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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publication2024 IEEE Colombian Conference on Communications and Computing, COLCOM 2024 - Proceedings
EditorsDiana Z. Briceno Rodriguez
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331504724
DOIs
StatePublished - 2024
Event2024 IEEE Colombian Conference on Communications and Computing, COLCOM 2024 - Barranquilla, Colombia
Duration: 21 Aug 202424 Aug 2024

Publication series

Name2024 IEEE Colombian Conference on Communications and Computing, COLCOM 2024 - Proceedings

Conference

Conference2024 IEEE Colombian Conference on Communications and Computing, COLCOM 2024
Country/TerritoryColombia
CityBarranquilla
Period21/08/2424/08/24

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

Keywords

  • Agriculture
  • Harvest
  • Innovation
  • IoT
  • machine learning
  • Production improvement

CACES Knowledge Areas

  • 217A Environmental Protection Technology
  • 118A Agricultural and livestock production

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