Escola Superior de Tecnologia e Gestão
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Browsing Escola Superior de Tecnologia e Gestão by Field of Science and Technology (FOS) "Ciências Agrárias::Agricultura, Silvicultura e Pescas"
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- Contributions to accelerating a numerical simulation of free flow parallel to a porous planePublication . Schepke, Claudio; Spigolon, Roberta A.; Rufino, José; Cristaldo, Cesar F. Da C.; Pizzolato, Glener L.Flow models over flat p orous surfaces have applications in natural processes, such as material, food, chemical processing, or mountain mudflow simulations. The development of simplified a nalytical or numerical models can predict characteristics such as velocity, pressure, deviation length, and even temperature of such flows for geophysical and engineering purposes. In this context, there is considerable interest in theoretical and experimental models. Mathematical models to represent such phenomena for fluid mechanics have continuously been developed and implemented. Given this, we propose a mathematical and simulation model to describe a free-flowing flow pa rallel toa porous material and its transition zone. The objective of the application is to analyze the influence o f t he p orous matrix on the flow u nder d ifferent m atrix p roperties. W e i mplement a Computational Fluid Dynamics scheme using the Finite Volume Method to simulate and calculate the numerical solutions for case studies. However, computational applications of this type demand high performance, requiring parallel execution techniques. Due to this, it is necessary to modify the sequential version of the code. So, we propose a methodology describing the steps required to adapt and improve the code. This approach decreases 5.3% the execution time of the sequential version of the code. Next, we adopt OpenMP for parallel versions and instantiate parallel code flows and executions on multi-core. We get a speedup of 10.4 by using 12 threads. The paper provides simulations that offer the correct understanding, modeling, and construction of abrupt transitions between free flow a nd porous media. The process presented here could expand to the simulations of other porous media problems. Furthermore, customized simulations require little processing time, thanks to parallel processing.
- Disease detection and mapping in olive groves using UAVs and deep learning for precision agriculturePublication . Morais, Maurício Herche Fófano de; Lima, José; Santos, Murillo Ferreira dos; Mendes, João CarlosThis dissertation presents the implementation and validation of a cost-effective, Unmanned Aerial Vehicle (UAV) based system for automated detection and spatial mapping of olive knot disease in olive groves. Addressing the need for accessible and efficient plant disease monitoring in Precision Agriculture (PA), the proposed methodology leverages existing UAV imagery capabilities with lightweight Deep Learning (DL) models, specifically the You Only Look Once (YOLO) object detection architecture, to enable scalable and accurate detection. The academic contributions presented in this work have resulted in two peer-reviewed publications related to the dissertation topic, as detailed at the end of this document. An annotated dataset of UAV-acquired images was compiled, and several state-of-the-art YOLO object detection models were trained and evaluated under identical conditions. The best-performing model achieved a strong F1-score, demonstrating good results in detecting olive knot disease and accurately mapping its spatial distribution within the plantation. The workflow integrates spatial cross-referencing of detections with UAV flight path data and proximity analysis, enabling the assignment of disease detections to individual trees. An interactive map interface, developed using the Folium Python library, provides visualization of the disease distribution and supports practical grove management. The experimental results indicate that cost-effective UAVs and lightweight DL models can be effectively combined for plant disease detection and spatial analysis, offering a robust and scalable approach for real-world agricultural applications. Limitations regarding early symptom detection and image quality are discussed, and directions for future work are proposed.
- Impact of hyper-parameter tuning on CNN accuracy in agricultural image classificationPublication . Mendes, João; Lima, José; Costa, Lino; Hendrix, Eligius M.T.; Pereira, Ana I.This study explores the impact of hyper-parameter optimization on the performance of convolutional neural networks (CNNs) for olive cultivar classification using transfer learning. Pre-trained ImageNet models such as VGG16, InceptionV3, and ResNet50 were adapted to a proprietary dataset, with VGG16 selected for detailed evaluation. Key hyper-parameters, including layer count, neurons per layer, dropout rate, learning rate, and batch size, were tuned using random search. The best configuration achieved a validation accuracy of 87.5%, significantly outperforming the control model. Sensitivity analyses with Morris and Sobol methods identified the number of layers as the most influential factor, followed by dropout and learning rates through interaction effects. These findings demonstrate the importance of tailoring CNN architecture and regularization settings to the problem domain. These results underscore the importance of tuning architectural depth and regularization mechanisms for performance optimization. As a practical guideline, models with fewer layers and intermediate dropout levels demonstrated higher robustness and generalization, offering an effective strategy for adapting CNNs to agricultural classification tasks.
- Optimizing Olive Disease Classification Through Hybrid Machine Learning and Deep Learning TechniquesPublication . Mendes, João; Moso, Juliet; Berger, Guido; Lima, José; Costa, Lino; Guessoum, Zahia; Pereira, Ana I.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.