Machine Learning for Crop Yield Prediction Using Sensor Data

This paper has proposed a machine-learning-based framework for crop yield prediction driven by multi-parameter agricultural sensor data, addressing the limitations of manual, experience-based yield estimation through continuous, automated environmental monitoring combined with data-driven modelling. The framework specifies a complete pipeline from sensor deployment through preprocessing, feature engineering, model training, and evaluation, and conceptually compares six regression algorithms — Linear Regression, Decision Tree, Random Forest, Support Vector Regression, Gradient Boosting/XGBoost, and Artificial Neural Networks — for their suitability to this problem. Because no field dataset was collected for this study, the work is presented explicitly as a proposed framework and experimental design rather than a report of measured results, and all illustrative figures and tables are labelled accordingly. If implemented with genuine sensor data, the framework is expected to support practical benefits for farmers and agricultural planners, including more timely irrigation and fertilization decisions and earlier identification of yield risk. Realizing this potential in practice will require addressing the limitations discussed in Section X, particularly sensor reliability, cost, and regional generalization, and pursuing the future directions outlined in Section XI, from edge AI to satellite-data fusion, to move the framework from a conceptual proposal toward a field-validated decision-support tool.

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