<![CDATA[Indonesia is an agrarian country heavily reliant on agricultural commodities. Apples, in particular, hold significant importance in this agricultural context, making a substantial contribution to the nation's agricultural prosperity. However, the agricultural sector faces challenges, notably in the form of diseases like Alternaria Leaf Spot, which have the potential to adversely affect crop yields. This research introduces a system for detecting Alternaria Leaf Spot disease on apple leaves, utilizing RGB color space and mathematical morphology operations. Implementing a GUI-based approach through Matlab software, the system efficiently detects infected areas, achieving good performance with a precision value of 96.22% and a recall of 88.74%. The color-based segmentation process, combined with morphological operations, results in the generation of bounding boxes around infected areas. Evaluation using a dataset of 45 apple leaf images demonstrates success in detecting and quantifying leaf spots. These positive outcomes underscore the practical potential of the system in automating efficient monitoring of apple plant diseases, paving the way for further developments in image-based plant disease detection.]]>
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