Modelling Specific Energy Requirement for a Power-Operated Vertical Axis Rotor Type Intra-Row Weeding Tool Using Artificial Neural Network

Specific energy prediction is critically important to enhance field performance of agricultural implements. It enables optimal utilization tractor power, reduced inefficiencies, and identification comprehensive inputs for designing energy-efficient In this study, A 3-5-1 artificial neural network (ANN) model was developed estimate specific requirement a vertical axis rotor type intra-row weeding tool. The depth operation in soil bed, cone index, forward/implement speed ratio (u/v) were selected as the input variables. Soil bin investigations conducted using (RVA), interfaced with draft, torque, sensors, data acquisition system record dynamic forces employed during soil–tool interaction at ranges different operating parameters. (DO) had maximum influence on RVA, followed by index (CI) u/v ratio. ANN able predict requirements RVA high accuracies indicated R2 (0.91), low RMSE (0.0197) MAE (0.0479). Findings highlight potential an efficient technique modeling interactions under experimental conditions. Such estimations will eventually optimize efficiency implements field.