risk ◽  
Potato Late Blight ◽  
The United States ◽  
Dew Point ◽  
Great Lakes Region ◽  
Upper Great Lakes ◽  
Increased Risk

Concern in the agricultural community over observed and projected climate change has prompted numerous studies on the possible implications for crop yields. However, relatively little work has focused on disease management. In the upper Great Lakes region of the United States, late blight (Phytophthora infestans) of potato (Solanum tuberosum) is a temporally sporadic disease, occurring only when microclimate conditions within the canopy are favorable and inoculum is present. This and other studies indicate that historical climatological trends in the upper Great Lakes region have resulted in warmer and wetter growing season conditions, as well as local increases in precipitation totals and in the frequency of days with precipitation. Consequently, the risk of potato late blight is increasing. Historical trends in hourly weather variables and potato late blight risk as expressed by a modified Wallin disease severity value index were analyzed at seven regional weather stations from 1948–99. All sites showed significant trends in at least one of the risk estimates. While late blight risk was greatest at all locations in August, periods of increasing risk occurred across the region particularly during July. The increases in disease risk appeared to be associated with upward trends in dry bulb and dew point temperature at nearly all of the stations, especially during July and August. Increased risk of potato late blight has implications for extension agents and commercial horticulturists that include increased emphasis on grower education and application of integrated disease management techniques.

scholarly journals Opportunities for an improved potato late blight management in the Republic of Ireland: Field evaluation of the modified Irish Rules crop disease risk prediction model

2021 ◽  
Author(s):  
Mladen Cucak ◽  
Rafael de Andrade Moral ◽  
Rowan Fealy ◽  
Keith Lambkin ◽  
Steven Kildea
Keyword(s):  
Late Blight ◽  
Management Practices ◽  
Disease Risk ◽  
Control Strategies ◽  
Field Evaluation ◽  
Potato Late Blight ◽  
Current Standard ◽  
Republic Of Ireland ◽  
The Republic ◽  
Late Blight Management

Potato late blight remains the most significant disease threat of potato cultivation globally, often requiring expensive, time-consuming and environmentally unfriendly approaches to disease management. The goal of this research was to evaluate whether an estimation of potato late blight risk based on environmental factors can be reliably used to adjust the standard potato late blight management practices and the role of cultivar resistance under growing conditions and contemporary Phytophthora infestans populations in the Republic of Ireland. Using the modified Irish Rules model, it was possible to reduce fungicide usage by 58.7% on average, compared to current standard practices used by growers and without adversely compromising disease control and yield, with similar results achieved by the half dose programme. Host resistance levels were found to be correlated with a delay in the initiation of the epidemics, final foliar disease levels and reduction of fungicide usage. Disease levels on the highly resistant cultivars remained low and a clear selection pattern towards the P. infestans genotypes EU_13_A2 and EU_6_A1 was observed. An increase in the frequency of strains belonging to genotypes EU_13_A2 and EU_6_A1 was also observed to occur in the latter part of the trial growing seasons. Due to the increasingly dynamic nature of the population structure, associated with the continued evolution of the P. infestans population and the arrival of EU_36_A2 in the Republic of Ireland, routine population monitoring is required to ensure that potato late blight control strategies remain effective.

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