Machine Learning in Precision Agriculture

Abstract

Introduction: Agriculture has been a fundamental aspect of human existence for thousands of years, dating back to around 9000 BC, when humans began transitioning from a nomadic lifestyle to settled farming [1]. Although agriculture is usually considered an “old-fashioned occupation,” recent advancements in machine learning have paved the way for transforming agricultural forecasting through intelligent crop and yield prediction systems. Despite this, many farmers still use traditional crops and yield prediction methods that are often manual, data-scarce, and inaccurate.

Objectives: This research investigates the potential benefits of integrating machine learning into agricultural forecasting, which can reduce the risk of crop failure and financial loss associated with traditional methods. The paper aims to make technology more accessible and actionable rather than just theoretical.

Methods: Previous studies have applied multiple machine learning algorithms, such as Random Forest and Support Vector Machine (SVM), for crop prediction. Despite promising results, little work has been done on combining both crop type and yield predictions into an integrative machine learning framework. This paper explores the utilization of ensemble-based models, namely XGBoost, LightGBM, and Random Forest, trained on two datasets to accurately predict crop type. Additionally, it examines the use of regression-based algorithms to predict crop yield accurately, employing feature selection and 5-fold cross-validation.

Results: The ensemble-based models returned an accuracy of 98% for both crop type prediction and yield forecasting, showing the effectiveness of multiple algorithms. The algorithms also achieved an accuracy of 99.8% on the less comprehensive dataset, while individual models such as CatBoost achieved varying accuracies highlighted in Table 1.

Conclusions: The findings obtained in this study can help future farmers reduce effective costs, increase production rates, and enhance crop yield by minimizing waste. This approach contributes towards the realization of AI-driven, data-centric precision agriculture, optimizes resource utilization, and supports intelligent decision-making in modern farming