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.

A fuzzy logic algorithm derived mechatronic concept prototype for crop damage avoidance during eco-friendly eradication of intra-row weeds

Crop damage during the intra-row weed eradiation is one of the biggest challenges in intercultural agricultural operations.Several available mechanical systems provide effective weeding but result in excess crop damage.On the other hand,chemical based systems have been raising serious environmental and food concerns.This study presents development of a cost-effectivemechatronic prototype for intra-rowweeding operation.The primary focus was on incurring minimal crop damage.The system integrates time of flight and inductive sensing into fuzzy logic algorithm for electronic control of a four-bar linkage mechanism(FBLM).The crank of FBLM was connected to the vertical rotary weed control shaft with weeding blades.The crop sensing triggers the electronic control to laterally shift the control shaft away from crop,proportional to the forward speed and soil conditions.The developed algorithm incorporates varied conditions of soil,forward speed,and plant spacing to calculate dynamic lateral shift speed(SRPM).The prototype was evaluated to determine the relationships between the operating conditions and electronic control parameters.Moreover,the plant damage was assessed under varied conditions of plant spacing,forward speeds,soil cone index,operational depth and electronic control parameters.The derived SRPM was established as the ultimate governing factor for avoiding crop damage that varied significantlywith electronic response time and soil strength(P<0.05).Plant damage increased significantly under higher forward speeds and lower plant spacing(P<0.05).Preliminary field evaluation of the developed prototype showed a significant potential of this system for effective control on weeds(>65%)and crop damage(<25%).