Development of artificial neural network models for the performance prediction of an inclined plate seed metering device

Authors:
M. Anantachar;G.V. Prasanna Kumar;T. Guruswamy
Affiliations:
Department of Farm Machinery, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584101, Karnataka, India;Department of Agricultural Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli 791109, Itanagar, Arunachal Pradesh, India;Department of Farm Machinery, College of Agricultural Engineering, University of Agricultural Sciences, Raichur 584101, Karnataka, India
Venue:
Applied Soft Computing
Year:
2011

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Abstract

Seed planting equipment with inclined plate seed metering devices is the most commonly used equipment for planting of peanut crop in India. For obtaining the high yield, it is very essential to drop the peanut seeds in rows maintaining accurate seed rate and seed spacing with minimum damage to seeds during metering. This mainly depends on the forward speed of the planting equipment, rotary speed of the metering plate and the area of cells on the plate. The relationship between these factors and the performance parameters viz., seed rate, seed spacing and percent seed damage can be established using regression analysis. But they may not be very accurate and consistent throughout the solution space. Hence, an attempt has been made in this paper to develop the feed forward artificial neural network (ANN) models for the prediction of the performance parameters of an inclined plate seed metering device. The data were generated in the laboratory by conducting experiments on a sticky belt test stand provided with a seed metering device and an opto-electronic seed counter. The generated data was used to develop both statistical and neural network models. The optimum architecture of the neural network models was determined using genetic algorithm (GA) as a single objective constrained optimization problem. The performance of the developed models was compared among themselves for 4 randomly generated test cases. The results show that the ANN model predicted the performance parameters of the seed metering device better than the statistical models. It is possible to determine the optimum levels of the input parameters to obtain the desired performance parameters of the seed metering device by performing reverse mapping of the developed ANN models.