Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest

Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest

In recent years, agricultural remote sensing technology has made great progress. The availability of sensors capable of detecting electromagnetic energy and/or heat emitted by targets improves the pre-harvest process and therefore becomes an indispensable tool in the post-harvest phase. Therefore, w...

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Bibliographic Details
Published in:Agriculture (Basel) Vol. 14; no. 1; p. 161
Main Authors: Rodrigues, Dágila, Coradi, Paulo, Timm, Newiton, Fornari, Michele, Grellmann, Paulo, Amado, Telmo, Teodoro, Paulo, Teodoro, Larissa, Baio, Fábio, Chiomento, José
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-01-2024
Subjects:
agricultural monitoring
Agricultural production
Agriculture
Algorithms
Artificial intelligence
Crop diseases
Crop yield
Crop yields
Data collection
Decision making
Electric waves
Electromagnetic radiation
Electromagnetic waves
Electromagnetism
Food
Forecasting techniques
Grain
Grain industry
grain post-harvest
grain production
Harvest
Harvesting
Internet of Things
Methods
Monitoring
Plant diseases
prediction of agricultural results
Process planning
Production processes
Quality control
Quality management
Remote sensing
Remote sensors
Satellites
Sensors
Storage units
Sustainable agriculture
Unmanned aerial vehicles
Vegetation
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Summary:In recent years, agricultural remote sensing technology has made great progress. The availability of sensors capable of detecting electromagnetic energy and/or heat emitted by targets improves the pre-harvest process and therefore becomes an indispensable tool in the post-harvest phase. Therefore, we outline how remote sensing tools can support a range of agricultural processes from field to storage through crop yield estimation, grain quality monitoring, storage unit identification and characterization, and production process planning. The use of sensors in the field and post-harvest processes allows for accurate real-time monitoring of operations and grain quality, enabling decision-making supported by computer tools such as the Internet of Things (IoT) and artificial intelligence algorithms. This way, grain producers can get ahead, track and reduce losses, and maintain grain quality from field to consumer.
ISSN:2077-0472
2077-0472
DOI:10.3390/agriculture14010161