Regression and artificial neural network modeling for the prediction of gray leaf spot of maize.
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Summary
Regression and artificial neural network modeling approaches were combined to develop models to predict the severity of gray leaf spot of maize, caused by Cercospora zeae-maydis, and the most useful predictor variables were hours of daily temperatures and hours of nightly relative humidity.
- Type
- article
- Published
- 2005-04-01
- Cited by
- 48
- References
- 37
- OpenAlex
- https://openalex.org/W2133854279
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:22870521
Keywords
Regression analysis, Regression, Cercospora, Statistics, Mean squared error
References
- Data modelling with neural networks: Advantages and limitations
- Epidemiology and predictive management of gray leaf spot of maize
- Some Comments on Cp
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- Squeezing the turnip with artificial neural nets.
- Model for Economic Analysis of Fungicide Usage in Hybrid Corn Seed Production.
- Development of a pathogen growth response model for the Virginia peanut leaf spot advisory program
- Neural network classification of tan spot and stagonospora blotch infection periods in a wheat field environment.
- Influence of tillage on development of gray leaf spot and number of airborne conidia of Cercospora zeae-maydis
- Survival of Cercospora zeae-maydis in corn residue in Ohio
- Overwintering and spore release of Cercospora zeae-maydis in corn debris in North Carolina.
- Effects of Coating, Deployment Angle, and Compass Orientation on Performance of Electronic Wetness Sensors During Dew Periods.
- Gray leaf spot of corn: a disease on the move.
- External growth, penetration, and development of Cercospora zeae-maydis in corn leaves.
- Management Practices to Reduce Gray Leaf Spot of Maize
- Infection cycle components and disease progress of gray leaf spot on field corn.
- Probabilities for profitable fungicide use against gray leaf spot in hybrid maize.
- Influence of environment and plant maturity on gray leaf spot of corn caused by Cercospora zeae-maydis.
- A Cerospora Leaf Spot Model for Sugar Beet: In Practice by an Industry.
Cited by
- Disease cycle approach to plant disease prediction.
- Warning models for coffee rust (Hemileia vastatrix Berkeley & Broome) by data mining techniques
- Artificial neural networks in vegetables: A comprehensive review
- Toward a Reliable Evaluation of Forecasting Systems for Plant Diseases: A Case Study Using Fusarium Head Blight of Wheat.
- Based on time series and RBF network plant disease forecasting
- ANN for prediction of Area and Production of Maize crop for Upper Brahmaputra Valley Zone of Assam
- MLP for prediction of area and rice production of upper Brahmaputra Valley zone of Assam
- Neural network model to predict deoxynivalenol (DON) in barley using historic and forecasted weather conditions
- Soil health through soil disease suppression: Which strategy from descriptors to indicators?
- A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks
- Influence of Host Resistance on Stewart's Wilt Forecasts and Probability of Exceeding Thresholds for Use of Seed-Treatment Insecticides on Sweet Corn.
- Risk of Natural Spread of Hymenoscyphus fraxineus with Environmental Niche Modelling and Ensemble Forecasting Technique
- Interpretation of commercial production information: A case study of lulo (Solanum quitoense), an under-researched Andean fruit
- How do Size and Resource Availability Control Aboveground Biomass Allocation of Tree Seedlings
- Analysis of Andean blackberry (Rubus glaucus) production models obtained by means of artificial neural networks exploiting information collected by small-scale growers in Colombia and publicly available meteorological data
- Regression and Neural Networks Models for Prediction of Crop Production
- Original Contribution COMPARISON OF DOUBLE DIGIT INDEX AND DISEASE SEVERITY IN DISEASE PROGRESS OF WHEAT SEPTORIOSIS (SEPTORIA TRITICI) USING ARTIFICIAL NEURAL NETWORK
- Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models
- Comparison of double digit index and disease severity in disease progress of wheat septoriosis (Septoria tritici) using artificial neural network.
- Utilization of Weather Data in Predicting Bread Loaf Volume by Neural Network Method
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