Browsing by Author "Rodrigues, Raimundo Nonato"
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- Bioenergy relations with agriculture, forestry and other land uses: Highlighting the specific contributions of artificial intelligence and co-citation networksPublication . Martinho, Vítor; Rodrigues, Raimundo NonatoThe concerns with the environment and sustainability have promoted options for energy sources that mitigate the footprint of human life. The use of biomass from agriculture, forestry and other land uses (AFOLU) has enormous potential for the production of bioenergy as a renewable source of energy. In this context, this research aims to analyse the interrelationships between bioenergy and agriculture, forestry and other land uses, highlighting the contributions of the digital transition for these dimensions. To achieve these objectives, a bibliometric analysis through co-citation links (and items related to cited authors, references and sources) was carried out for the dimensions associated with the bioenergy and the AFOLU and after a specific literature survey was performed for the contributions from the digital transition for these frameworks. With this study, top authors, references and sources were identified for the topics assessed and it was highlighted the importance of digital transitions for more efficient bioenergy use and production in the worldwide contexts.
- Machine Learning and Food Security: Insights for Agricultural Spatial Planning in the Context of Agriculture 4.0Publication . Martinho, Vítor; Cunha, Carlos; Pato, Lúcia; Costa, Paulo Jorge; Sánchez-Carreira, María Carmen; Georgantzís, Nikolaos; Rodrigues, Raimundo Nonato; Coronado, Freddy: Climate change and global warming interconnected with the new contexts created by the COVID-19 pandemic and the Russia-Ukraine conflict have brought serious challenges to national and international organizations, especially in terms of food security and agricultural planning. These circumstances are of particular concern due to the impacts on food chains and the resulting disruptions in supply and price changes. The digital agricultural transition in Era 4.0 can play a decisive role in dealing with these new agendas, where drones and sensors, big data, the internet of things and machine learning all have their inputs. In this context, the main objective of this study is to highlight insights from the literature on the relationships between machine learning and food security and their contributions to agricultural planning in the context of Agriculture 4.0. For this, a systematic review was carried out based on information from text and bibliographic data. The proposed objectives and methodologies represent an innovative approach, namely, the consideration of bibliometric evaluation as a support for a focused literature review related to the topics addressed here. The results of this research show the importance of the digital transition in agriculture to support better policy and planning design and address imbalances in food chains and agricultural markets. New technologies in Era 4.0 and their application through Climate-Smart Agriculture approaches are crucial for sustainable businesses (economically, socially and environmentally) and the food supply. Furthermore, for the interrelationships between machine learning and food security, the literature highlights the relevance of platforms and methods, such as, for example, Google Earth Engine and Random Forest. These and other approaches have been considered to predict crop yield (wheat, barley, rice, maize and soybean), abiotic stress, field biomass and crop mapping with high accuracy (R2 ≈ 0.99 and RMSE ≈ 1%)