Agribusinesses need to answer to societal growing concerns regarding the use of natural resources. In this context, the digital technologies described by Industry 4.0 appear as allies in the optimization of agribusiness. The use of these technologies is closely related to digital transformation, leading to this work's goal: to analyze the dimensions of Digital Transformation (DT) in the Modern Agricultural (MA) context. The methodology of this study was divided into three main parts. Firstly, a Systematic Literature Review (SLR) was performed with the help of the SciMAT® software. Then, a content analysis was performed using a semantic technique to define the dimensions a posteriori. Finally, a Latent Dirichlet Allocation (LDA) model using the RStudio® software was made to validate the results found in the semantic analysis. To the best of our knowledge, there are only five studies that define dimensions of DT in MA, however none of them used a robust SLR with content analysis. Two of these studies did empirical researches with specialists to define the dimensions, and the other three defined specifics dimensions only a priori. Our study brings as an innovation the definition of the dimensions a posteriori through the SLR. Additionally, no studies that validated the results using a LDA model were found. As a result of our studies, we have identified eight dimensions: Economical, Government, Sustainability; Infrastructure, Technological, Cooperation, Change, and People/Knowledge/Skills. The first three dimensions are more influenced by external factors, while external and internal factors influence the following three, and finally, the last two are more affected by internal factors. For each set of the dimensions listed, the main pillars/drivers/motivators and the main barriers/challenges/difficulties were identified. For example, for the mixed dimension, the first set can be the creation of regional centers that broadcast digital technology content. Regarding the second set, we can cite the difficulty in transforming data into useful information for the same dimension. The results serve as a foundation for both practical business actions and the academic field. They presented a robust theoretical basis for developing guidelines for agricultural management (of farms and other related startups and companies) and public policies to encourage the adoption and implementation of technological resources guided by sustainable development goals. The impacts can be viewed as: reduced production through sustainable and responsible production, assistance in the conscious and sustainable use of water resources, and sustainable actions to mitigate climate change. In conclusion, we highlight that the environment in which the framework will be applied should influence the emphasis given to each set of dimensions. For instance, legislators that seek to develop policies for DT in MA should pay greater attention to the external dimensions. In contrast, farmers that seek to implement DT should be more focused on the internal dimensions.