Advanced Machine Learning Techniques for Optimizing Crop Selection in Precision Agricultural Systems

Crop selection is imperative for precision agriculture to ensure maximum output, minimize waste of resources, and promote sustainable agricultural practices. It is important to employ data-driven decision support tools for such issues as soil erosion, climate change, and water scarcity during cultivation. The research utilizes a sophisticated machine learning (ML)-based crop selection optimization framework employing a range of classification models such as Gaussian Naïve Bayes, Support Vector Classifier (SVC), K-Nearest Neighbours (KNN), Logistic Regression (LR), Decision Tree (DT), Extra Trees, Random Forest (RF), and Bagging. A big dataset of soil nutrients, temperature, humidity, rainfall, and pH was normalized with min-max scaling. Kernel Principal Component Analysis (KPCA) was applied for feature extraction to extract nonlinear relationships among variables. This enhanced the model's performance to detect latent patterns influencing crop suitability. RF Classifier outperformed all other models significantly, based on performance assessment. It had an accuracy of 99.88%, 100% training accuracy, 99.9% validation accuracy. RF's collective learning approach, which minimizes overfitting, enhances generalization, and manages high-dimensional data efficiently, is responsible for this better performance. Data-driven, sustainable, and highly precise agricultural planning is made possible by the research's demonstration of the effectiveness of ensemble-based ML techniques, particularly Random Forest, for crop selection in precision agriculture.