Optimizing the seed-cell filling performance of an inclined plate seed metering device using integrated ANN-PSO approach

Uniformseed distribution within the row is the prime objective of precision planters for better crop growth and yield.Inclined plate planters are generally used for sowing bold seeds likemaize,groundnut,chickpea,and their operating parameters like the forward speed of operation,the seedmetering plate inclination,and the seed level in the hopper affect the cell fill and subsequently the uniformseed distribution.Therefore,to achieve precise seed distribution,these parameters need to be optimized.In the present study,out of the different optimization techniques,a new intelligent optimization technique based on the integrated ANN-PSO approach has been used to achieve the set goal.A 3–5-1 artificial neural network(ANN)model was developed for predicting the cell fill of inclined plate seedmetering device,and the particle swarmoptimization(PSO)algorithmwas applied to obtain the optimum values of the operating parameters corresponding to 100%cell fill.The most appropriate optimal values of the forward speed of operation,the seed metering plate inclination,and the seed level in the hopper for achieving 100%cell fill were found to be 3 km/h,50-degree,and 75%of total height,respectively.The proposed integrated ANN-PSO approach was capable of predicting the optimal values of operating parameters with amaximumdeviation of 2%compared to the experimental results,thus confirmed the reliability of the proposed optimization technique.

Micro-sonic sensor technology enables enhanced grass height measurement by a Rising Plate Meter

Globally,the Rising Plate Meter(RPM)is a device used to measure compressed sward height,to enable estimation of herbage mass.Despite improved farm management practices aided by a variety of technological advances,the standard design of a RPM has remained relatively unchanged.Recently,however,a RPM utilising a micro-sonic sensor,with digital data capture capability via a Bluetooth communications link to a smart device application,has been developed.Here,we assess the comparable ability of both a standard cumulative ratchet counter RPM and the micro-sonic sensor RPM,to accurately and precisely measure fixed heights.Moreover,as correct allocation of grazing area requires accurate geolocation positioning,we assess the associated GPS technology.The micro-sonic sensor RPM was significantly more accurate for height capture than the cumulative ratchet counter RPM.Overall,across all heights,the cumulative ratchet counter RPM underestimated height by 7.68±0.06mm(mean±SE).Alternatively,the micro-sonic sensor RPM overestimated height by 0.18±0.08 mm.In relation to a practical applications,these discrepancies can result in an under-and overestimation of dry matter yield by 13.71%and 0.32%kilograms per hectare,respectively.The performance of the on-board GPS did not significantly differ from that of a tertiary device.Overall,the wireless technology,integrated mapping,and decision support tools offered by the innovative micro-sonic sensor RPM provides for a highly efficacious grassland management tool.