Autor(es): Mendes, João ; Moso, Juliet ; Berger, Guido ; Lima, José ; Costa, Lino ; Guessoum, Zahia ; Pereira, Ana I.
Data: 2024
Identificador Persistente: http://hdl.handle.net/10198/34427
Origem: Biblioteca Digital do IPB
Assunto(s): Optimization; Hybrid Model; Machine Learning; Deep Learning; Olive Disease; Olive Leaves
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.
