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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2017: 100.00
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2017: 100.00
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
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Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2018.7(10): 3494-3515
DOI: https://doi.org/10.20546/ijcmas.2018.710.405


Neural Network Prediction of Performance Parameters of an Inclined Plate Seed Metering Mechanism and its Reverse Mapping for Rice
Manisha Sahu* and Ajay Verma
Department of Farm Machinery and Power Engineering, IGKV University,Raipur 492012 (Chhattisgarh), India
*Corresponding author
Abstract:

India is a predominantly agriculture based economy country. Annual population growth rate of the country is nearly 1.8 % and if per capita consumption of rice is expected to be 400 gm of rice per day then the demand for rice in 2025 will be 130 m. tones. For obtaining the high yield with seed planting equipment or planter, it is very essential to drop the paddy seeds in rows maintaining accurate seed rate and seed spacing with minimum damage to seeds during metering. This mainly depends on forward speed of the planting equipment, peripheral speed of metering plate and 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 may pose to difficulty in the determination of inputs for a set of desired outputs (reverse mapping). 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 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. In order to determine the optimum forward speed of the planter, peripheral speed of the metering plate and the area of cells on the plate to obtain the recommended seed rate of 104.68 seeds/m2, seed spacing of 100.04 mm and percent seed damage of 0.19% with 100% fill of the cells, a novel technique of reverse mapping using ANN model was followed. It was observed that the optimum forward speed of the planting equipment and optimum area of cells on the metering plate had good correlation with size of seed. Linear regression equations were developed to predict the optimum forward speed of the planting equipment and optimum area of cells on the metering plate using the size of seeds as independent parameters. The peripheral speed of the metering plate of 0.150 m/s was found to be optimum for the size of seeds in the range of 33.67-41.01 mm2. However the results need to be verified by conducting planting operation under actual field conditions.


Keywords: Neural network prediction,Performance,Inclined plate seed, Metering mechanism
Reverse Mapping for RICE
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How to cite this article:

Manisha Sahu and Ajay Verma. 2018. Neural Network Prediction of Performance Parameters of an Inclined Plate Seed Metering Mechanism and its Reverse Mapping for Rice.Int.J.Curr.Microbiol.App.Sci. 7(10): 3494-3515. doi: https://doi.org/10.20546/ijcmas.2018.710.405