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Crop prediction based on soil and environmental characteristics using feature selection techniques.
- Source :
-
Mathematical & Computer Modelling of Dynamical Systems . Dec 2021, Vol. 27 Issue 1, p117-140. 24p. - Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *FEATURE selection
*AGRICULTURAL productivity
*CROP yields
*CROPS
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 13873954
- Volume :
- 27
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Mathematical & Computer Modelling of Dynamical Systems
- Publication Type :
- Academic Journal
- Accession number :
- 154690240
- Full Text :
- https://doi.org/10.1080/13873954.2021.1882505