The World Bank has estimated that an additional $80 billion in financing are needed annually to achieve the 70 % increase in food supply required to feed the world in 2050. One of the cornerstones in achieving this increase in production is expected to be improved agricultural technology, where one of the latest additions is precision agriculture. It is believed that the money for investing in this technology must come from the private sector, but financial institutions are hesitant in lending money to farmers. This, in part, comes down to a high perceived riskiness in agricultural lending stemming from the risk composition in agriculture compared to other industries as well as from weak collaterals provided by farmers. This thesis aims to find what factors are most prominent in banks´ risk assessment of agricultural firms during the lending process and look at how precision agriculture could help mitigate these risks. We have gathered aggregated quantitative data from FAOSTAT and the Swedish Board of Agriculture on farm income and hectare yield (productivity) at Swedish farms. These variables were found to be two of the most important factors in agricultural lending based on previous research. In addition to this data, information on e.g. weather, ecological farming and expenditure related to pesticides, fertilizer, and machinery were collected to further the analysis. Precision agriculture is made up from a myriad of different technologies. We have opted to not separate the technologies in this study as the adoption of each technology included in the term is currently not sufficiently well understood. This aggregation of technologies allowed for us to use the dynamic AAGE-model to estimate the adoption based on the minimum hectare size where precision agriculture should be profitable at each point in time. The study finds that precision agriculture does have a positive impact on farm productivity and income volatility. Hence, precision agriculture should reduce the risk of agricultural financing given to adopting farmer which would increase the access to credit and, in continuation, lead to an increase in aggregated food production. In addition, we conclude that financial institutions should gain a better knowledge of precision agriculture technologies and use this information to improve the credit evaluation process in agricultural lending. Lastly, banks should understand how the risks related to information asymmetry and moral hazard could be reduced by utilizing the data available through farmers use of precision agriculture technology.