ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus
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Percorrer ESTiG - Publicações em Proceedings Indexadas à WoS/Scopus 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.
- 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.
- Enhancing agricultural audit systems with IoT and predictive analytics: deployment and performancePublication . Correia, Maria Patrocínia; Alves, Joaquim; Sequeira, Romeu; Exposto, JoséThis article proposes an extension of a geospatial-based agricultural audit platform by integrating Internet of Things technologies and predictive analytics. It builds on previous work to address field connectivity, scalability, and risk anticipation. The proposal outlines a modular architecture, identifies technical challenges and mitigation strategies, and presents use cases for different user profiles. Ethical, legal, and environmental implications of digitalisation are also discussed. This conceptual work supports future experimental validation and highlights the potential of digital transformation to enhance compliance with the Common Agricultural Policy and align with the European Green Deal priorities.
- 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.
- 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.
- UAV-assisted navigation for insect traps in olive grovesPublication . Berger, Guido S.; Bonzatto Junior, Luciano; Pinto, Milena F.; Oliveira Júnior, Alexandre de; Mendes, João; Pereira, Ana I.; Silva, Yago M. R. da; Valente, António; Lima, JoséUnmanned Aerial Vehicles (UAVs) have emerged as valuable tools in precision agriculture due to their ability to provide timely and detailed information over large agricultural areas. In this sense, this work aims to evaluate the semi-autonomous navigation capacity of a multirotor UAV when applied in the field of precision agriculture. For this, a small aircraft is used to identify and track a set of fiducial markers (Ar Track Alvar) in an environment that simulates inspections of insect traps in olive groves. The purpose of this marker is to provide a visual reference point for the drone’s navigation system. Once the Ar Track Alvar marker is detected, the robot will receive navigation information based on the marker’s position to approach the specific trap. The experimental setup evaluated the computer vision algorithm applied to the UAV to make it recognize the Ar Track Alvar marker and then reach the trap efficiently. Experimental tests were conducted in a indoor and outdoor environment using DJI Tello. The results demonstrated the feasibility of applying these fiducial markers as a solution for the UAV’s navigation in this proposed scenario.