ItemArticle

A hybrid GNNvanilla vision transformer model for IoT-based soil and crop forecasting

Title
A hybrid GNNvanilla vision transformer model for IoT-based soil and crop forecasting
Creator
Mallick, Shrabani; Suhas, S.; John, Tegil J.; Mallick, Soubhagya Ranjan; Chaudhary, Neha; Tumma, Shyam Sunder; Behera, Aurobinda
Description
In this work, we propose a Graph?Neural Network (GNN) and Vanilla Transformer-based hybrid model for IoT driven soil and crop prediction. Conventional forecasting approaches are unable to model complicated spatial and temporal inter-dependencies and are not?very effective. The given paper solves this problem by using GNNs to learn the spatial relationships among the IoT sensor nodes and vanilla transformer model to?learn the temporal dependencies in crop and weather data. Vanilla vision transformer is able to recover missing contextual information during training. It is trained on data from IoT sensors that monitor soil moisture, temperature, humidity and a variety of other environmental factors as?well as historical crop yield and weather related information. The hybrid model can enable the real-time accurate prediction for crop?yield production and soil health status, which enables a smarter agriculture decision. The experimental results show that the proposed work achieves the lowest root mean square error (RMSE 2.1) and the highest crop accuracy (92%) for?short-term and long-term forecasts. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.
Source
International Journal of Information Technology (Singapore);Volume;17;Issue;9;pp.5713-5719
Date
01-01-2025
Publisher
Springer Science and Business Media B.V.
Subject
Crop yield prediction; GNN; IoT-based forecasting; Precision agriculture; Spatial-temporal modeling; Vanilla vision transformer
Coverage
Mallick S., Department of CSE, Dr. B.R Ambekar Institute of Technology Port, Blair, India; Suhas S., Department of Computer Science and Engineering, SJCE, JSS Science and Technology University, Karnataka, Mysore, India; John T.J., Computer Science and Engineering, Christ University, Bangalore, India; Mallick S.R., Symbiosis Institute of Technology, Symbiosis International (Deemed University), Nagpur Campus, Pune, India; Chaudhary N., Research and Development Cell, Swami Vivekanand Subharti University, Uttar Pradesh, Meerut, India; Tumma S.S., Department of MBA, Auroras PG College (MBA), Hyderabad, Ramanthapur, India; Behera A., Department of Vegetable Science, College of Agriculture, Odisha University of Agriculture and Technology (OUAT), Odisha, Bhubaneswar, 751003, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 25112104;
Format
online
Language
English
Type
Article

Citation

Mallick, Shrabani; Suhas, S.; John, Tegil J.; Mallick, Soubhagya Ranjan; Chaudhary, Neha; Tumma, Shyam Sunder; Behera, Aurobinda, “A hybrid GNNvanilla vision transformer model for IoT-based soil and crop forecasting,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/22103.

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