1. Crop prediction based on soil and environmental characteristics using feature selection techniques.
- Author
-
Suruliandi, A., Mariammal, G., and Raja, S.P.
- Subjects
FEATURE selection ,AGRICULTURAL productivity ,CROP yields ,CROPS ,MACHINE learning - 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]
- Published
- 2021
- Full Text
- View/download PDF