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.