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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 2018: 95.39
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2018: 95.39
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2018.7(4): 803-810
DOI: https://doi.org/10.20546/ijcmas.2018.704.090


Yield Prediction in Brinjal (Solanum melongena CV MAHYCO-11) Across Different Growth Stages Using ANN Models
R. Hanumanthaiah1*, R. Venugopalan2, K. Padmini2 and K.J. Yogeesh1
1Department.of Farm Engineering, IAS, Banaras Hindu University, Varanasi, Uttar Pradesh-221005, India
2Indian Institute of Horticultural Research, Bengaluru-560089, Karnataka, India
*Corresponding author
Abstract:

An attempt has been made in the present investigation to assess the influence of various biometrical characters across different growth stages in Brinjal crop yield along with various yield attributing characters across four growth stages using ANN models. Results of seedling stage had indicated that plant height, girth and number of primary branches could together predict the crop yield to an extent of 83 % for training set and 62 % for validation of model accuracy. Normalized importance was 0.225, 0.142, and 0.131 respectively. In case of Vegetative stage plant height, girth and number of leaves could together predict the crop yield to an extent of 89 %, for training set and 85 % for validation of model accuracy and Normalized importance was 0.126, 0.098 and 0.133, respectively. Where as in Flowering stage both the plant spreads (east-west and north-south) could together predict the crop yield to an extent of 88 % for training set and 64 % for validation of model accuracy and Normalized Importance was 0.220 and 0.245 respectively. Finally for fruiting stage plant height, number of primary branches and plant spread (east-west) could together predict the crop yield to an extent of 68 % for training set and 64 % model accuracy. Normalized importance was 0.229, 0.134 and 0.227 respectively.


Keywords: Artificial neural network, Biometrical traits, Crop logging, Growth stages, Root mean square error, Precision farming
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How to cite this article:

Hanumanthaiah, R., R. Venugopalan, K. Padmini and Yogeesh, K.J. 2018. Yield Prediction in Brinjal (Solanum melongena CV MAHYCO-11) Across Different Growth Stages Using ANN Models.Int.J.Curr.Microbiol.App.Sci. 7(4): 803-810. doi: https://doi.org/10.20546/ijcmas.2018.704.090