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

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 Henrique Rojo Baio and José Luís Trevizan Chiomento
Additional contact information
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
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

Agriculture, 2024, vol. 14, issue 1, 1-30

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 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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