<![CDATA[Basal Stem Rot (BSR) disease caused by Ganoderma boninense is a major threat to oil palm productivity due to its destructive nature and the challenges associated with early-stage detection. To support sustainable production and mitigate significant yield losses, a system capable of classifying oil palm trees into healthy and infected categories is required. In this study, two deep learning approaches, namely CNN and SCNN, are applied to identify oil palm conditions based on UAV-derived imagery. While CNN is widely used for image-based detection tasks due to its capability to extract relevant visual representations, it is prone to overfitting during training. Therefore, SCNN is employed to address this issue by leveraging image similarity comparison mechanisms. Experimental results show that both methods achieve high classification accuracy, with SCNN outperforming CNN by achieving an accuracy of 96.48% compared to 95.644% for CNN. The superior performance of SCNN indicates its sensitivity to subtle visual differences between healthy and early-stage infected oil palm trees, enabling more reliable classification performance. Thus, SCNN is considered more effective for oil palm condition detection and contributes to reducing overfitting, resulting in improved model stability.]]>
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