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Wavelet Decomposition and Machine Learning Technique for Predicting Occurrence of Spiders in Pigeon Pea

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Title Wavelet Decomposition and Machine Learning Technique for Predicting Occurrence of Spiders in Pigeon Pea
Not Available
 
Creator Ranjit Kumar Paul
Sengottaiyan Vennila
Md Yeasin
Satish Kumar Yadav
Shabistana Nisar
Amrit Kumar Paul
Ajit Gupta
Seetalam Malathi
Mudigulam Karanam Jyosthna
Zadda Kavitha
Srinivasa Rao Mathukumalli
Mathyam Prabhakar
 
Subject pigeon pea
spiders
regression
wavelet–ANN
weather variables
 
Description Not Available
Influence of weather variables on occurrence of spiders in pigeon pea across locations
of seven agro-climatic zones of India was studied in addition to development of forecast models
with their comparisons on performance. Considering the non-normal and nonlinear nature of time
series data of spiders, non-parametric techniques were applied with developed algorithm based on
combinations of wavelet–regression and wavelet–artificial neural network (ANN) models. Haar
wavelet filter decomposed each of the series to extract the actual signal from the noisy data. Prediction
accuracy of developed models, viz., multiple regression, wavelet–regression, and wavelet–ANN,
tested using root mean square error (RMSE) and mean absolute percentage error (MAPE), indicated
better performance of wavelet–ANN model. Diebold Mariano (DM) test also confirmed that the
prediction accuracy of wavelet–ANN model, and hence its use to forecast spiders in conjunction with
the values of pest–defender ratios, would not only reduce insecticidal sprays, but also add ecological
and economic value to the integrated pest management of insects of pigeon pea.
Not Available
 
Date 2022-08-01T15:09:05Z
2022-08-01T15:09:05Z
2022-06-14
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/73671
 
Language English
 
Relation Not Available;
 
Publisher Agronomy