The Harumanis mango holds a special status in Perlis as its delightful flavor and aromatic fragrance. However, the formidable challenges in cultivating Harumanis trees have led to significant losses, impacting its robust sales in Malaysia. This might cause a huge impact on Perlis economy since Harumanis is one of the most commercial products in Perlis. Therefore, Harumanis has been remarked by the plantation workers. A high-quality Harumanis tree thrives when nourished with rich nutrients, resulting in abundant fruit production compared to less nourished trees. The tree’s health can be gauged by observing the condition of its leaves. The healthy leaves shape is scatter while the unhealthy leaves is gathered. The moment features such as central moment, Hu’s moment, Zernike moment and Affine moment invariants of the images will then be extracted and classified by using extreme learning machine (ELM) and on-line sequential extreme learning machine (OS-ELM), to determine whether the it is healthy or unhealthy leaf. The total of 1500 healthy and unhealthy leaves images in the dataset. Initial image resizing to 500*500 pixels and application of Modified Linear Contrast Stretching (MLCS) during the image preprocessing. Then, Fast k-means (FKM) clustering algorithm had been used for image segmentation based on the Saturation colour components. Next, the segmented images were allocated into both training and testing datasets, maintaining a ratio of 60% for training and 40% for testing. Lastly, the dataset is classified by ELM and OS-ELM. The result shows that OS-ELM archive the highest testing accuracy with 91.67% among all.

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