<![CDATA[Smart farming has become a rapidly growing research area with the aim of increasing agricultural productivity and efficiency through advanced technologies. One of the key technologies in smart agriculture is image processing, which enables real-time monitoring and analysis of crop conditions. This article reviews image processing applications in smart agriculture, with a focus on the methods and techniques used for crop monitoring. Image processing methods discussed include pest and disease detection, measuring plant growth, as well as monitoring soil moisture and plant health. Image processing techniques such as convolution-based image analysis (Convolutional Neural Networks/CNNs), image segmentation, and pattern recognition are applied to obtain accurate and relevant information. Case studies and field experiments show that image processing can provide accurate and real-time data, enabling farmers to make more informed and efficient decisions. In conclusion, the application of image processing technology in smart agriculture has great potential to increase crop yields, reduce resource use, and advance sustainable agricultural practices.]]>
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