UAV Image Classification of Oil Palm Plants Using CNN Ensemble Model

Basal Stem Rot (BSR), caused by Ganoderma boninense, is one of the most destructive diseases affecting oil palm plantations in Southeast Asia. Early detection of this disease is crucial to prevent its widespread transmission and to maintain plantation productivity. This study proposes an image classification approach using ensemble learning with three Convolutional Neural Network (CNN) architectures: DenseNet161, ResNet152, and VGG19, to detect BSR-infected oil palm trees through aerial imagery captured by Unmanned Aerial Vehicles (UAVs). The dataset used consists of 7,348 annotated images classified into two categories: healthy and unhealthy. Experimental results show that the DenseNet161 model outperformed the others, achieving a validation accuracy of 91.75% and a validation loss of 0.0307. The ensemble CNN approach demonstrated improved classification accuracy and holds significant potential for implementation in automated and precise plant health monitoring systems. This research provides a valuable contribution to AI-based agricultural technology, particularly in disease management for oil palm plantations.