Bhardwaj, MR and Pawar, J and Bhat, A and Enaganti, I and Sagar, K and Narahari, Y (2023) An Innovative Deep Learning Based Approach for Accurate Agricultural Crop Price Prediction. In: 19th IEEE International Conference on Automation Science and Engineering, CASE 2023, 26-30 August 2023, Auckland, New Zealand.
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Abstract
Accurate prediction of agricultural crop prices is a crucial input for decision-making by various stakeholders in agriculture: farmers, consumers, retailers, wholesalers, and the Government. These decisions have significant implications including, most importantly, the economic well-being of the farmers. In this paper, our objective is to accurately predict crop prices using historical price information, climate conditions, soil type, location, and other key determinants of crop prices. This is a technically challenging problem, which has been attempted before. In this paper, we propose an innovative deep learning based approach to achieve increased accuracy in price prediction. The proposed approach uses graph neural networks (GNNs) in conjunction with a standard convolutional neural network (CNN) model to exploit geospatial dependencies in prices. Our approach works well with noisy legacy data and produces a performance that is at least 20 better than the results available in the literature. We are able to predict prices up to 30 days ahead. We choose two vegetables, potato (stable price behavior) and tomato (volatile price behavior) and work with noisy public data available from Indian agricultural markets. © 2023 IEEE.
| Item Type: | Conference Paper |
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| Publication: | IEEE International Conference on Automation Science and Engineering |
| Publisher: | IEEE Computer Society |
| Additional Information: | The copyright for this article belongs to the IEEE Computer Society. |
| Keywords: | Convolutional neural networks; Costs; Decision making; Deep learning; Forecasting; Graph neural networks; Neural network models, Accurate prediction; Agricultural crops; Climate condition; Decisions makings; Key determinants; Learning-based approach; Price information; Price prediction; Soil types; Well being, Crops |
| Department/Centre: | Division of Electrical Sciences > Computer Science & Automation |
| Date Deposited: | 03 Dec 2023 06:25 |
| Last Modified: | 03 Dec 2023 06:25 |
| URI: | https://eprints.iisc.ac.in/id/eprint/83461 |
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