Crop Yield Prediction by Integrating Et-DP Dimensionality Reduction and ABP-XGBOOST Technique

Abstract

In the Indian economy, one of the most significant sectors is agriculture, which is the primary source of livelihood. In precision agriculture, the major intention is to ameliorate Crop Yield (CY) production along with quality by mitigating operational costs along with environmental pollution. To attain precision agriculture, Crop Yield Prediction (CYP) is highly significant. However, CYs become unpredictable owing to their reliance on huge dimension features like soil, climate, pesticides, diseases, et cetera. Therefore, for CYP, a Feature Selection (FS)-centric Machine Learning (ML) methodology is desired to provide precision agriculture along with mitigate the computational time. An Ensemble Threshold-centric Data Perturbation (DP) FS (ET-DPFS) dimensionality reduction centered Alpha-Beta Pruning centered Extreme Gradient Boosting (ABP-XGBOOST) CYP methodology is developed here. In this model, highly pertinent candidate features, which contain higher relevance regarding the class, lower redundancy amongst the features being selected, along with stay strong against noisy data, are selected. In the proposed work, firstly, by dealing with Nan values, missing values, outliers, categorical features, along with date-time variables, an exploratory evaluation is conducted over the data. After that, by utilizing the Analysis of Variance (ANOVA) test, the redundant features are taken away. Then, by employing the ET-DPFS, the relevant features are selected. ‘’3’ base learner FS methodologies like Median Absolute Deviation-centric LASSO (MAD-LASSO), Coefficient Vector-centric Mayfly Optimization (CV-MO), and HE weight initialization-centered Relief (HEwint-Relief) are included in this ET-DPFS. Lastly, by deploying the ABP-XGBOOST, the features being selected are trained along with verified. Experiential evaluation displays that the proposed methodology is highly reliable than the prevailing methodologies by achieving precise prediction with lower Values for Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).