Please use this identifier to cite or link to this item: https://ir.swu.ac.th/jspui/handle/123456789/12761
ชื่อเรื่อง: Sugarcane Yield Grade Prediction using Random Forest and Gradient Boosting Tree Techniques
ผู้แต่ง: Charoen-Ung P.
Mittrapiyanuruk P.
Keywords: 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
วันที่เผยแพร่: 2018
บทคัดย่อ: 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.
URI: https://ir.swu.ac.th/jspui/handle/123456789/12761
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057769021&doi=10.1109%2fJCSSE.2018.8457391&partnerID=40&md5=f72e835daf3542148a329c19467b8ca5
Appears in Collections:Scopus 1983-2021

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