The world’s population has reached 8 billion and is projected to reach 9.7 billion by 2050, increasing the demand for food production. Artificial intelligence (AI) technologies that optimize resources and increase productivity are vital in an environment that has tensions in the supply chain and increasingly frequent weather events. This study performed a systemic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology on artificial intelligence technologies applied to agriculture. It retrieved 906 relevant studies from five electronic databases and selected 176 studies for bibliometric analysis. The quality appraisal step selected 17 studies for the analysis of the benefits, challenges, and trends of AI technologies used in agriculture. This work showed an evolution in the area with increased publications over the last five years, with more than 20 different AI techniques applied in the 176 studies analyzed, with machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision being the most used technologies. Considering a worldwide scope, the countries highlighted were India, China, and the USA. Agricultural sectors included crop management and prediction and disease and pest management. Finally, it presented challenges and trends that are promising when considering the future directions in AI for agriculture.

The growing global demand for soybean production combined with its increased market value could result in a new supercycle for this commodity. For Brazilian agribusiness, there has been an opportunity to increase exports, particularly in soybean production, in recent years, and therefore, soybean production has been expanding more and more across the states of the Brazilian Amazon. Soybean is the most important grain crop among temporary crops in the Brazilian Amazon; in 2019, it reached a value of USD 21.78 billion, using a planted area of 124,947 km² (about 55% of the planted area). At the same time, overall deforestation increased significantly in recent years: 10,897 km² in 2019 and 9811 km² in 2020. To study these changes, economic, social, and environmental sustainability indicators were identified and analyzed using a regression model, and changes in the main economic and socio-environmental indicators were observed that identified a strong positive correlation between agricultural GHG emissions and soybean-planted area. The impact on the local population was also analyzed between the years 2000 and 2019, and there was a mismatch between the population growth rate and the growth rate of the harvested area, which resulted in the displacement of the populations to the cities, and identified a strong positive correlation between the unemployment rate for young people and the soybean-planted area. In this context, this paper presents an analysis of the correlation between soy expansion and the main economic indicators and socioenvironmental impacts in the Brazilian Amazon.

In this paper, a methodology was developed to analyze the results of energy efficiency programs used in the electricity distribution sector in Brazil. Analyzing the gains obtained through the investments made, and classifying which actions resulted in the best performance, contributed to decision making on the best allocation of investments to obtain the greatest energy savings. The Brazilian Energy Efficiency program was analyzed with a developed non-parametric model, using the data envelopment analysis method, and the categories of projects with better performance were determined. A database of the results from 1704 projects, from 2008 to 2016 in the Energy Efficiency program in Brazil´s electricity distribution sector, was used. The results obtained show that the best performance was achieved by projects in the industrial and cogeneration categories; however, in Brazil these constitute only 4.24% of the projects presented and 5.28% of the total investments in the last eight years, indicating a need to review the regulatory strategies for energy efficiency in this country.

In the search for strategies to mitigate climate change, the promotion of renewable energy is a major challenge worldwide, particularly for developing countries such as Brazil and Colombia, which aim to diversify their power grids by using unconventional renewable energy sources. One of the main obstacles is the development of innovative projects. Increasing oil palm cultivation in the Amazon region for the food and biodiesel industries is producing a large volume of biomass. The present study outlines a methodology for analysis of renewable energy projects based on identification of environmental, economic, and social sustainability criteria and indicators (C&I) for the oil palm production chain. This methodology was then used to develop a computer simulation model in the RETScreen® International software environment for financial viability and risk analysis.

The electricity distribution system is constantly evolving, with the spread of distributed energy resources, smart grids and the inclusion of competitive markets. With the consumer taking on the role of supplier of surplus energy to the network (prosumer), with the trend of expanded participation through the intelligent buildings that will integrate the distribution system actively. In this scenario, the strong correlation of sustainable buildings and smart buildings is evident in the Brazilian case, which has traditional governmental programs that promote the efficient use of energy as a sustainability strategy, with emphasis on buildings, which must follow the international trend, adopting the Net Zero Energy Buildings. Also noteworthy is the considerable increase in the distributed generation of electric energy, building a favorable scenario for the investment in smart buildings. In order to contribute to the studies in this area, this paper presents the active participation of buildings in the electricity distribution network, involving and correlating the concepts of smart Technologies and distributed energy resources, through the review of publications contextualizing the international trend, as well as data on the Brazilian case.

The search for sustainable energy alternatives is urgent in the face of climate change and resource scarcity. In this context, increasing energy efficiency in buildings through distributed energy resources (DERs) is crucial for sustainability and self-sufficiency. This article aims to analyze the impact of several energy efficiency actions, in addition to the installation of a photovoltaic solar energy system in a public building in a semi-arid region, determining the necessary aspects for such buildings to become positive energy buildings (PEBs) and/or net zero energy buildings (NZEBs). As a basis for the methodology, a case study was carried out in a university restaurant in a semi-arid region in Brazil. Several pieces of data were collected, such as the air temperature, solar radiation, active energy and the number of users in the building. The relevance of each variable in relation to electricity consumption was identified through statistical correlation analysis, resulting in an energy consumption per square meter per year of 80.78 kWh/m²/year and an active energy consumption per user per year of 0.88 kWh/m²/year. Three energy efficiency actions were evaluated and compared technically and economically against the investment in a grid-connected photovoltaic system (GCPVS) for the same building, simulating before and after the entry into force of Law 14.300/2022, which regulates distributed generation in Brazil. The installation of thermal insulation on the building’s roof showed good technical, economic and environmental performance, compared to GCPVS, and proved to be attractive and competitive among the other scenarios. All simulated actions resulted in an annual emission reduction of 14.8 tCO₂e. When calculating the building’s generation potential, it was found that it could be considered an NZEB and PEB.