Kumar, Satya PrakashTewari, V. K.Chandel, Abhilash KumarMehta, C. R.Pareek, C. M.Chethan, C. R.Nare, Brajesh2023-09-272023-09-272023-09-07Kumar, S.P.; Tewari, V.K.; Chandel, A.K.; Mehta, C.R.; Pareek, C.M.; Chethan, C.R.; Nare, B. Modelling Specific Energy Requirement for a Power-Operated Vertical Axis Rotor Type Intra-Row Weeding Tool Using Artificial Neural Network. Appl. Sci. 2023, 13, 10084.http://hdl.handle.net/10919/116362Specific energy prediction is critically important to enhance field performance of agricultural implements. It enables optimal utilization of tractor power, reduced inefficiencies, and identification of comprehensive inputs for designing energy-efficient implements. In this study, A 3-5-1 artificial neural network (ANN) model was developed to estimate specific energy requirement of a vertical axis rotor type intra-row weeding tool. The depth of operation in soil bed, soil cone index, and forward/implement speed ratio (<i>u</i>/<i>v</i>) were selected as the input variables. Soil bin investigations were conducted using the vertical axis rotor (R_VA), interfaced with draft, torque, speed sensors, and data acquisition system to record dynamic forces employed during soil&ndash;tool interaction at ranges of different operating parameters. The depth of operation (DO) had the maximum influence on the specific energy requirement of the R_VA, followed by the cone index (CI) and the <i>u</i>/<i>v</i> ratio. The developed ANN model was able to predict the specific energy requirements of R_VA at high accuracies as indicated by high R² (0.91), low RMSE (0.0197) and low MAE (0.0479). Findings highlight the potential of the ANN as an efficient technique for modeling soil&ndash;tool interactions under specific experimental conditions. Such estimations will eventually optimize and enhance the performance efficiency of agricultural implements in the field.application/pdfenCreative Commons Attribution 4.0 Internationalintra-row weedingspecific energyartificial neural networkperformance efficiencyArticle - Refereed2023-09-27https://doi.org/10.3390/app131810084