A Hybrid Machine Learning Approach to Predictive Analytics in Precision Agriculture for Sustainable Crop Optimization

Abstract

Driven by data-enabled technologies, precision agriculture is evolving into an innovative way to increase agricultural productivity and sustainability. Conventional agricultural practices face several challenges, such as poor soil quality, inappropriate crop selection, improper resource utilization, and unpredictable climate conditions. To address these limitations, this paper proposes a hybrid machine learning framework for sustainable crop optimization and predictive analysis in precision agriculture. Agricultural datasets containing features like nutrients in the soil, temperatures, humidity, rainfall, and other parameters related to pH values are utilized to build a smart crop forecasting and recommendation system. Data preprocessing and normalization are applied to improve data quality and reduce inconsistency. K-Means clustering is applied to uncover hidden patterns in soil and environmental conditions, while Random Forest and XGBoost are used to generate accurate crop forecast and recommendations. The experimental results indicate that the hybrid model is better than existing machine learning approaches in terms of prediction accuracy, precision, recall, and F1-score with a prediction accuracy of 97.8%. In addition, sustainable agriculture is facilitated by efficient fertilizer usage, minimized manual labor, and efficient water usage. The suggested hybrid learning strategy presents an intelligent and scalable way forward for the current era of precision agriculture. The findings may help policymakers, farmers, and agricultural scientists implement data-driven methods to enhance agricultural yields.