Recognition of leaf diseases in hazelnut orchards with drone imagery by deep learning models: effectiveness of U-Net model variants

This paper presents a novel semantic segmentation approach for the binary classification of diseased and healthy regions in hazelnut orchards, addressing a significant gap in existing research by focusing on tree-level analysis. Utilizing video imagery captured by unmanned aerial vehicles (UAVs), this study investigates the challenges associated with drone-based image acquisition, such as environmental conditions, physical obstructions, battery life, and image stability. A unique image dataset specifically designed for tree-based disease detection is created. Various deep learning models, including seven U-Net variants, are employed to tackle the complexities of image processing and segmentation. The U-Net++ model is identified as the most effective for this task, demonstrating strong performance in distinguishing affected areas. However, further optimization of hyperparameters and expansion of the dataset are recommended to enhance model robustness and generalization. This research underscores the critical need for additional studies on UAV-based tree-level disease detection and highlights the substantial potential of deep learning in agricultural technology for mitigating production losses through early and accurate identification of stressed regions.