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::Biotecnologia Agrária e Alimentar"
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- Architecture for efficient food management and waste reductionPublication . Pereira, Hélder; Oliveira, Pedro Filipe; Matos, PauloThis article presents a modular architecture for developing the ZeroWaste mobile application, designed to optimize food management in the home environment and reduce food waste through collaborative and scalable features. Food waste is a global issue with severe environmental, economic, and social repercussions, and ZeroWaste seeks to address this challenge by promoting conscious and sustainable consumption practices. Developed in React Native to support multiple platforms, the application integrates Firebase for authentication, notifications, and real-time data storage, enabling timely alerts on product expiration and facilitating user control over food inventory. Additionally, it incorporates an artificial intelligence module that suggests personalized recipes based on available products, encouraging food usage before spoilage. The proposed architecture also includes an automated product registration system using barcode scanning, supporting the creation of a community database that streamlines food item identification. Other features, such as shared shopping lists and multi-residence inventory management, expand the collaborative scope of the application, fostering the exchange and donation of food between users. With its flexible and expandable design, ZeroWaste is oriented towards continuous development and future improvements, meeting the growing need for technological solutions in sustainable and collaborative food management. This architectural proposal provides a robust foundation for developing an innovative solution.
- A deep learning approach for average height estimation in oak colony using rgb imagesPublication . Britto, Raphael Duarte; Mendes, João; Grilo, Vinicius; Castro, João Paulo; Santos, Murillo Ferreira dos; Castro, Marina; Pereira, Ana I.; Lima, JoséMany strategies have been developed to monitor the volume of volume of Above Ground Biomass (AGB) in forest areas as a fundamental step for managing carbon concentration. This study explores the use of use of Light Detection and Ranging (LiDAR) data obtained through Unmanned Aerial Vehicles (UAVs) to estimate height values in a vegetation colony composed of oaks (Quercus pyrenaica Willd.) in northern Portugal. The extraction of pertinent information from LiDAR data was facilitated by using the LAStools extension within the Quantum Geographic Information System (QGIS) software framework. The generated raster and image information were used to calculate the height values of the vegetation. Following this extraction, the information was meticulously organized into datasets, which were then employed in Deep Learning (DL) algorithms. The VGG16 model was selected as the underlying framework for the present study. Height predictions were made using dimensions of 16× 16, 32× 32, and 64 × 64 pixels for the Red, Green and Blue (RGB) images. The data was estimated and compared using both the standard format of the VGG16 model and a superficially adapted version of its convolution layers. The algorithm’s efficacy was validated by comparing the forecast results with the data obtained from QGIS, which revealed minimal discrepancies. It was observed that using 64× 64 pixel scale images yielded enhanced accuracy, resulting in reduced values for the Mean Absolute Error (MAE). The study demonstrates the viability of applying DL techniques to accurately capture information about a forest area using RGB images.
- A YOLO-Based Approach for Detection of Olive Knot Disease through UAV and Computer Vision TechnologiesPublication . Morais, Maurício Herche Fófano de; Mendes, João; Santos, Murillo Ferreira dos; Fernandes, Fernanda Mara; Lima, José; Pereira, Ana I.This work presents an approach for detecting olive knot disease in olive trees, utilizing Computer Vision (CV), Unmanned Aerial Vehicle (UAV) based imagery, and Machine Learning (ML) within the context of Precision Agriculture (PA). The study focuses on applying the You Only Look Once (YOLO) deep learning architecture to develop a model capable of identifying trees affected by the disease with accuracy and speed. By integrating UAV technology with object detection algorithms, this approach enables real-time monitoring of olive plantations, supporting early detection and targeted interventions. This study emphasizes the potential of combining drone imaging and ML to drive sustainable and practical solutions in PA. Results show that this method can potentially improve crop management by reducing human labor and contributing to the enhancement of disease control strategies.