| G. Vonitsanos, E.-E. Economopoulou, S. Sioutas, A. Kanavos, Ph. Mylonas |
| Sensor-Driven Ensemble Learning for Crop Recommendation and Disease Prediction in Precision Agriculture |
| 20th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2025), 27-28 November 2025, Mystras, Greece |
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ABSTRACT
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| Agriculture plays a vital role in ensuring global food security, yet it faces growing challenges from climate change, resource limitations, and increasing demand. This paper presents an ensemble learning framework that leverages Internet of Things (IoT) sensor data for crop recommendation and plant disease prediction in precision agriculture. Environmental parameters including temperature, humidity, rainfall, soil pH, and nutrient levels are modeled using Random Forest, Neural Networks, and Logistic Regression classifiers. Experimental evaluation on a publicly available dataset shows that Random Forest achieves superior performance, reaching 89.20% accuracy and the highest F1-score, outperforming baseline models in robustness and interpretability. The integration of Apache Spark enables scalable and near real-time analysis, making the approach suitable for practical deployment. By combining ensemble learning with sensor-driven environmental monitoring, the proposed framework supports sustainable, interpretable, and data-driven agricultural decision-making for farmers, researchers, and policymakers.
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| 27 November , 2025 |
| G. Vonitsanos, E.-E. Economopoulou, S. Sioutas, A. Kanavos, Ph. Mylonas, "Sensor-Driven Ensemble Learning for Crop Recommendation and Disease Prediction in Precision Agriculture", 20th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP 2025), 27-28 November 2025, Mystras, Greece |
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