Dynamic ensemble-based machine learning models for predicting pest populations
Early prediction of pest occurrences can enhance crop production, reduce input costs, and minimize environmental damage. Advances in machine learning algorithms facilitate the development of efficient pest alert systems. Furthermore, ensemble algorithms help in the utilization of several models rath...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Applied Mathematics and Statistics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fams.2024.1435517/full |
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| author | Ankit Kumar Singh Md Yeasin Ranjit Kumar Paul A. K. Paul Anita Sarkar |
| author_facet | Ankit Kumar Singh Md Yeasin Ranjit Kumar Paul A. K. Paul Anita Sarkar |
| author_sort | Ankit Kumar Singh |
| collection | DOAJ |
| description | Early prediction of pest occurrences can enhance crop production, reduce input costs, and minimize environmental damage. Advances in machine learning algorithms facilitate the development of efficient pest alert systems. Furthermore, ensemble algorithms help in the utilization of several models rather than being dependent on a single model. This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). Various abiotic factors such as minimum and maximum temperature, rainfall, and morning and evening relative humidity were incorporated into the model as exogenous variables. The proposed algorithms were compared with fixed-weighted and unweighted ensemble methods, and candidate machine learning models, using the pest population data for yellow stem borer (YSB) from two regions of India. Error metrics include the root mean square log error (RMSLE), root relative square error (RRSE), and median absolute error (MDAE), along with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This study concluded that the proposed dynamic ensemble algorithm demonstrated better predictive accuracy in forecasting YSB infestation in rice crops. |
| format | Article |
| id | doaj-art-c3e024a39d654005a6efe60b8a430b85 |
| institution | OA Journals |
| issn | 2297-4687 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Applied Mathematics and Statistics |
| spelling | doaj-art-c3e024a39d654005a6efe60b8a430b852025-08-20T02:39:08ZengFrontiers Media S.A.Frontiers in Applied Mathematics and Statistics2297-46872024-12-011010.3389/fams.2024.14355171435517Dynamic ensemble-based machine learning models for predicting pest populationsAnkit Kumar Singh0Md Yeasin1Ranjit Kumar Paul2A. K. Paul3Anita Sarkar4The Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi, IndiaICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaICAR-Indian Agricultural Statistics Research Institute, New Delhi, IndiaThe Graduate School, ICAR-Indian Agricultural Research Institute, New Delhi, IndiaEarly prediction of pest occurrences can enhance crop production, reduce input costs, and minimize environmental damage. Advances in machine learning algorithms facilitate the development of efficient pest alert systems. Furthermore, ensemble algorithms help in the utilization of several models rather than being dependent on a single model. This study introduces a dynamic ensemble model with absolute log error (ALE) and logistic error functions using four machine learning models—artificial neural networks (ANNs), support vector regression (SVR), k-nearest neighbors (kNN), and random forests (RF). Various abiotic factors such as minimum and maximum temperature, rainfall, and morning and evening relative humidity were incorporated into the model as exogenous variables. The proposed algorithms were compared with fixed-weighted and unweighted ensemble methods, and candidate machine learning models, using the pest population data for yellow stem borer (YSB) from two regions of India. Error metrics include the root mean square log error (RMSLE), root relative square error (RRSE), and median absolute error (MDAE), along with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm. This study concluded that the proposed dynamic ensemble algorithm demonstrated better predictive accuracy in forecasting YSB infestation in rice crops.https://www.frontiersin.org/articles/10.3389/fams.2024.1435517/fullaccuracydynamic ensemblemachine learningpest populationyellow stem borer |
| spellingShingle | Ankit Kumar Singh Md Yeasin Ranjit Kumar Paul A. K. Paul Anita Sarkar Dynamic ensemble-based machine learning models for predicting pest populations Frontiers in Applied Mathematics and Statistics accuracy dynamic ensemble machine learning pest population yellow stem borer |
| title | Dynamic ensemble-based machine learning models for predicting pest populations |
| title_full | Dynamic ensemble-based machine learning models for predicting pest populations |
| title_fullStr | Dynamic ensemble-based machine learning models for predicting pest populations |
| title_full_unstemmed | Dynamic ensemble-based machine learning models for predicting pest populations |
| title_short | Dynamic ensemble-based machine learning models for predicting pest populations |
| title_sort | dynamic ensemble based machine learning models for predicting pest populations |
| topic | accuracy dynamic ensemble machine learning pest population yellow stem borer |
| url | https://www.frontiersin.org/articles/10.3389/fams.2024.1435517/full |
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