 Crop prediction based on soil and environmental characteristics using feature selection techniquesA. Suruliandi, G. Mariammal and S.P. Raja Mathematical and Computer Modelling of Dynamical Systems, 2021, vol. 27, issue 1, 117-140 Abstract: Earlier, crop cultivation was undertaken on the basis of farmers’ hands-on expertise. However, climate change has begun to affect crop yields badly. Consequently, farmers are unable to choose the right crop/s based on soil and environmental factors, and the process of manually predicting the choice of the right crop/s of land has, more often than not, resulted in failure. Accurate crop prediction results in increased crop production. This is where machine learning playing a crucial role in the area of crop prediction. Crop prediction depends on the soil, geographic and climatic attributes. Selecting appropriate attributes for the right crop/s is an intrinsic part of the prediction undertaken by feature selection techniques. In this work, a comparative study of various wrapper feature selection methods are carried out for crop prediction using classification techniques that suggest the suitable crop/s for land. The experimental results show the Recursive Feature Elimination technique with the Adaptive Bagging classifier outperforms the others. Date: 2021 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:nmcmxx:v:27:y:2021:i:1:p:117-140 Ordering information: This journal article can be ordered from DOI: 10.1080/13873954.2021.1882505 Access Statistics for this article Mathematical and Computer Modelling of Dynamical Systems is currently edited by I. Troch More articles in Mathematical and Computer Modelling of Dynamical Systems from Taylor & Francis Journals |
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