<![CDATA[The health of oil palm plants can be visually assessed through changes in leaf color, which reflect the plant's physiological condition. Leaf color serves as a critical, non-destructive indicator for evaluating plant health. This study aims to develop an innovative method for detecting oil palm leaf health using MATLAB-based digital image processing techniques. The process begins with leaf image acquisition, followed by pre-processing to enhance image quality, and then color space conversion from RGB to HSV. The analysis focuses on the Hue and Saturation components, which represent the leaf's color tone and intensity. Two sample images—healthy and unhealthy leaves—are compared. The results demonstrate that healthy leaves exhibit higher average Hue and Saturation values compared to unhealthy ones, providing a key parameter for automated leaf condition classification. This study introduces a cost-effective system adaptable for small-scale farmers' plantations, offering an effective, efficient, and economical solution. This approach shows significant potential for implementation in automated plant health monitoring systems and further development for precision agriculture, particularly in oil palm plantations, to enhance productivity and sustainability in modern agriculture.]]>
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