Forecasting the Factors Responsible for Improving the Yield of Sugarcane Crop using Artificial Neural Network
| International Journal of Electronics and Communication Engineering |
| © 2023 by SSRG - IJECE Journal |
| Volume 10 Issue 7 |
| Year of Publication : 2023 |
| Authors : S. Muthukumaran, N. Gnanasankaran, Sandhya Soman, G. Rakesh |
10.14445/23488549/IJECE-V10I7P104
How to Cite?
S. Muthukumaran, N. Gnanasankaran, Sandhya Soman, G. Rakesh, "Forecasting the Factors Responsible for Improving the Yield of Sugarcane Crop using Artificial Neural Network," SSRG International Journal of Electronics and Communication Engineering, vol. 10, no. 7, pp. 33-43, 2023. Crossref, https://doi.org/10.14445/23488549/IJECE-V10I7P104
Abstract:
Sugarcane is a cash crop cultivated in India's Tropical and Sub Tropical regions, contributing 5.7% of the Gross Domestic Product (GDP) to the Indian Economy. Sugarcane Farming gives employment opportunities to 60 million rural families. It is cultivated all over India to a latitude of 80◦ N to 300◦ N. Sugarcane contains sucrose in its stem juice and is the primary raw material for producing sugar. Sugarcane crop production depends on the season, biological, and economic cause. The growing demand for sugarcane worldwide incorporates the backbone of sugarcane agriculture. This paper proposed A Hybrid Machine Learning Model (HMLM) for forecasting the sugarcane yield, which helps both farmers and the sugar mills to assist in annual planning. The proposed model used Backward Feature Elimination to select the factors that dominate sugarcane production. K-Means Clustering (K-MC) is applied to the selected attributes, and the dataset is partitioned. An Artificial Neural Network (ANN) is created for each clustered dataset, and the parameters influencing sugarcane production are found. The performance of each Neural Network (NN) created was analysed by implementing performance measures, and the findings were compiled. The information gained from the suggested model aids farmers in making decisions to increase sugarcane crop productivity.
Keywords:
Artificial Neural Network, Backward feature elimination, K-means clustering, Machine learning, Sugarcane production.
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