Publication: Sugarcane Yield Grade Prediction using Random Forest and Gradient Boosting Tree Techniques
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Issued Date
2018
Resource Type
File Type
application/pdf
Other identifier(s)
2-s2.0-85057769021
Rights Holder(s)
Scopus
Bibliographic Citation
Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018.
Suggested Citation
Charoen-Ung P., Mittrapiyanuruk P. Sugarcane Yield Grade Prediction using Random Forest and Gradient Boosting Tree Techniques. Proceeding of 2018 15th International Joint Conference on Computer Science and Software Engineering, JCSSE 2018.. doi:10.1109/JCSSE.2018.8457391 Retrieved from: https://hdl.handle.net/20.500.14740/5854
Author(s)
Abstract
This paper presents a machine learning based model for predicting the sugarcane yield grade of an individual plot. The dataset used in this work is obtained from a set of sugarcane plots around a sugar mill in Thailand. The features used in the prediction consist of the plot characteristics (soil type, plot area, groove width, plot yield/ yield grade of the last year), sugarcane characteristics (cane class and type), plot cultivation scheme (water resource type, irrigation method, epidemic control method, fertilizer type/formula) and rain volume. We use two predictive algorithms: (i) random forest classification, and (ii) gradient boosting tree classification. The accuracies of our machine learning based predictive methods are 71.83% and 71.64%, respectively. Meanwhile, the accuracies of two non-machine-learning baselines are 51.52% (using the actual yield of the last year as the prediction) and 65.50% (the target yield of each plot is manually predicted by human expert), respectively. This shows that our work is accurate enough to be applied for decision making of sugar mill operation planning. © 2018 IEEE.
Subject(s)
Adaptive boosting
Artificial intelligence
Decision making
Decision trees
Disease control
Forestry
Learning systems
Software engineering
Sugar factories
Water resources
Epidemic control
Grade predictions
Gradient boosting
Irrigation methods
Predictive algorithms
Predictive methods
Random forest classification
Random forests
Forecasting
Artificial intelligence
Decision making
Decision trees
Disease control
Forestry
Learning systems
Software engineering
Sugar factories
Water resources
Epidemic control
Grade predictions
Gradient boosting
Irrigation methods
Predictive algorithms
Predictive methods
Random forest classification
Random forests
Forecasting