Design of an IoT-based Machine-Learning model to improve harvest performance on a farm

Galo Valverde, Kevin Barreiro

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

Agriculture plays a crucial role in the economy of any nation, as the need for food increases daily alongside the global population. However, traditional farming techniques are unable to meet the demand generated by the current population. This is why innovative approaches have been develop to automate the agricultural process. The objective of this article was to design an integrated IoT and Machine Learning model for monitoring soil pH, weather conditions, irrigation control, pests, and diseases on a farm to enhance production by early identification of issues that could impact these parameters; thereby improving crop yield, processing, and marketing through Internet of Things (IoT)-based crop cultivation.

Idioma originalInglés
Título de la publicación alojada2024 IEEE Colombian Conference on Communications and Computing, COLCOM 2024 - Proceedings
EditoresDiana Z. Briceno Rodriguez
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9798331504724
DOI
EstadoPublicada - 2024
Evento2024 IEEE Colombian Conference on Communications and Computing, COLCOM 2024 - Barranquilla, Colombia
Duración: 21 ago. 202424 ago. 2024

Serie de la publicación

Nombre2024 IEEE Colombian Conference on Communications and Computing, COLCOM 2024 - Proceedings

Conferencia

Conferencia2024 IEEE Colombian Conference on Communications and Computing, COLCOM 2024
País/TerritorioColombia
CiudadBarranquilla
Período21/08/2424/08/24

Nota bibliográfica

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© 2024 IEEE.

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