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Remote Sensing and GIS for Weed Detection Using Enhanced Spatial Insights: A Review
Abstract
Remote sensing (RS) and Geographic Information Systems (GIS) technologies are receiving significant attention due to their combined potential to revolutionize spatial analysis and enhance understanding of complex environmental and geographic phenomena. This integration has vast applications, spanning from urban planning to engineering technologies. Remote sensing and GIS play essential roles in detecting and mapping weed infestations in agricultural fields, facilitating precise weed management and reducing herbicide use. Advancements include high-resolution imagery and machine learning algorithms, which enhance detection accuracy and efficiency. This chapter reviews and discusses the framework of weed detection based on different techniques, modes of data collection, and categories of weed features, including spectral and spatial features of weeds. Additionally, it discusses the different challenges encountered in the detection of weed.
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