Using artificial neural networks to determine the relative contribution of abiotic factors influencing the establishment of insect pest species

2008 ◽  
Vol 3 (1) ◽  
pp. 64-74 ◽  
Author(s):  
Michael J. Watts ◽  
S.P. Worner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Abiotic Factors ◽  
Insect Pest ◽  
Pest Species ◽  
Relative Contribution ◽  
Factors Influencing ◽  
Artificial Neural

scholarly journals The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 652 ◽  
Author(s):  
Sławomir Francik ◽  
Sławomir Kurpaska
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square ◽  
Internal Temperature ◽  
Data Set ◽  
External Temperature ◽  
Factors Influencing ◽  
Testing Data ◽  
Artificial Neural ◽  
Hidden Layer

It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

scholarly journals Analysis of factors influencing deployment of fire suppression resources in Spain using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 138-145 ◽  
Author(s):  
S Costafreda-Aumedes ◽  
A Cardil ◽  
DM Molina ◽  
SN Daniel ◽  
R Mavsar ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fire Suppression ◽  
Factors Influencing ◽  
Artificial Neural

Instar Determination of Blaptostethus pallescens (Hemiptera: Anthocoridae) Using Artificial Neural Networks

2019 ◽  
Vol 113 (1) ◽  
pp. 50-54
Author(s):  
Daiane das Graças Carmo ◽  
Elizeu de Sá Farias ◽  
Thiago Leandro Costa ◽  
Elenir Aparecida Queiroz ◽  
Moysés Nascimento ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Head Width ◽  
Correct Identification ◽  
Pest Species ◽  
Tropical Regions ◽  
Artificial Neural ◽  
Input Variables ◽  
Hidden Layer

Abstract Blaptostethus pallescens Poppius is an important predator of vegetable pests in tropical regions. The correct identification of the stages of the life cycle of predatory species is crucial, since different stages may present different rates of pest consumption. Artificial neural networks (ANNs) are computational tools with a structure based on the human brain. With applications in several fields, ANNs have been applied in pest management for identification of pest species, spatial distribution modeling, and insect forecasting. The objective of this study was to apply ANNs as a method for the instar determination of B. pallescens using three morphometric measures (head width, body width, and body length). Cluster analysis was performed to categorize the insects in instars according to the morphometric variables. Subsequently, the ANNs were trained for instar determination using the morphometric measures as input variables. The ANNs tested (with 2, 4, 6, 8, 10, and 12 hidden neurons) provided proper data fitting (R2 > 98%). However, due to the parsimony principle, the network with hidden layer size 6 was selected. This study shows the successful application of ANNs in the instar determination of B. pallescens, which would not be possible using classical methods.

scholarly journals Investigation into the pharmacokinetic–pharmacodynamic model of Zingiberis Rhizoma/Zingiberis Rhizoma Carbonisata and contribution to their therapeutic material basis using artificial neural networks

RSC Advances ◽  
2017 ◽  
Vol 7 (41) ◽  
pp. 25488-25496 ◽  
Author(s):  
Sujuan Zhou ◽  
Jiang Meng ◽  
Bo Liu
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Drug Efficacy ◽  
Pharmacodynamic Model ◽  
Relative Contribution ◽  
Material Basis ◽  
Artificial Neural

A PK/PD model of ZR/ZRC based on ANN was utilized to evaluate relative contribution of concentration to its drug efficacy.

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