Application of Remote Sensing and Machine Learning in Sustainable Agriculture

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.