Hybrid tree-fuzzy-rough set decision support for determining plant growth using vision-based descriptors

College

Gokongwei College of Engineering

Department/Unit

Manufacturing Engineering and Management

Document Type

Conference Proceeding

Source Title

Proceedings - 2019 6th NAFOSTED Conference on Information and Computer Science, NICS 2019

First Page

460

Last Page

465

Publication Date

12-1-2019

Abstract

© 2019 IEEE. Correct identification of the growth stage of crops contributes largely to the proper allocation and control of environmental factors for optimized harvestable products. Machine vision approaches for lettuce growth stage prediction has issues such as feature extraction, feature selection and dimensionality reduction for optimum classification accuracy, and robust framework for the prediction system. This paper presented a methodology of classifying lettuce growth stage using a Hybrid Decision Tree-Fuzzy- Rough Set. Vision features are extracted and subjected to dimensionality reduction using Decision Tree. The reduced inputs are used to design the Mamdani Fuzzy Inference system. Rough Set Theory is then applied to the Fuzzy Logic model to simplify the rules. Experimental results show a high performance in determining the growth stage of test lettuce images.

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Digitial Object Identifier (DOI)

10.1109/NICS48868.2019.9023829

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