Nondestructive Technology for Real-Time Monitoring and Prediction of Soybean Quality Using Machine Learning for a Bulk Transport Simulation-Reference-Cited by-同舟云学术

Nondestructive Technology for Real-Time Monitoring and Prediction of Soybean Quality Using Machine Learning for a Bulk Transport Simulation

Published:2023-02-01 Issue:3 Volume:23 Page:3028-3040
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Jaques Lanes Beatriz Acosta¹^ORCID,Coradi Paulo Carteri²^ORCID,Lutz Everton¹^ORCID,Teodoro Paulo Eduardo³^ORCID,Jaeger Douglas Vinicius²,Teixeira Angelico Loreto¹^ORCID

Affiliation:

1. Department of Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, Santa Maria, Rio Grande do Sul, Brazil

2. Department of Agricultural Engineering, Laboratory of Postharvest, Federal University of Santa Maria, Cachoeira do Sul, Rio Grande do Sul, Brazil

3. Department of Agronomy, Federal University of Mato Grosso do Sul, Campus de Chapadão do Sul, Chapadão do Sul, Brazil

Funder

CAPES

CNPq

FAPERGS-RS

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10032973/09991151.pdf?arnumber=9991151

Reference67 articles.

1. Modelling respiration rate of soybean seeds ( Glycine max (L.)) in hermetic storage

2. Correlation of physical properties for establishments of standardized groups of soybean seed technologies in post-harvest

3. Nondestructive Measurement of Moisture Content Using a Parallel-Plate Capacitance Sensor for Grain and Nuts

4. Carbon Dioxide Respiration Rates in Wheat at Various Temperatures and Moisture Contents

5. Soybean seed storage: Packaging technologies and conditions of storage environments

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2. Modeling of Typically Shaped Corn Seeds and Calibration of the Coefficient of Rolling Friction;Agronomy;2023-06-09

3. Sensor-cable-probe and sampler for early detection and prediction of dry matter loss and real-time corn grain quality in transport and storage;Scientific Reports;2023-04-07

4. A Model for Predicting and Grading the Quality of Grain Storage Processes Affected by Microorganisms under Different Environments;International Journal of Environmental Research and Public Health;2023-02-25