Design and development of rice tungro disease detector / by Kimberly Dennisse T. Calimlim and Justin G. Primero.

By:

Calimlim, Kimberly Dennisse T [author]

Contributor(s):

Material type: Text

TextLanguage: English Publication details: Indang, Cavite : Cavite State University- Main Campus, 2019.Description: xvi, 144 pages : illustrations ; 28 cmContent type:

text

Media type:

unmediated

Carrier type:

volume

Subject(s):

DDC classification:

620.0042 C12 2019

Online resources:

Click here to view abstract and table of contents

Production credits:

College of Engineering and Information Technology (CEIT), Department of Computer and Electronics Engineering

Abstract: CALIMLIM, KIMBERLY DENNISSE T. and PRIMERO, JUSTIN G. Design and Development of Rice Tungro Disease Detector. Undergraduate Design Project. Bachelor of Science in Electronics Engineering. Cavite State University, Indang, Cavite. January 2020. Adviser: Engr. Nemilyn A. Fadchar. A design project aimed to design and develop a rice disease detector that can classify rice plant with tungro disease and healthy. The instrument evaluated the sample's leaf absorption as it was discovered that a leafs ability to absorb light intensity varies depending on its state of health. The device consisted of two major parts, the transmitter which compose of various light sources with different wavelength and the photodetector circuit which mainly compose of photodiode and Arduino Uno R3 (Microcontroller Unit). The study used Matrix Laboratory (MATLAB) App Designer for the purpose of creating a Graphical User Interface (GUI). Two classifier algorithms, namely Artificial Neural Network and Support Vector Machine, were used in the design project. Several LEDs were used in the design project which ranges from visible to infrared wavelength. It was also an objective of the researchers to find out if it was possible to use only one LED and which LED was the most efficient. The design project was evaluated at the Department of Agriculture and Food Engineering, Cavite State University, Indang, Cavite. Rice leaves infected with tungro and healthy samples were prepared and scanned. These samples were tested using the combination of all LEDs and each LED when performed individually. Aside from that, the two machine learning algorithms were also examined to determine which was more efficient for the project. Analysis of Variance was conducted, as well, to have knowledge of the significant difference of the treatments done. Lastly, the sensitivity of the design project was also evaluated. Based on the results of the evaluation, the design project accomplished the objectives. The device was able to perform according to the intention of the study. Apart from that, the design project was able to conclude that the combination of all LEDs using Artificial Neural Network was the most acceptable treatment.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Materials specified	URL	Status	Notes	Date due	Barcode
Theses / Manuscripts	Ladislao N. Diwa Memorial Library Theses Section	Non-fiction	620.0042 C12 2019 (Browse shelf(Opens below))		Link to resource	Room use only	DP-695		00079501

Design Project (Bachelor of Science in Electronics and Communication Engineering) Cavite State University.

Includes bibliographical references.

College of Engineering and Information Technology (CEIT), Department of Computer and Electronics Engineering

CALIMLIM, KIMBERLY DENNISSE T. and PRIMERO, JUSTIN G. Design and Development of Rice Tungro Disease Detector. Undergraduate Design Project. Bachelor of Science in Electronics Engineering. Cavite State University, Indang, Cavite. January 2020. Adviser: Engr. Nemilyn A. Fadchar.
A design project aimed to design and develop a rice disease detector that can classify rice plant with tungro disease and healthy. The instrument evaluated the sample's leaf absorption as it was discovered that a leafs ability to absorb light intensity varies depending on its state of health. The device consisted of two major parts, the transmitter which compose of various light sources with different wavelength and the photodetector circuit which mainly compose of photodiode and Arduino Uno R3 (Microcontroller Unit). The study used Matrix Laboratory (MATLAB) App Designer for the purpose of creating a Graphical User Interface (GUI). Two classifier algorithms, namely Artificial Neural Network and Support Vector Machine, were used in the design project. Several LEDs were used in the design project which ranges from visible to infrared wavelength. It was also an objective of the researchers to find out if it was possible to use only one LED and which LED was the most efficient. The design project was evaluated at the Department of Agriculture and Food Engineering, Cavite State University, Indang, Cavite. Rice leaves infected with tungro and healthy samples were prepared and scanned. These samples were tested using the combination of all LEDs and each LED when performed individually. Aside from that, the two machine learning algorithms were also examined to determine which was more efficient for the project. Analysis of Variance was conducted, as well, to have knowledge of the significant difference of the treatments done. Lastly, the sensitivity of the design project was also evaluated. Based on the results of the evaluation, the design project accomplished the objectives. The device was able to perform according to the intention of the study. Apart from that, the design project was able to conclude that the combination of all LEDs using Artificial Neural Network was the most acceptable treatment.

Submitted to the University Library 01/29/2020 DP-695

Click on an image to view it in the image viewer