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AI and machine learning tools in plantation mapping: potentials of high-resolution satellite data.

Authors :
Segar, Nithya
Kaliyaperumal, Ragunath
Pazhanivelan
Kumaraperumal
Latha
Muthumanickam
Jagadeeshwaran
Source :
Agricultural Science & Technology (1313-8820); Jun2024, Vol. 16 Issue 2, p3-16, 14p
Publication Year :
2024

Abstract

Plantation mapping plays a vital role in agriculture, forestry, and land management. The integration of Artificial intelligence and Machine learning techniques with high-resolution satellite data has revolutionized the accuracy and efficiency of plantation mapping. Utilizing AI and machine learning tools for plantation mapping offers a transformative approach to efficient and accurate land management. These technologies enable automated analysis of satellite imagery and other geospatial data, facilitating rapid and precise identification of plantations, crop health assessment, and yield predictions. The integration of AI enhances the mapping process, providing valuable insights for sustainable agriculture, resource optimization, and environmental monitoring. The application of these advanced tools in plantation mapping represents a significant leap towards data-driven and environmentally conscious land management practices. It presents a promising advancement in agricultural practices. By leveraging these technologies for automated analysis of satellite imagery and geospatial data, accurate and timely mapping of plantations becomes feasible. The use of AI and ML tools in Plantation mapping, challenges in integration, the possible solutions and its future prospects are reviewed in this paper not only to enhance efficiency but also to offer insights into crop health, aiding in precision agriculture and resource optimization. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13138820
Volume :
16
Issue :
2
Database :
Complementary Index
Journal :
Agricultural Science & Technology (1313-8820)
Publication Type :
Academic Journal
Accession number :
178843416
Full Text :
https://doi.org/10.15547/ast.2024.02.012