Deployment and Verification of Custom Autonomous Low-Budget IoT Devices for Image Feature Extraction in Wheat

Given the need for effective crop monitoring while reducing human intervention and workload, it is necessary to implement devices that operate autonomously and are durable. These devices must be capable of operating over long distances, operate with low energy requirements, and resist climatic adver...

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Autores: Martinez, F., Romaine, James B., Manzano Crespo, José María, Ierardi, Carmelina, Millán Gata, Pablo
Formato: artículo
Fecha de publicación:2024
País:España
Recursos:Universidad Loyola Andalucía
Repositorio:Brújula
OAI Identifier:oai:repositorio.uloyola.es:20.500.12412/6288
Acesso em linha:https://hdl.handle.net/20.500.12412/6288
Access Level:acceso abierto
Palavra-chave:Computer vision
Object detection
Smart agriculture
YOLOv8
YOLOv10
Spiking
Stubble
Wheat
Height
IoT
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spelling Deployment and Verification of Custom Autonomous Low-Budget IoT Devices for Image Feature Extraction in WheatMartinez, F.Romaine, James B.Manzano Crespo, José MaríaIerardi, CarmelinaMillán Gata, PabloComputer visionObject detectionSmart agricultureYOLOv8YOLOv10SpikingStubbleWheatHeightIoTGiven the need for effective crop monitoring while reducing human intervention and workload, it is necessary to implement devices that operate autonomously and are durable. These devices must be capable of operating over long distances, operate with low energy requirements, and resist climatic adversities and external factors such as dust and water to function effectively in rural areas. In this work, we introduce a low-cost autonomous IP67 IoT vision device designed and implemented in a real-world scenario. The device is used for automatic detection of wheat characteristics in order to optimise human resources when monitoring the growth and health of crops. Equipped with algorithms capable of capturing and processing images, the device has been designed to be computationally efficient, cost effective and power efficient. The device utilises the LoRaWAN communication protocol and requires 2.878W and has 3.5 months of autonomy. Furthermore, it leverages specific low computational algorithms that can operate with low-resolution images making them more effective. To test the device in a real-world scenario, an algorithm that measures the height of wheat was introduced using classical vision techniques, with 97% accuracy. Furthermore, the device incorporates an ad-hoc trained YOLOv8 and YOLOV10 object detection machine learning algorithm for the detection of spikes and stubble areas, which achieves a recall of 71.1% and a precision of 79.5% for the YOLOv8. In the case of the YOLOv10, it achieves a recall of 70% and a precision of 77 %. The results are validated by expert agronomists annotations and data collected via a custom created web platform for remote visualisation and decision making tasks.2024info:eu-repo/semantics/articlehttps://hdl.handle.net/20.500.12412/6288reponame:Brújulainstname:Universidad Loyola AndalucíaInglésJunta de Andalucía a través del proyecto oliVAr y IRRIGATE-PY20 RE 017 LOYOLA con Grant GOPO-SE-23-0001; y por la Agencia Andaluza de Cooperación Internacional Para el Desarrollo (AACID) a través del proyecto Reactivando con Grant 0INN005/2021http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:repositorio.uloyola.es:20.500.12412/62882026-06-24T12:48:37Z
dc.title.none.fl_str_mv Deployment and Verification of Custom Autonomous Low-Budget IoT Devices for Image Feature Extraction in Wheat
title Deployment and Verification of Custom Autonomous Low-Budget IoT Devices for Image Feature Extraction in Wheat
spellingShingle Deployment and Verification of Custom Autonomous Low-Budget IoT Devices for Image Feature Extraction in Wheat
Martinez, F.
Computer vision
Object detection
Smart agriculture
YOLOv8
YOLOv10
Spiking
Stubble
Wheat
Height
IoT
title_short Deployment and Verification of Custom Autonomous Low-Budget IoT Devices for Image Feature Extraction in Wheat
title_full Deployment and Verification of Custom Autonomous Low-Budget IoT Devices for Image Feature Extraction in Wheat
title_fullStr Deployment and Verification of Custom Autonomous Low-Budget IoT Devices for Image Feature Extraction in Wheat
title_full_unstemmed Deployment and Verification of Custom Autonomous Low-Budget IoT Devices for Image Feature Extraction in Wheat
title_sort Deployment and Verification of Custom Autonomous Low-Budget IoT Devices for Image Feature Extraction in Wheat
dc.creator.none.fl_str_mv Martinez, F.
Romaine, James B.
Manzano Crespo, José María
Ierardi, Carmelina
Millán Gata, Pablo
author Martinez, F.
author_facet Martinez, F.
Romaine, James B.
Manzano Crespo, José María
Ierardi, Carmelina
Millán Gata, Pablo
author_role author
author2 Romaine, James B.
Manzano Crespo, José María
Ierardi, Carmelina
Millán Gata, Pablo
author2_role author
author
author
author
dc.subject.none.fl_str_mv Computer vision
Object detection
Smart agriculture
YOLOv8
YOLOv10
Spiking
Stubble
Wheat
Height
IoT
topic Computer vision
Object detection
Smart agriculture
YOLOv8
YOLOv10
Spiking
Stubble
Wheat
Height
IoT
description Given the need for effective crop monitoring while reducing human intervention and workload, it is necessary to implement devices that operate autonomously and are durable. These devices must be capable of operating over long distances, operate with low energy requirements, and resist climatic adversities and external factors such as dust and water to function effectively in rural areas. In this work, we introduce a low-cost autonomous IP67 IoT vision device designed and implemented in a real-world scenario. The device is used for automatic detection of wheat characteristics in order to optimise human resources when monitoring the growth and health of crops. Equipped with algorithms capable of capturing and processing images, the device has been designed to be computationally efficient, cost effective and power efficient. The device utilises the LoRaWAN communication protocol and requires 2.878W and has 3.5 months of autonomy. Furthermore, it leverages specific low computational algorithms that can operate with low-resolution images making them more effective. To test the device in a real-world scenario, an algorithm that measures the height of wheat was introduced using classical vision techniques, with 97% accuracy. Furthermore, the device incorporates an ad-hoc trained YOLOv8 and YOLOV10 object detection machine learning algorithm for the detection of spikes and stubble areas, which achieves a recall of 71.1% and a precision of 79.5% for the YOLOv8. In the case of the YOLOv10, it achieves a recall of 70% and a precision of 77 %. The results are validated by expert agronomists annotations and data collected via a custom created web platform for remote visualisation and decision making tasks.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12412/6288
url https://hdl.handle.net/20.500.12412/6288
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Junta de Andalucía a través del proyecto oliVAr y IRRIGATE-PY20 RE 017 LOYOLA con Grant GOPO-SE-23-0001; y por la Agencia Andaluza de Cooperación Internacional Para el Desarrollo (AACID) a través del proyecto Reactivando con Grant 0INN005/2021
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:Brújula
instname:Universidad Loyola Andalucía
instname_str Universidad Loyola Andalucía
reponame_str Brújula
collection Brújula
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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