Paper ID Title Keywords Author/s Affiliation Email 80 Smart Pest Identification and Management System Using Drone for Mango Farming Mango farming, multiclass support vector machine, gray-level co-occurrence matrix, pest management, mobile app, drone technology Gina S. Tumang, Giselle Y. Ayson, Patrick G. Zabala, Miguel S. Puno Department of Computer Engineering, Holy Angel University gtumang@hau.edu.ph Abstract This study assists the farmers by automatically identifying the pest through leaf and fruit markings with mobile and web application that generates pest prevention and control. It also foresees weather using API for location-based weather prediction in crop management specifically in pesticide application. The used of the drone technology helps to capture images of leaves at a high altitude. Using multi-SVM and GLCM in MATLAB, pest identification was determined by extracting contrast, kurtosis, skewness, entropy, and RMS. This project provides innovations on pest management of mango farms by storing data that can be used for future decisions through analytics.

MIS
9th Edition
ISBN:9781337681919
Author:BIDGOLI
Publisher:BIDGOLI
Chapter2: Computers: The Machines Behind Computing
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 Describe and indicate how will you intend to utilize the proposed project in your community

Paper ID
Title
Keywords
Author/s
Affiliation
Email
80
Smart Pest Identification and Management System Using
Drone for Mango Farming
Mango farming, multiclass support vector machine, gray-level co-occurrence
matrix, pest management, mobile app, drone technology
Gina S. Tumang, Giselle Y. Ayson, Patrick G. Zabala, Miguel S. Puno
Department of Computer Engineering, Holy Angel University
gtumang@hau.edu.ph
Abstract
This study assists the farmers by automatically identifying the pest through leaf and fruit markings
with mobile and web application that generates pest prevention and control. It also foresees
weather using API for location-based weather prediction in crop management specifically in
pesticide application. The used of the drone technology helps to capture images of leaves at a
high altitude. Using multi-SVM and GLCM in MATLAB, pest identification was determined by
extracting contrast, kurtosis, skewness, entropy, and RMS. This project provides innovations on
pest management of mango farms by storing data that can be used for future decisions through
analytics.
Transcribed Image Text:Paper ID Title Keywords Author/s Affiliation Email 80 Smart Pest Identification and Management System Using Drone for Mango Farming Mango farming, multiclass support vector machine, gray-level co-occurrence matrix, pest management, mobile app, drone technology Gina S. Tumang, Giselle Y. Ayson, Patrick G. Zabala, Miguel S. Puno Department of Computer Engineering, Holy Angel University gtumang@hau.edu.ph Abstract This study assists the farmers by automatically identifying the pest through leaf and fruit markings with mobile and web application that generates pest prevention and control. It also foresees weather using API for location-based weather prediction in crop management specifically in pesticide application. The used of the drone technology helps to capture images of leaves at a high altitude. Using multi-SVM and GLCM in MATLAB, pest identification was determined by extracting contrast, kurtosis, skewness, entropy, and RMS. This project provides innovations on pest management of mango farms by storing data that can be used for future decisions through analytics.
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