A predictive analytics model with Bayesian-Optimized Ensemble Decision Trees for enhanced crop recommendation
Behnaz Motamedi, Balázs Villányi
Researchers have been working on developing new effective, reliable, and environmentally friendly crop recommendation systems. This study introduces a thorough framework for predicting crop recommendations by combining sophisticated machine learning (ML) classifiers with a multi-class classification approach. The study is intended to (1) comprehensively assess the importance of environmental alterations and soil nutrient characteristics across a variety of crop classes, (2) develop effective predictive analytics models using the fine Gaussian support vector machine (FGSVM) and coarse k-nearest neighbors (Coa-KNN) algorithms, (3) reduce the dimension of feature vectors and minimize training time (FGPCASVM-CRP) approach through principal component analysis (PCA), (4) explore and analyze a Bayesian optimized ensemble decision tree for crop recommendation prediction (BOEDT-CRP) model based on assessment specifications, and (5) compare the proposed approach with multiple ML classifiers with various hyperparameter optimization, including FGSVM, coarse Gaussian SVM (Coa-GSVM), wide neural network (WNN), trilayered neural network (TNN), Fine k-nearest neighbors (FKNN), cosine k-nearest neighbors (Cos-KNN), bagged tree ensemble (BTE), and subspace discriminant ensemble (SDE). The proposed models throughout the training and testing stages reveal outstanding results, with comparable accuracy rates of 99.5%, precision rates of 99.49% and 99.55%, recall rates of 99.49% and 98.59%, and f1-scores of 99.5% and 99.54%. The findings support the conclusion that the proposed models can significantly support farmers in intelligent crop management and harvesting, leading to enhanced production and decreased reliance on human labor.
Year of publication: |
2024
|
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Authors: | Motamedi, Behnaz ; Villányi, Balázs |
Subject: | Bayesian-Optimized Ensemble Decision Trees | Hyper-parameter optimization | Machine learning classifiers | Multi-class classification | Principal component analysis | Entscheidungsbaum | Decision tree | Künstliche Intelligenz | Artificial intelligence | Klassifikation | Classification | Data Mining | Data mining | Prognoseverfahren | Forecasting model | Algorithmus | Algorithm | Theorie | Theory | Hauptkomponentenanalyse |
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