Meta Learning-Based Dynamic Ensemble Model for Crop Selection

Abstract

ABSTRACT Agricultural sector is working for optimal crop yield toward securing a sustainable food supply for the world. Fast growth in precision agriculture helps farmers to increase their yields by extending the era of machine-learning techniques. However, in organic and inorganic farming, predicting yield is an open issue that dominantly depends on the presence of soil nutrients. The lack of knowledge about the richness of land nutrients deals with the crop selection problem. Therefore, the proposed work extended the idea of a dynamic ensemble model for imbalanced multi-class nutrient data. In this work, an attempt is being made to include a novel customized voting strategy for deciding the final class output from the ensemble model. As an initial step, a well-known ranking technique, VIKOR, is applied over land nutrients to extract the most informative land samples. The rationale is to reduce the complexity of the ensemble model by determining only informative land samples for further classification. Furthermore, the meta-learning approach of dynamic ensemble selection accounts for multi-criterion-based competent classifier selection as meta-classifiers. These meta-classifiers decide on ensemble formation with the customized voting strategy to classify the right crop for the test land. To investigate nutrient richness, real-time soil and water nutrient data are collected from the soil testing laboratory, which covers different spatial data. Our experiments on six popular DES algorithms over nutrient data reveal the proposed algorithm’s outperformance in specificity, sensitivity, BCA, Multi-Area under Curve, and precision. Moreover, the lesser computational time of the proposed work indicates the model’s efficiency toward suitable crop selection.