| Autor: |
Gokuldhev, M., Raju, D. Naveen, Rajan, R. Ashoka, Indu, V. Thanammal |
| Zdroj: |
International Journal of Information Technology; October 2022, Vol. 14 Issue: 6 p3123-3131, 9p |
| Abstrakt: |
In recent years, the internet of things (IoT) based sensors are deployed on the farmland to monitor the crops and soil suitability. The early prediction of drought plays an important part in farming which alerts the farmers and reduces the harmful effects on crop production. To enhance crop productivity and drought prediction rate, an Intuitionistic fuzzy kernel ridge regression model-based Darts Game optimization (IFKRR-DGO) method is proposed in this paper. In this work, the feature selection is performed using a modified Latent Dirichlet Allocation (LDA) model and the efficiency of the proposed method is computed by utilizing three crop datasets. As compared with other state of art methods, the proposed method achieves greater robustness and accuracy in predicting drought and enhancing the crop productivity for Sugarcane, Soybean and Bajra crops. The experimental analysis is carried out for various state of art methods and from the results, it is demonstrated that the proposed IFKRR-DGO method attains a higher accuracy rate in terms of predicting drought (95%) and the crop production rate for three datasets (99.58%, 98.75% and 94.9%). |
| Databáze: |
Supplemental Index |
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