Anis Elaoud, Hanen Ben Hassen, Rim Jalel, Nahla Ben Salah, Afif Masmoudi, Atef Masmoudi
Journal of Terramechanics, Volume 110, 2023, Pages 39-45, ISSN 0022-4898
https://doi.org/10.1016/j.jterra.2023.08.002.(https://www.sciencedirect.com/science/article/pii/S0022489823000678)
Abstract: The investigation and evaluation of the phenomenon of soil compaction after the passage of repetitive equipment (Tractor, cultivator..) is considered as a preventive solution to preserve agricultural soils against degradation and maintain sustainable agriculture. In reality, there is currently no reliable method for predicting the primary causes of soil compaction, particularly in moist soils. This study applicates Artificial Neural Network (ANN) modeling to make a resistance penetration prediction. Resistance penetration (Rp) test data acquired from measured experimental values are used to train the models. The learning score coefficient (0.96), the RMSE (0.51) and MAE (0.39) show that the forecasts from the ANN models coincide with the measured field data. It is concluded that the developed ANN models would be used effectively to make predictions that are more accurate on the soil state compaction in different moisture conditions. This work will help the farmers to optimize the machine use and hence to enhance production and improve yields with minimal costs.
Keywords: Soil resistance; Moisture; Artificial Neural Network; Modeling