Journal of Smart and Sustainable Farming
Understanding Crop Practices and Patterns Using Remote Sensing Technologies
Journal of Smart and Sustainable Farming
Received On : 25 November 2024
Revised On : 12 January 2025
Accepted On : 28 February 2025
Published On : 12 March 2025
Volume 01, 2025
Pages : 034-044
Abstract
In the last twenty years, scholars have employed remote sensing techniques for a diverse range of agricultural applications. These include the estimation of crop acreage, discrimination between different crops, assessment of soil moisture and crop condition, estimation of crop yield, implementation of precision agriculture practices, conducting soil surveys, managing agricultural water resources, and providing agro-meteorological and agro advisory services. The improved satellite data accessibility at improved geographical, spectral, and temporal resolutions has facilitated the emergence of novel applications in agriculture and contributed to economic expansion. One notable example is the usage of satellite data in insurance, enabling enhanced risk management strategies in the agricultural sector. Nevertheless, the effective use of satellite data in this perception necessitates a combination of technical proficiency about their limitations and capabilities, as well as a comprehension of their effects on the efficacy of risk mitigation initiatives. Given the potential lack of precision in agronomic terminology within the remote sensing literature, we provide a comprehensive categorization of prevalent agricultural practices, accompanied with detailed elucidations. Two key approaches that were identified are crop rotation and crop succession.
Keywords
Remote Sensing, Crop Practices, Crop Succession, Crop Patterns, Sequential Cropping, Normalized Difference Vegetation Index.
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Cite this Article
Prathap Mani, “Understanding Crop Practices and Patterns Using Remote Sensing Technologies”, Journal of Smart and Sustainable Farming, pp. 034-044, 12 March 2025, doi: 10.64026/JSSF/2025004.
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© 2025 Prathap Mani. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.