Advanced Forecasting of Crop Prices Through Graph Convolutional Model with Gated Recurrent Neural Networks Enhanced by Heuristic Search Optimisation Algorithm

International Journal of Electronics and Communication Engineering

© 2025 by SSRG - IJECE Journal

Volume 12 Issue 12

Year of Publication : 2025

Authors : J. Jagadeesan, R. Nagarajan

10.14445/23488549/IJECE-V12I12P106

How to Cite?

J. Jagadeesan, R. Nagarajan, "Advanced Forecasting of Crop Prices Through Graph Convolutional Model with Gated Recurrent Neural Networks Enhanced by Heuristic Search Optimisation Algorithm," SSRG International Journal of Electronics and Communication Engineering, vol. 12,  no. 12, pp. 60-74, 2025. Crossref, https://doi.org/10.14445/23488549/IJECE-V12I12P106

Abstract:

Crop price prediction is crucial in reducing market uncertainties and supporting knowledgeable decision-making among farmers, traders, and policymakers. Conventional techniques are relatively simple and easy to understand and implement; however, they provide low prediction performance for non-linear, non-smooth, and high-dimensional data, and they need more a priori knowledge and assumptions. Recently, the Machine Learning (ML) and Deep Learning (DL) models have helped model these non-linear patterns, providing highly reliable and robust crop price prediction. This research paper presents an Advanced Forecasting of Crop Prices through Graph Convolutional Neural Networks by Heuristic Search Optimisation (AFCP-GCNNHSO) approach. The main intention of the AFCP-GCNNHSO approach is to enable effective prediction of crop prices by the use of feature selection and a fine-tuned DL model. To handle high-dimensional data, the AFCP-GCNNHSO method utilizes the Aquila Optimiser (AO) technique for the optimal feature subset selection, which results in reduced complexity and enhanced prediction performance. For effective crop price prediction, the AFCP-GCNNHSO method employs an Enhanced Grasshopper Optimization Algorithm (EGOA) with a Gated Graph Convolution Neural Network (GGCN) technique. The EGOA based hyperparameter tuning is implemented to optimize the GGCN model by minimizing prediction error. The experimental analysis of the AFCP-GCNNHSO method is performed using the agricultural dataset from the Kaggle repository, and the comparison results highlight its supremacy with a minimal MSE of 0.00038.

Keywords:

Crop Prices Prediction, Graph Convolutional Neural Network, Enhanced Grasshopper Optimisation Algorithm, Feature Selection.

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