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Agriculture
Volume 14
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10.3390/agriculture14010161
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Correction published on 11 July 2025, see Agriculture 2025, 15(14), 1490.
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Review

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

by
Dágila Melo Rodrigues
1,
Paulo Carteri Coradi
1,2,*,
Newiton da Silva Timm
1,
Michele Fornari
1,
Paulo Grellmann
2,
Telmo Jorge Carneiro Amado
1,
Paulo Eduardo Teodoro
3,
Larissa Pereira Ribeiro Teodoro
3,
Fábio Henrique Rojo Baio
3 and
José Luís Trevizan Chiomento
4
1
Department Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, Avenue Roraima, 1000, Camobi, Santa Maria 97105-900, Brazil
2
Laboratory of Post-Harvest (LAPOS), Campus Cachoeira do Sul, Federal University of Santa Maria, Highway Taufik Germano, 3013, Passo D’Areia, Cachoeira do Sul 96506-322, Brazil
3
Campus de Chapadão do Sul, Federal University of Mato Grosso do Sul, Chapadão do Sul 79560-000, Brazil
4
Department of Agronomy, University of Passo Fundo, Avenue Brasil Leste, 285, São José Passo Fundo 99052-900, Brazil
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(1), 161; https://doi.org/10.3390/agriculture14010161
Submission received: 13 December 2023 / Revised: 10 January 2024 / Accepted: 16 January 2024 / Published: 22 January 2024 / Corrected: 11 July 2025
(This article belongs to the Special Issue Agricultural Products Processing and Quality Detection)
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Abstract

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.
Keywords: grain production; grain post-harvest; agricultural monitoring; prediction of agricultural results grain production; grain post-harvest; agricultural monitoring; prediction of agricultural results

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MDPI and ACS Style

Rodrigues, D.M.; Coradi, P.C.; Timm, N.d.S.; Fornari, M.; Grellmann, P.; Amado, T.J.C.; Teodoro, P.E.; Teodoro, L.P.R.; Baio, F.H.R.; Chiomento, J.L.T. Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest. Agriculture 2024, 14, 161. https://doi.org/10.3390/agriculture14010161

AMA Style

Rodrigues DM, Coradi PC, Timm NdS, Fornari M, Grellmann P, Amado TJC, Teodoro PE, Teodoro LPR, Baio FHR, Chiomento JLT. Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest. Agriculture. 2024; 14(1):161. https://doi.org/10.3390/agriculture14010161

Chicago/Turabian Style

Rodrigues, Dágila Melo, Paulo Carteri Coradi, Newiton da Silva Timm, Michele Fornari, Paulo Grellmann, Telmo Jorge Carneiro Amado, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, and José Luís Trevizan Chiomento. 2024. "Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest" Agriculture 14, no. 1: 161. https://doi.org/10.3390/agriculture14010161

APA Style

Rodrigues, D. M., Coradi, P. C., Timm, N. d. S., Fornari, M., Grellmann, P., Amado, T. J. C., Teodoro, P. E., Teodoro, L. P. R., Baio, F. H. R., & Chiomento, J. L. T. (2024). Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest. Agriculture, 14(1), 161. https://doi.org/10.3390/agriculture14010161

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