CROP DETECTION USING GIS TECHNOLOGIES AND REMOTE SENSING
Abstract
Crop detection and monitoring are vital components of modern agricultural practices aimed at optimizing land use, improving yield predictions, and enhancing resource management. This article provides a comprehensive review of the recent advances in crop detection using Geographic Information System (GIS) technologies and remote sensing. The integration of GIS and remote sensing has revolutionized the way crops are monitored, enabling the mapping and analysis of agricultural land at various spatial and temporal scales.
The article begins by outlining the fundamental principles of remote sensing and GIS and their application in crop monitoring. It then delves into the various remote sensing technologies, including optical, radar, and multispectral imaging, and their utility in crop mapping and classification. Furthermore, the article discusses the role of GIS in processing and analyzing remote sensing data, emphasizing its spatial analysis capabilities and integration with other geospatial datasets for comprehensive crop detection.
Moreover, the article highlights the emerging trends in the field, such as the use of unmanned aerial vehicles (UAVs) and hyperspectral imaging for high-resolution crop monitoring. It also addresses the integration of machine learning and artificial intelligence with remote sensing and GIS for automated crop detection and yield prediction. Additionally, the review covers the applications of crop detection in precision agriculture, land-use planning, and environmental monitoring.
The synthesized review provides insights into the challenges and opportunities associated with crop detection using GIS technologies and remote sensing, including data integration, accuracy assessment, and scalability. The article concludes by outlining future research directions and the potential impact of advancing technologies on sustainable agricultural practices, food security, and environmental conservation.
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