Monitoring and predicting corn grain quality on the transport and post-harvest operations in storage units using sensors and machine learning models

Monitoring and predicting corn grain quality on the transport and post-harvest operations in storage units using sensors and machine learning models

Abstract Monitoring the intergranular variables of corn grain mass during the transportation, drying, and storage stages it possible to predict and avoid potential grain quality losses. For monitoring the grain mass along the transport, a probe system with temperature, relative humidity, and carbon...

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Bibliographic Details
Main Authors: Dágila Melo Rodrigues, Paulo Carteri Coradi, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro, Rosana dos Santos Moraes, Marisa Menezes Leal
Format: Article
Language:English
Published: Nature Portfolio 2024-03-01
Series:Scientific Reports
Subjects:
Artificial intelligence
Grain quality
Loss reduction and grain conservation
Post-harvest technologies
Predictive models
Online Access:https://doi.org/10.1038/s41598-024-56879-5

Internet

https://doi.org/10.1038/s41598-024-56879-5

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