Application of Remote Sensing and Machine Learning in Sustainable Agriculture

Claudiu Coman (), Ecaterina Coman, Vasile Gherheș, Anna Bucs and Dana Rad
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Claudiu Coman: Department of Social Sciences and Communication, Faculty of Sociology and Communication, Transilvania University of Brasov, 500036 Brasov, Romania
Ecaterina Coman: Department of Management and Economic Informatics, Faculty of Economic Sciences and Business Administration, Transilvania University of Brasov, 500036 Brasov, Romania
Vasile Gherheș: Department of Communication and Foreign Languages, Interdisciplinary Research Center for Communication and Sustainability, Politehnica University of Timisoara, 300006 Timisoara, Romania
Anna Bucs: Doctoral School of Social Sciences and Humanities, University of Craiova, 200585 Craiova, Romania
Dana Rad: Center of Research Development and Innovation in Psychology, Faculty of Educational Sciences Psychology and Social Work, Aurel Vlaicu University of Arad, 310045 Arad, Romania

Sustainability, 2025, vol. 17, issue 12, 1-18

Abstract: The growing demand for sustainable food production has driven significant advancements in modern agriculture, including increasing interest in Controlled Environment Agriculture (CEA), a high-tech solution designed to provide fresh, local, and organic products. Although the integration of various technologies in agriculture continues to expand, many opportunities remain to improve environmental performance and operational efficiency. Recent advancements in Remote Sensing (RS) and Machine Learning (ML) offer promising tools for enhancing resource efficiency, improving sustainability, and optimizing processes across various agricultural settings. This study presents a bibliometric analysis of the application of Remote Sensing and Machine Learning in agriculture, highlighting publication trends, influential research contributions, and emerging themes in this interdisciplinary field. While the majority of the analyzed literature addresses general agricultural modernization, the growing relevance of RS and ML in artificial climate facilities and controlled environments has been evident in more recent research. Furthermore, we explore how RS and ML technologies contribute to real-time monitoring, precision agriculture, and decision-making in agriculture.

Keywords: controlled environment agriculture; remote sensing; machine learning; sustainable food production; bibliometric analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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