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Agriculture forms the cornerstone of global food security, with olives playing a pivotal role not only as a food source but also in cosmetics, medicine, and other industries. However, diseases affecting olive trees pose significant threats to agricultural productivity and economic stability, underscoring the need for innovative detection solutions. A promising solution to these challenges is the development of deep learning-based computer-aided diagnostic applications, which have shown remarkable success in various fields, especially in recent years. This study presents a novel deep-learning approach for olive leaf disease detection, introducing a MetaFormer-based architecture that combines the power of transformer-based components, specifically separable self-attention, with the efficiency of a lightweight design. The proposed model was evaluated using two distinct datasets, Dataset-1 and Dataset-2, where it achieved impressive accuracy rates of 99.31% and 96.91%, respectively. When compared to other cutting-edge models such as Swin-Base, MaxViT-Base, DeiT3-Base, CAFormer-s18, CAFormer-m36, ResNet50, and MobileNetv3, the Proposed Model outperformed them in terms of accuracy, precision, recall, and F1-score. These advancements were made possible through the incorporation of separable self-attention, which allows for capturing both local and global dependencies in olive leaf images, and a streamlined architecture that reduces computational complexity without sacrificing performance. Furthermore, Grad-CAM visualizations highlighted the interpretability of the model, confirming its ability to focus on disease-relevant regions of the images. This study offers a significant contribution to the field of agricultural disease detection, particularly in olive farming, and sets the stage for future work in adapting the model for other crops and real-time applications in agriculture.
Paçal et al. (Mon,) studied this question.