Voting-Based Classification Approach for Date Palm Health Detection Using UAV Camera Images: Vision and Learning

In this study, we introduce the application of deep learning (DL) models, specifically convolutional neural networks (CNNs), for detecting the health status of date palm leaves using images captured by an unmanned aerial vehicle (UAV). The images are modeled using the Newton–Euler method to ensure stability and accurate image acquisition. These deep learning models are implemented by a voting-based classification (VBC) system that combines multiple CNN architectures, including MobileNet, a handcrafted CNN, VGG16, and VGG19, to enhance classification accuracy and robustness. The classifiers independently generate predictions, and a voting mechanism determines the final classification. This hybridization of image-based visual servoing (IBVS) and classifiers makes immediate adaptations to changing conditions, providing straightforward and smooth flying as well as vision classification. The dataset used in this study was collected using a dual-camera UAV, which captures high-resolution images to detect pests in date palm leaves. After applying the proposed classification strategy, the implemented voting method achieved an impressive accuracy of 99.16% on the test set for detecting health conditions in date palm leaves, surpassing individual classifiers. The obtained results are discussed and compared to show the effectiveness of this classification technique.