A root crop classifier using convolutional neural network / Justine Carlo De Vega, Cherry Laurino, and Aidhel Olaes.
Material type:
TextPublisher: Rosario, Cavite : Cavite State University-CCAT Campus, 2019Description: xiv, 56 leaves : illustrations ; 28 cmSubject(s): Convolutional neural network | Root crops | Classification | Image processingLOC classification: UM QA 76.87 | D48 2019Summary: DE VEGA, JUSTINE CARLO D. LAURINO, CHERRY J. AND OLAES, AIDHEL D.A ROOT CROPS CLASSIFIER USING CONVOLUTIONAL NEURAL NETWORK. Undergraduate Thesis. Bachelor of Science in Computer Science. Cavite State University-CCAT Campus, Rosario, Cavite. June 2019. Adviser: Ms. Yvana Jardine Nocon.
The study, a root crops classification system using convolutional neural network, was conducted from August 2017 to March 2019. Specifically, the study aimed to: 1) obtain necessary requirements needed in classifying root crops, including its dataset, characteristics and information; 2) analyze the acquired requirements and identify the appropriate algorithm to be used; 3) design and develop the application that can run in a mobile platform, using an appropriate mobile application development tools; 4) test the accuracy and compatibility of the application; and 5) assess the application using modified evaluation instrument adapted from ISO-IEC 25010.
The developers used the rapid application development system to accomplish the root crops classification system. In this model, the analysis, design and implementation stage interacts continuously to create the prototype for testing and evaluation.
The system was evaluated by five respondents composed of IT experts of Cavite State University-CCAT. The root crops classifier gained a total score of 4.25/5, suggesting a very satisfactory performance. The application is able to meet its purpose of using image processing to detect and identify root crops available in the market.
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Cavite State University-Learning Resource Center | Thesis/Manuscript/Dissertation | TH | UM QA 76.87 D48 2019 (Browse shelf) | 1 | Available | T0005208 |
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| UM QA 76.76.A65 Ib23 2018 Seven Deadly Sins : a personal computer-based horror survival game / | UM QA 76.76.D47 At85 2019 Philippines three heroes : a mobile application of the lives of Rizal, Bonifacio and Aguinaldo / | UM QA 76.76.E95 M35 2017 Development of Talk to Teki : a knowledge-based application system / | UM QA 76.87 D48 2019 A root crop classifier using convolutional neural network / | UM QD 77 A48 2013 Alugbati (Basella rubra Linn.), dragon fruit (Hylocereus undatus), and ube (Dioscorea alata) as acid base indicators / | UM QK 861 S26 2008 Malunggay leaves extract : enhances hair growth on rodents / | UM QK 99.A1 P47 2011 Screening of the antibacterial activity of various parts of alibangbang plant against Staphylococcus aureus and pseudomonas aeruginosa / |
An Undergraduate Thesis (BSCS) -- Cavite State University-CCAT Campus, 2019.
Includes bibliographical references and appendices.
DE VEGA, JUSTINE CARLO D. LAURINO, CHERRY J. AND OLAES, AIDHEL D.A ROOT CROPS CLASSIFIER USING CONVOLUTIONAL NEURAL NETWORK. Undergraduate Thesis. Bachelor of Science in Computer Science. Cavite State University-CCAT Campus, Rosario, Cavite. June 2019. Adviser: Ms. Yvana Jardine Nocon.
The study, a root crops classification system using convolutional neural network, was conducted from August 2017 to March 2019. Specifically, the study aimed to: 1) obtain necessary requirements needed in classifying root crops, including its dataset, characteristics and information; 2) analyze the acquired requirements and identify the appropriate algorithm to be used; 3) design and develop the application that can run in a mobile platform, using an appropriate mobile application development tools; 4) test the accuracy and compatibility of the application; and 5) assess the application using modified evaluation instrument adapted from ISO-IEC 25010.
The developers used the rapid application development system to accomplish the root crops classification system. In this model, the analysis, design and implementation stage interacts continuously to create the prototype for testing and evaluation.
The system was evaluated by five respondents composed of IT experts of Cavite State University-CCAT. The root crops classifier gained a total score of 4.25/5, suggesting a very satisfactory performance. The application is able to meet its purpose of using image processing to detect and identify root crops available in the market.
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