Boosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agriculture

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dc.contributor.authorNagesh, O. Sri
dc.contributor.authorBudaraju, Raja Rao
dc.contributor.authorKulkarni, Shriram S.
dc.contributor.authorVinay M.
dc.contributor.authorAjibade, Samuel-Soma M.
dc.contributor.authorChopra, Meenu
dc.contributor.authorJawarneh, Malik
dc.contributor.authorKaliyaperumal, Karthikeyan
dc.date.accessioned2024-05-20T11:56:17Z
dc.date.available2024-05-20T11:56:17Z
dc.date.issued2024en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractDue to the limited availability of natural resources, it is essential that agricultural productivity keep pace with population growth. Despite unfavorable weather circumstances, this project's major objective is to boost production. As a consequence of technological advancements in agriculture, precision farming as a way for enhancing crop yields is gaining appeal and becoming more prevalent. When it comes to predicting future data, machine learning employs a number of methods, including the creation of models and the acquisition of prediction rules based on past data. In this manuscript, we examine various techniques to machine learning, as well as an automated agricultural yield projection model based on selecting the most relevant features. For the purpose of selecting features, the Grey Level Co-occurrence Matrix method is utilised. For classification, we make use of the AdaBoost Decision Tree, Artificial Neural Network (ANN), and K-Nearest Neighbour (KNN) algorithms. The data set that was used in this study is simply a compilation of information about a variety of topics, including yield, pesticide use, rainfall, and average temperature. This data collection consists of 33 characteristics or qualities in total. The crops soya beans, maze, potato, rice, paddy, wheat, and sorghum are included in this data collection. This data collection was made possible through the collaboration of the Food and Agriculture Organisation (FAO) and the World Data Bank, both of which make their data available to the public. The AdaBoost decision tree has achieved the highest level of accuracy possible when used to anticipate agricultural yield. Both the accuracy rate and the recall rate are quite high at 99 percent.en_US
dc.identifier.doi10.1007/s43621-024-00254-xen_US
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85191529508en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/7275
dc.identifier.urihttps://doi.org/10.1007/s43621-024-00254-x
dc.identifier.volume5en_US
dc.identifier.wosWOS:001222280900001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer Natureen_US
dc.relation.ispartofDiscover Sustainabilityen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBoosting, Crop Yield Prediction; Feature Selection; Gray Level Co-occurrence Matrix; AdaBoost Decision Tree; Accuracyen_US
dc.titleBoosting enabled efficient machine learning technique for accurate prediction of crop yield towards precision agricultureen_US
dc.typeArticleen_US

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