Analysing the Quality of Phaseolus Vulgaris Family of Legumes using Artificial Neural Network and “Bag of Features” Techniques
Mirafe R. Prospero1, Bryan G. Dadiz2
1Mirafe R. Prospero, Computer Studies Department, Lyceum of the Philippines-Laguna, Calamba City, Philippines,
2Bryan G. Dadiz, Computer Studies Department and Graduate School, Technological Institute of the Philippines – Manila.
Manuscript received on 05 July 2019 | Revised Manuscript received on 08 July 2019 | Manuscript published on 30 August 2019 | PP: 1270-1274 | Volume-8 Issue-10, August 2019 | Retrieval Number: I7540078919/2019©BEIESP | DOI: 10.35940/ijitee.I7540.0881019
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The Philippine Council for Agriculture, Forestry and National Resources Research and Development-Department of Science and Technology (PCAARRD-DOST) have recognized the importance of cultivating legumes as priority crop among others in the vegetable industry under the National Vegetable Research & Development Program. They have further emphasized the need for innovating the methods to improve the processes in terms of producing better quality of products. The study developed a prototype compiled application based on the trained and validated dataset using ANN (Artificial Neural Network) machine. The BoF (Bag of Features) technique was utilized for image features extraction in the SVM (Support Vector Machine) environment for quality classification of Phaseolus Vulgaris family of legumes. These are commonly cultivated in the Philippines. The combined methods yielded an accuracy of 90.2%. Index Terms:
Keywords: ANN, Bag of Features, compiled application, digital image processing, SVM
Scope of the Article: Image Processing and Pattern Recognition