Paper Title
A Deep Learning-Based Mobile Application Framework for Classifying Rice Crop Diseases in Labo, Camarines Norte
Abstract
This study was conducted in response to current issues in the Department of Agriculture's rice crops in Camarines
Norte. The goal of this study was to create an architectural framework for classifying and identification of rice crop diseases.
The researcher used a qualitative approach in this study, employing focus group discussions. Literature review, flowcharting,
and block diagram were the system methodologies used in developing the architectural framework. Respondents were
selected using purposive sampling. The selected participants participated in the focus group discussion via an online
meeting. An in-depth informal interview with the respondents was also facilitated in order to gather additional information
for the development of an architectural framework. Based on the result of this study, it was discovered that there are several
issues concerning the identification and classification of rice crop diseases, such as insufficient information about the
specific disease, time consuming in waiting for the results of tested rice crop leaf sample, and difficulty in early disease
prevention, which leads to poor performance in early identification and classification, which also affects rice crop
production.
Keywords - Architectural Framework, Identifying, Classifying, Neural Network, Mobile Application
Author - Joyce C. Malubay, Melinda A. Beninsig
Published : Volume-11,Issue-1 ( Jan, 2024 )
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Published on 2024-03-30 |
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