The use of multi-layer perceptrons (MLP) to determine the significance of climatic variables to the establishment of insect pest species is described. Results show that the MLP are able to learn to accurately predict the establishment of a pest species within a specific geographic region. Analysis of the MLP yielded insights into the contribution of the individual input variables and allowed for the identification of those variables that were most significant in either encouraging or inhibiting establishment
Quantitative methods for pest risk assessment combine sound statistical tools with sound ecological ...
This paper presents a novel methodology for multi-scale and multi-type spatial data integration in s...
Machine learning Artificial Intelligence (AI) hold the potential to benefit farmers and the environm...
The use of multi-layer perceptrons (MLP) to determine the significance of climatic variables to the ...
The use of multi-layer perceptrons (MLP) to determine the significance of climatic variables to the ...
Abstract The use of multi-layer perceptrons (MLP) to deter-mine the signicance of climatic variable...
A method is presented for applying a null-model analysis to the verification of the significance of ...
Insect pests now pose a greater threat to crop production given the recent emergence of insecticide ...
This document is the initial report on a systematic approach to the application of MLP to the aphid ...
To refine our knowledge and to adequately test hypotheses concerning theoretical and applied aspects...
A comparison of two artificial neural network methods for predicting the risk of insect pest species...
Not AvailableAim : Methodology : Results : Interpretation : Approaches to modelling pest populations...
The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely man...
Aim The purpose of this study was to improve understanding of the relationship between the spatial p...
The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely man...
Quantitative methods for pest risk assessment combine sound statistical tools with sound ecological ...
This paper presents a novel methodology for multi-scale and multi-type spatial data integration in s...
Machine learning Artificial Intelligence (AI) hold the potential to benefit farmers and the environm...
The use of multi-layer perceptrons (MLP) to determine the significance of climatic variables to the ...
The use of multi-layer perceptrons (MLP) to determine the significance of climatic variables to the ...
Abstract The use of multi-layer perceptrons (MLP) to deter-mine the signicance of climatic variable...
A method is presented for applying a null-model analysis to the verification of the significance of ...
Insect pests now pose a greater threat to crop production given the recent emergence of insecticide ...
This document is the initial report on a systematic approach to the application of MLP to the aphid ...
To refine our knowledge and to adequately test hypotheses concerning theoretical and applied aspects...
A comparison of two artificial neural network methods for predicting the risk of insect pest species...
Not AvailableAim : Methodology : Results : Interpretation : Approaches to modelling pest populations...
The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely man...
Aim The purpose of this study was to improve understanding of the relationship between the spatial p...
The appearance of pest insects can lead to a loss in yield if farmers do not respond in a timely man...
Quantitative methods for pest risk assessment combine sound statistical tools with sound ecological ...
This paper presents a novel methodology for multi-scale and multi-type spatial data integration in s...
Machine learning Artificial Intelligence (AI) hold the potential to benefit farmers and the environm...