Agricultural machines that carry mobile tools or implements have an internal dynamic that makes them more prone to rollover risk than machines with fixed configurations. Previous research has led to the development of algorithms capable of predicting rollover risk in real time by estimating relevant metrics, primarily the LTR (Load Transfer Ratio), with a focus on vehicles with static configurations. This paper presents the adaptation of these algorithms for machines with mobile tools and describes the field tests, conducted on a self-propelled sprayer, to evaluate their effectiveness in predicting rollover. The tests also aimed to acquire the necessary data to build an accurate numerical model of the vehicle and produce high-quality simulated tests. This paper describes the chosen instrumentation, the test procedure, and the experimental design of these tests. These field tests include static tests and dynamic tests. Static tests determine vehicle parameters, while dynamic tests measure variables related to the machine displacement and wheel/ground forces in real time. The initial results demonstrate the algorithms' ability to capture variations in LTR, which express lateral and longitudinal rollover risk. The data collected through these tests will enable the vehicle displacements to be replayed by simulation, producing simulated rollover accidents and evaluating the effectiveness of the developed algorithms in accident prevention.

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