Search

Context. Soil-tool interaction modelling and optimisation reduce manufacturing costs and energy requirements for precision tillage equipment design. Diverse tillage tools have been designed to reduce draft requirements and desirable soil disturbance, but this is not fully understood. Aims. The current study investigated the effects of tool width, cone index, depth, and forward speed on draft with corresponding rupture width in order to develop response surface methodology (RSM) and artificial neural network (ANN) models and compared them to other models in order to predict draft and rupture width. Methods. Experiments were carried out in a soil bin with a vertisol, and rupture width was measured using an image processing technique. Key results. Using RSM, the optimum values for minimum draft with maximum rupture width within a range of independent variables were found to be l00 mm tool width, 600 kPa cone index, 141.63 mm tillage depth, and 3 km/h forward speed. For predicting the draft, the coefficients of determination ([R.sup.2]) for ANN and RSM models were 0.997 and 0.987, respectively; for rupture width prediction, [R.sup.2] were 0.921 and 0.976. Conclusions. Developed ANN and RSM models of draft and rupture width were better than other analytical or numerical models, and both models' predictions were in good agreement with experiment values within the range of [+ or -] 5% uncertainty. Implications. The developed models can be used to predict the draft and soil disturbance requirements of tillage tools and design precision tillage tools. Keywords: artificial neural network, cone index, draft, image processing, modelling, response surface methodology, rupture width, vertisols., Introduction Tillage operations are an unavoidable step in agricultural production systems, and consume energy and time. For a proper tillage operation, extensive knowledge of soil characteristics before, during, and after [...]

Modelling and optimizing soil-tool interaction parameters for tillage operations is crucial for developing efficient and precise tools. This study focused on a specific draft of sweep tools in the soil bin filled with vertisol, considering factors such as tool geometry, cone index, working depth and operational speed. Data analysis showed that the range of specific draft values, from 9.51 to 38.95 kN/m2. Machine learning models, including artificial neural network (ANN), support vector machine (SVM), bagged trees (BT) and boosted trees (BoT), were developed using experimental data to predict the specific draft of sweep tools with hyperparameter configuration. The developed machine learning models have also been compared with the predictive multiple linear regression (MLR) model, and it was found that the predictive performance of the machine learning models was better than the MLR model during training and testing. The fine-tuned ANN model achieved impressive statistical performance with the lowest mean absolute error (MAE) of 0.489 kN/m2, root mean square error (RMSE) of 0.619 kN/m2, standard error of prediction (SEP) of 3.462% and highest coefficient of determination (R2) of .99 during testing. R2 values for BT, BoT, SVM and MLR models were .97, .96, .94 and .83, respectively, for specific draft predictions. The findings from this study have practical implications for optimizing sweep tool design and improving tillage operation. Manufacturers and farmers can benefit from predictive modelling using machine learning to design and select appropriate tillage tools for specific soil conditions. This approach can lead to improved soil health, increased yields and reduced costs. [ABSTRACT FROM AUTHOR]