Ensemble Learning-Based Seed Germination Quality Prediction for Precision Forming

Abstract

Seed germination is one of the critical objectives of precision farming. The related contemporary research focuses on predictive analytics in precision farming, which has built significantly on machine learning strategies. The supervised learning models of these machine learning are widely adapted to perform seed germination quality prediction. However, the curse of dimensionality in the training corpus is the critical constraint that often deprives the prediction accuracy of contemporary models. The contribution of this manuscript is an ensemble learning-based germination quality prediction (EL-GQP) of the seeds in precision farming. The experimental results of the cross-validation performed on the benchmark dataset confirm the significance of the proposed model, which is scaled against the contemporary model.