Among the different stages involved in agricultural production processes, agricultural spraying used for phytosanitary control encompasses a significant portion of the total production cost, and its incorrect execution can increase human and biological risks. Therefore, research is necessary to ensure the proper use of agricultural pesticides, ensuring the quality of their application. Among the quality indicators provided by the droplet spectrum, the volumetric median diameter (VMD) is one of the most commonly used. However, its analysis is based on post-application tests, thus preventing action during pesticide spraying. This research aims to implement an artificial neural network (ANN) architecture in an agricultural sprayer with direct injection technology, in order to perform real-time classification of droplet sizes based on measurements of flow rate, pressure, and fluidic resistance from the instruments on the sprayer, as well as historical operational characteristics of the machinery. Therefore, training and testing are performed based on experimental results and tables from manufacturers' technical manuals of the nozzles. The obtained results allow us to characterize that the utilized methodology provides accurate estimations of droplet sizes, thereby enhancing the quality of the application.