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Percorrer Escola Superior de Tecnologia e Gestão por Domínios Científicos e Tecnológicos (FOS) "Ciências Agrárias::Agricultura, Silvicultura e Pescas"
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- Advancing Sustainability and Productivity: The Role of Precision Agriculture in Vineyards and Olive GrovesPublication . Fernandes, Fernanda Mara; Santos, Murillo Ferreira dos; Morais, Maurício Herche Fófano de; Lima, José; Pereira, Ana I.; Mercorelli, PaoloPrecision agriculture has emerged as a vital approach to modern agricultural management, addressing the dual challenge of increasing food production while preserving the environment. Its importance lies in its ability to leverage advanced technologies to optimize productivity, reduce waste, and ensure sustainability, particularly in high-value crops such as vineyards and olive groves. This study explores the application of precision agriculture tools, such as sensors, drones, geolocation systems, and data analytics, in these crops to enhance productivity, improve product quality, and minimize environmental impact. In vineyards, precision viticulture focuses on managing spatial and temporal variability within plots to increase economic performance through higher productivity, superior fruit quality, and reduced production costs. The targeted application of inputs and precise management practices result in resource savings and uniform fruit quality, crucial for producing premium wines. Similarly, in olive groves, technologies enable effective plant health monitoring and early disease detection. At the same time, drones assist in evaluating plant vigor and planning optimal harvest times, ultimately maximizing yield and olive oil quality. By integrating traditional agricultural practices with modern technological advancements, this study anticipates a range of positive outcomes, including reduced resource waste, improved competitiveness in global markets, and strengthened sustainability in production networks. The findings underscore the transformative potential of precision agriculture, offering valuable insights into sustainable agricultural development and setting a pathway for further innovation in the sector.
- Automated preprocessing of olive leaf images for cultivar classification using YOLO11Publication . Mendes, João; Lima, José; Rodrigues, Nuno; Pereira, Ana I.Olive cultivation is a pillar of Mediterranean agriculture, deeply rooted in both tradition and economic importance. This paper presents a novel two-phase methodology for the automated preprocessing of olive leaf images to facilitate accurate cultivar classification. Leveraging the state-of-the-art YOLO11 framework, two models (YOLO11n and YOLO11s) were employed for detection and segmentation tasks. A comprehensive dataset, combining in-situ captured images with publicly available data, was meticulously annotated using both manual and semi-automatic processes. The detection model identifies individual olive leaves, while the segmentation model isolates the leaves by replacing the background with a uniform white, thereby simulating laboratory conditions. Experimental results demonstrate that YOLO11n outperforms YOLO11s in terms of mean Average Precision and F1-score, confirming the feasibility of deploying the system on mobile devices for real-time, in-field classification.
- 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.
- Control Allocation and Controller Tuning for an Over-Actuated Hexacopter Tilt-Rotor Applied for Precision AgriculturePublication . Libório, Leandro Oliveira; Pimentel, Gabriel Oliveira; Santos, Murillo Ferreira dos; Fernandes, Fernanda Mara; Lima, José; Morais, Maurício Herche Fófano de; Mercorelli, Paolo; Pereira, Ana I.This work presents the control allocation and tuning methodology for an over-actuated Hexacopter Tilt-Rotor (HTR) designed for precision agriculture applications. The HTR's innovative design includes two independently tiltable rotors, enhancing stability and forward velocity, making it suitable for low-altitude maneuvers in agricultural environments. The study focuses on the implementation of a cascade Proportional (P)-Proportional, Integral and Derivative (PID) control structure with Successive Loop Closure (SLC) and the application of an extended Fast Control Allocation (FCA) method to optimize actuator performance. The control gains were meticulously tuned to ensure stability and robustness across six degrees of freedom, achieving precise trajectory tracking and efficient resource use. Validation was conducted through simulations using Robot Operating System (ROS) and Gazebo, replicating realistic precision agriculture scenarios. Results demonstrate the efficacy of the proposed control strategies, highlighting their potential for real-world applications in crop monitoring, pest detection, and resource optimization. Future work includes physical implementation and integration with collaborative robotics.
