<![CDATA[Technological advances in modern agriculture have driven the development of automated systems to improve efficiency and accuracy in monitoring crop conditions. One promising approach is the use of digital image processing to automatically detect crop conditions. This study aims to examine image processing methods and algorithms that can be used in identifying various aspects of crops, such as pest and disease detection, determining maturity levels, and evaluating leaf and stem health. By using techniques such as image segmentation, feature extraction, and machine learning-based classification, this system is able to analyze crop images in real-time with a high degree of accuracy. The results of the study indicate that image processing has great potential in supporting an efficient, environmentally friendly, and sustainable precision farming system. The implementation of this technology is expected to help farmers in making faster and more precise decisions, thereby increasing the productivity and quality of agricultural products]]>
