Olive trees play a crucial role in the global agricultural landscape,
serving as a primary source of olive oil production. However, olive
trees are susceptible to several diseases, which can significantly impact
yield and quality. This study addresses the challenge of improving the
diagnosis of diseases in olive trees, specifically focusing on aculus olearius
and Olive Peacock Spot diseases. Using a novel hybrid approach that
combines deep learning and machine learning methodologies, the authors
aimed to optimize disease classification accuracy by analyzing images of
olive leaves. The presented methodology integrates Local Binary Patterns
(LBP) and an adapted ResNet50 model for feature extraction,
followed by classification through optimized machine learning models,
including Stochastic Gradient Descent (SGD), Support Vector Machine
(SVM), and Random Forest (RF). The results demonstrated that the
hybrid model achieved a groundbreaking accuracy of 99.11%, outperforming
existing models. This advancement underscores the potential of
integrated technological approaches in agricultural disease management
and sets a new benchmark for the early and accurate detection of foliar
diseases.
