Development of interactive application for classification of Artocarpus Species
The demand of automated tools has been increasing regarding to the lack of people that expert in taxonomist. The aim of this research is to identify the classification of Artocarpus species by using interactive application and the effectiveness of the interactive application for classification of Ar...
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| Format: | Undergraduate Final Project Report |
| Published: |
2020
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| Online Access: | http://discol.umk.edu.my/id/eprint/4077/ |
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| Summary: | The demand of automated tools has been increasing regarding to the lack of people that expert in taxonomist. The aim of this research is to identify the classification of Artocarpus species by using interactive application and the effectiveness of the interactive application for classification of Artocarpus species. This study focusses on identification and classification of selected Artocarpus species which are A. heterophyllus, A. altilis, A. integer and A. odoratissirnus belong to genus Artocarpus and family Moraceae through their morphological and features extraction by using image processing method. Support Vector Machine (SVM) will be used to get the highest accuracy for the classification of Artocarpus species. The combination of Prewitt algorithm, Canny alogorithm, Gray-Level co-occurrence matrix will be used in SVM. This study capable to provide the results for current accuracy data representation of the selected Artocarpus species. The development of Graphical User Interface (GUI) for classification of Artocarpus species help user to identify and differentiate the species in faster and easier way especially botanist, taxonomist, and researcher. This system can increase the accuracy and speed of the processing and extraction of features from digital images of leaves samples. A Graphical User Interface utilizes a combination of devices and technologies to give a platform where users can interact with and producing information. This study shown the comparative results by using different algorithms which are GLCM, Canny, and Prewitt for all data samples. Support Vector Machines has been provided a good performance as an identification model and produce outstanding results for classification of Artocarpus species. GLCM achieved the highest accuracy which recorded 89% of the overall accuracy for the classification of Artocarpus species where A.altilis and A.odoratissimus achieved 100% accuracy, A. heterophyllus achieved 87% accuracy, and ofA.integer achieved 67% accuracy. The Graphical User Interface (GUI) has been designed successfully to classify the plant species belong to genus Artocarpus. |
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