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 |
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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 |
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Subject |
pigeon pea
spiders regression wavelet–ANN weather variables |
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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 |
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Date |
2022-08-01T15:09:05Z
2022-08-01T15:09:05Z 2022-06-14 |
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Type |
Research Paper
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Identifier |
Not Available
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/73671 |
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Language |
English
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Relation |
Not Available;
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Publisher |
Agronomy
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