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

Author

Listed:
  • Dágila Melo Rodrigues

    (Department Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, Avenue Roraima, 1000, Camobi, Santa Maria 97105-900, Brazil)

  • Paulo Carteri Coradi

    (Department Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, Avenue Roraima, 1000, Camobi, Santa Maria 97105-900, Brazil
    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)

  • Newiton da Silva Timm

    (Department Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, Avenue Roraima, 1000, Camobi, Santa Maria 97105-900, Brazil)

  • Michele Fornari

    (Department Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, Avenue Roraima, 1000, Camobi, Santa Maria 97105-900, Brazil)

  • Paulo Grellmann

    (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)

  • Telmo Jorge Carneiro Amado

    (Department Agricultural Engineering, Rural Sciences Center, Federal University of Santa Maria, Avenue Roraima, 1000, Camobi, Santa Maria 97105-900, Brazil)

  • Paulo Eduardo Teodoro

    (Campus de Chapadão do Sul, Federal University of Mato Grosso do Sul, Chapadão do Sul 79560-000, Brazil)

  • Larissa Pereira Ribeiro Teodoro

    (Campus de Chapadão do Sul, Federal University of Mato Grosso do Sul, Chapadão do Sul 79560-000, Brazil)

  • Fábio Henrique Rojo Baio

    (Campus de Chapadão do Sul, Federal University of Mato Grosso do Sul, Chapadão do Sul 79560-000, Brazil)

  • José Luís Trevizan Chiomento

    (Department of Agronomy, University of Passo Fundo, Avenue Brasil Leste, 285, São José Passo Fundo 99052-900, Brazil)

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.

Suggested Citation

  • Dágila Melo Rodrigues & Paulo Carteri Coradi & Newiton da Silva Timm & Michele Fornari & Paulo Grellmann & Telmo Jorge Carneiro Amado & Paulo Eduardo Teodoro & Larissa Pereira Ribeiro Teodoro & Fábio , 2024. "Applying Remote Sensing, Sensors, and Computational Techniques to Sustainable Agriculture: From Grain Production to Post-Harvest," Agriculture, MDPI, vol. 14(1), pages 1-30, January.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:1:p:161-:d:1324219
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    References listed on IDEAS

    as
    1. Changwei Tan & Xinxing Zhou & Pengpeng Zhang & Zhixiang Wang & Dunliang Wang & Wenshan Guo & Fei Yun, 2020. "Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-15, March.
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