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- AI-powered solution for plant disease detection in viticulturePublication . Madeira, Miguel; Porfírio, Rui; Santos, Pedro Albuquerque; Madeira, Rui NevesIn an era dominated by the intersection of advanced technology and traditional industries, the domain of agriculture is on the verge of a revolutionary transformation. This article introduces a solution for vineyard producers, harnessing satellite imagery, weather data, and deep learning (DL) to identify vineyard diseases robustly. This solution, designed for proactive plant health management, stands as a transformative tool towards digital viticulture. Such tools transition from luxuries to essentials as vineyards confront evolving challenges like climate change and new pathogens. Our research builds on the hypothesis that customising deep learning architectures for specific tasks is crucial in enhancing their effectiveness. We contribute by introducing a tailored convolutional neural network (CNN) architecture, developed specifically for the classification of plant diseases using vineyard imagery. The experimental results demonstrate that our custom CNN architecture exhibits performance on par with established state-of-the-art models like ResNet50 and MobileNetV2, underscoring the value of specialized solutions in addressing the unique challenges of viticulture. This paper introduces an overview of the solution’s architecture, presents the implementation of DL modules with their corresponding results, and describes use case scenarios.
- Strawberry plant as a biomonitor of trace metal air pollution: a citizen science approach in an urban-industrial area near Lisbon, PortugalPublication . Gamelas ou Carla A. Gamelas, Carla; Canha, Nuno; Justino, Ana R.; Nunes, Alexandre; Nunes, Sandra; Dionísio, Isabel; Kertesz, Zsofia; Almeida, Susana MartaA biomonitoring study of air pollution was developed in an urban-industrial area (Seixal, Portugal) using leaves of strawberry plants (Fragaria × ananassa Duchesne ex Rozier) as biomonitors to identify the main sources and hotspots of air pollution in the study area. The distribution of exposed strawberry plants in the area was based on a citizen science approach, where residents were invited to have the plants exposed outside their homes. Samples were collected from a total of 49 different locations, and their chemical composition was analyzed for 22 chemical elements using X-ray Fluorescence spectrometry. Source apportionment tools, such as enrichment factors and principal component analysis (PCA), were used to identify three different sources, one geogenic and two anthropogenic (steel industry and traffic), besides plant major nutrients. The spatial distribution of elemental concentrations allowed the identification of the main pollution hotspots in the study area. The reliability of using strawberry leaves as biomonitors of air pollution was evaluated by comparing them with the performance of transplanted lichens by regression analysis, and a significant relation was found for Fe, Pb, Ti, and Zn, although with a different accumulation degree for the two biomonitors. Furthermore, by applying PCA to the lichen results, the same pollution sources were identified.
- IoT, UAV, BCI empowered deep learning models in precision agriculturePublication . Lian, Jian; Pereira, José Miguel Costa Dias
- Transmissores pneumáticosPublication . V. Dionísio, R.
- O processo industrialPublication . V. Dionísio, R.
- Fontes de alimentaçãoPublication . V. Dionísio, R.
- Aparelhos de medição de posição angularPublication . V. Dionísio, R.
- Sensores de nível por LASERPublication . V. Dionísio, R.
- BalançasPublication . V. Dionísio, R.
- Caudalímetros ultra-sónicosPublication . V. Dionísio, R.