- 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.
- Empowering olive cultivation with artificial intelligence: a systematic literature review on advancements and prospectsPublication . Mendes, João; Lima, José; Costa, Lino; Pereira, Ana I.This study provides a Systematic Literature Review on the application of Artificial Intelligence algorithms in the primary sector of olive cultivation. It compiles and analyses a collection of studies that leverage AI to enhance the efficiency and sustainability of olive production, maintenance, and harvesting processes. In this study, 43 papers were reviewed from the databases IEEE, Scopus, and Web of Science through the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method. This research aims to identify AI applications in the primary olive growing sector. The findings highlight a significant trend toward adopting advanced AI techniques, particularly Deep Learning algorithms such as Convolutional Neural Networks, for many tasks ranging from cultivar identification and foliar disease classification to crop yield forecasting with high accuracies.
- How Wine Information Seeking and Event Participation Impact Knowledge and Determines the Purchasing Behavior?Publication . Vieira, Elvira Pacheco; Borges, Ana Pinto; Rodrigues, Paula; Ostapenko, Svitlana; Almeida, António Lopes deHow knowledge impacts purchasing behavior is a matter of extensive investigation, but the research on the interplay between the self-reported wine knowledge and information seeking is scarce. Present research contributes to filling the gap within the understanding how self-reported wine knowledge and information seeking impacts wine purchase behavior. Based on the analysis of 1314 valid questionnaires administered to the participant of wine event “Essência do Vinho” held in Porto, Portugal between 23 and 26 of February 2023, partial least squares (PLS) path analytical technique was employed. We confirm that more knowledgeable consumers are less price sensitive and rely more on their knowledge and subsequent appreciation of place of origin of the wine at the moment of purchase. Meanwhile limited wine knowledge is a constraint in wine consumption, as it makes consumers more insecure in making their own choices and relying more on value for money appreciation, price and recommendation factors. We conclude that stimulation of information seeking and participation in the wine events is key in increasing wine expertise that impacts appreciation of the place of origin and subsequent willingness to pay. Thus, promoting awareness and expertise among consumers is the key to increasing competitiveness of the wine sector.
- 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.
- On-site power generation using biogas in sewage treatment plants: a techno-economic assessment of a brazilian uasb facilityPublication . Pérez, Nestor Proenza; Adrião Cabral, Edilson; Bimestre, Thiago Averaldo; Loures, Carla Almeida; Yepes Maya, Diego M.; Ribeiro, Luís FrölénSmall sewage treatment plants (STPs) in developing regions often flare the biogas produced in their upflow anaerobic sludge blanket (UASB) reactors, giving away a cost-effective energy source. This study tests whether on-site biogas-to-energy can pay for itself in approximately 2 years, even in plants treating less than 30 l s(-1). A small-scale STP in Angra dos Reis, Brazil (25 L/s), was studied, with an average biogas flow of 9.7 m(3)/h; electricity generation was modeled for an engine generator unit with an efficiency of 30%. The techno-economic results show that the actual system would generate 125 MWh/year at a levelized cost of 0.017-0.023 USD/kWh, covering 47% of the plant's electricity demand. At a discount rate of 8%, the net present value was + 9.3 k US$, and the simple payback period was 2 years for the initial investment. Additionally, extrapolating the results to account for future expansion of the sewage treatment plant based on the total population in the region served by the system reveals even more promising results, with a suggested payback period of 1 year and 1 month of operation, covering approximately 57% of electricity demand. Scaling this retrofit to the approximately 18,000 comparable UASB-based STPs worldwide at low capital cost could reduce electricity bills by approximately 40% and avoid similar to 450 tons of CO2-eq. per plant per year through methane capture and displacement of electricity from the grid. These results confirm that decentralized biogas power generation on a small scale is not only technically feasible, but also financially attractive and ecologically beneficial for operators of sewage and wastewater treatment plants and municipalities.
- Optimizing Olive Disease Classification Through Hybrid Machine Learning and Deep Learning TechniquesPublication . Mendes, João; Moso, Juliet; Berger, Guido S.; 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.