1 research outputs found
Not Available
Not AvailableSpecific energy prediction is critically important to enhance field performance of agri cultural 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 (u/v) were selected as the input variables. Soil bin investigations
were conducted using the vertical axis rotor (RVA), interfaced with draft, torque, speed sensors, and
data acquisition system to record dynamic forces employed during soil–tool interaction at ranges
of different operating parameters. The depth of operation (DO) had the maximum influence on
the specific energy requirement of the RVA, followed by the cone index (CI) and the u/v ratio. The
developed ANN model was able to predict the specific energy requirements of RVA 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–tool interactions under specific
experimental conditions. Such estimations will eventually optimize and enhance the performance
efficiency of agricultural implements in the field.Not Availabl