International Journal of Advances in Engineering & Scientific Research
Print ISSN : 2349 –4824
Online ISSN : 2349 –3607
Frequency : Continuous
Current Issue : Volume 9 , Issue 2
2022
Saisuman Singamsetty
Saisuman Singamsetty, Senior Consultant San Antonio, TX-78259, USA
DOI : https://doi.org/10.5281/zenodo.14888482 Page No : 01-08
Published Online : 2022-12-30
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Climate change, increasing food demand for a growing population, and industrial expansion has placed tremendous stress on crop yield. An accurate crop yield prediction model is vital for making informed decisions regarding market prices and ensuring sustainable agricultural practices. Existing schemes for crop yield prediction and recommendation suffer from insufficient accuracy and high computational time due to suboptimal parameter settings. To address these challenges, this study introduces an effective decision support system that integrates a high-level feature learning-based ensemble of fine-tuned deep-learning models. The system focuses on crop yield prediction and profitable crop recommendation. A one-dimensional depth-wise separable convolution auto-encoder (1D-DSCAE) is employed for feature extraction, and a Coati-optimized gated recurrent unit (CO-GRU) is used for crop yield prediction. Additionally, a Fuzzy Inference System (FIS) is integrated for decision-making to recommend profitable crops based on predicted yield and market prices. The proposed model demonstrates superior performance in terms of accuracy, precision, recall, F1 score, error, loss, and computational efficiency. Keywords: Crop Yield Prediction, Profitable Crop Recommendation, Deep Learning, Fuzzy Inference System, Coati Optimization, 1D-DSCAE, CO-GRU, Decision Support System |
