AI-Assisted Simulation of Organic Granular Fertilizer Spreading: Comparative Evaluation and Experimental Validation

Artificial intelligence (AI) tools are increasingly used to support scientific modeling; however, the reliability of AI-generated mathematical models for predicting agricultural machinery performance remains insufficiently understood. This study evaluated the ability of ChatGPT and Microsoft Copilot to independently formulate numerical models of the transverse distribution of organic granular fertilizers applied using a centrifugal spreader. AI-generated models are essentially statistical–probabilistic models that do not explicitly represent or reconstruct the physical mechanisms underlying particle dynamics. The tools received identical descriptions of the experimental conditions and input parameters. The tools received identical descriptions of the experimental conditions and input parameters. The generated models were subsequently applied under field-based operating conditions and benchmarked against measured fertilizer masses collected across the spreading width. Two types of fertilizer with contrasting physical characteristics—grain husk granules and poultry manure granules—were investigated. Model performance was assessed using average collected mass, transverse distribution characteristics, and coefficient of variation (CV). For grain husk granules, the experimental average mass and CV were 4.93 g and 7.78%, respectively. ChatGPT predicted an average of 5.33 g, whereas Copilot predicted 6.66 g; both models overestimated distribution variability, with CV values of 16.57% and 17.90%, respectively. For poultry manure granules, the experimental average mass and CV were 7.25 g and 16.26%. ChatGPT underestimated the deposited mass (5.83 g) but closely reproduced its variability (CV = 15.10%), while Copilot substantially overestimated both deposition magnitude (14.93 g) and variability (CV = 21.26%). Overall, ChatGPT produced predictions that were more consistent with the experimental distributions, whereas Copilot showed a systematic tendency to overestimate fertilizer deposition. The findings demonstrate that general-purpose AI tools can generate mathematically plausible representations of fertilizer spreading but cannot yet provide reliable quantitative predictions without material-specific calibration and experimental validation.