Learning from Data to Optimize Control in Precision Farming
Abstract
:1. Introduction
2. Statistical Inference and Machine Learning
2.1. Low-Order Statistics
2.2. Regression
2.3. Classification
2.4. Clustering
2.5. Artificial Neural Networks
2.6. Bayesian Time-Series Forecasting
3. Closing the Loop
4. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Method | Strengths | Weaknesses |
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MANOVA |
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Multiple Regression |
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Deep Neural Networks |
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Dynamic Bayesian Network |
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Support Vector Machine |
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k-means clustering | fast, simple. | Model order must be known in advance. |
DBSCAN clustering |
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Reinforcement Q-Learning |
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Kocian, A.; Incrocci, L. Learning from Data to Optimize Control in Precision Farming. Stats 2020, 3, 239-245. https://doi.org/10.3390/stats3030018
Kocian A, Incrocci L. Learning from Data to Optimize Control in Precision Farming. Stats. 2020; 3(3):239-245. https://doi.org/10.3390/stats3030018
Chicago/Turabian StyleKocian, Alexander, and Luca Incrocci. 2020. "Learning from Data to Optimize Control in Precision Farming" Stats 3, no. 3: 239-245. https://doi.org/10.3390/stats3030018
APA StyleKocian, A., & Incrocci, L. (2020). Learning from Data to Optimize Control in Precision Farming. Stats, 3(3), 239-245. https://doi.org/10.3390/stats3030018