Machine vision monitoring and detection system for large farm activities

In the country, it was only last March 2, 2019, that the ‘First Smart Farm in the Philippines’ has been inaugurated. The farm is owned by the government and not by any local farmer or farm owners. To hasten up the involvement of local farmers to the idea of smart farming, technologies that are easil...

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Main Author: De Ocampo, Anton Louise P.
Format: text
Language:English
Published: Animo Repository 2020
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Online Access:https://animorepository.dlsu.edu.ph/etd_doctoral/1416
https://animorepository.dlsu.edu.ph/context/etd_doctoral/article/2467/viewcontent/DeOcampo_AntonLouisePernez_11788879_MachineVisionMonitoringAndDetectionSystemForLargeFarmActivities_1_Redacted.pdf
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Institution: De La Salle University
Language: English
id oai:animorepository.dlsu.edu.ph:etd_doctoral-2467
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spelling oai:animorepository.dlsu.edu.ph:etd_doctoral-24672022-08-26T07:01:14Z Machine vision monitoring and detection system for large farm activities De Ocampo, Anton Louise P. In the country, it was only last March 2, 2019, that the ‘First Smart Farm in the Philippines’ has been inaugurated. The farm is owned by the government and not by any local farmer or farm owners. To hasten up the involvement of local farmers to the idea of smart farming, technologies that are easily deployable and less expensive can be introduced to them. One of the crux aspects in the implementation of smart farming is the monitoring system which observes the significant indicators that help farmers to identify what is needed, and where and when it should be applied. The machine vision monitoring and detection system developed in this research work consist primarily of three modules. First, the path planning module is designed to generate the mission-specific waypoints based on the user-defined area-of-interest (AOI) for the unmanned aerial vehicle intended for data acquisition. The second module is the farm activity monitor (FAM) which detects and counts farmers in the field and recognizes their activities. The final module is the crop health monitoring (CHM) which collects the field data related to vegetation fraction, weed estimate, nitrogen and chlorophyll contents of crops, and pest damage detection. Also, the crop schedule and the planned farm activities can be accessed. 2020-09-01T07:00:00Z text application/pdf https://animorepository.dlsu.edu.ph/etd_doctoral/1416 https://animorepository.dlsu.edu.ph/context/etd_doctoral/article/2467/viewcontent/DeOcampo_AntonLouisePernez_11788879_MachineVisionMonitoringAndDetectionSystemForLargeFarmActivities_1_Redacted.pdf Dissertations English Animo Repository Agricultural innovations Computer vision Electrical and Computer Engineering
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
language English
topic Agricultural innovations
Computer vision
Electrical and Computer Engineering
spellingShingle Agricultural innovations
Computer vision
Electrical and Computer Engineering
De Ocampo, Anton Louise P.
Machine vision monitoring and detection system for large farm activities
description In the country, it was only last March 2, 2019, that the ‘First Smart Farm in the Philippines’ has been inaugurated. The farm is owned by the government and not by any local farmer or farm owners. To hasten up the involvement of local farmers to the idea of smart farming, technologies that are easily deployable and less expensive can be introduced to them. One of the crux aspects in the implementation of smart farming is the monitoring system which observes the significant indicators that help farmers to identify what is needed, and where and when it should be applied. The machine vision monitoring and detection system developed in this research work consist primarily of three modules. First, the path planning module is designed to generate the mission-specific waypoints based on the user-defined area-of-interest (AOI) for the unmanned aerial vehicle intended for data acquisition. The second module is the farm activity monitor (FAM) which detects and counts farmers in the field and recognizes their activities. The final module is the crop health monitoring (CHM) which collects the field data related to vegetation fraction, weed estimate, nitrogen and chlorophyll contents of crops, and pest damage detection. Also, the crop schedule and the planned farm activities can be accessed.
format text
author De Ocampo, Anton Louise P.
author_facet De Ocampo, Anton Louise P.
author_sort De Ocampo, Anton Louise P.
title Machine vision monitoring and detection system for large farm activities
title_short Machine vision monitoring and detection system for large farm activities
title_full Machine vision monitoring and detection system for large farm activities
title_fullStr Machine vision monitoring and detection system for large farm activities
title_full_unstemmed Machine vision monitoring and detection system for large farm activities
title_sort machine vision monitoring and detection system for large farm activities
publisher Animo Repository
publishDate 2020
url https://animorepository.dlsu.edu.ph/etd_doctoral/1416
https://animorepository.dlsu.edu.ph/context/etd_doctoral/article/2467/viewcontent/DeOcampo_AntonLouisePernez_11788879_MachineVisionMonitoringAndDetectionSystemForLargeFarmActivities_1_Redacted.pdf
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