A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks
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| Izdano u: | Computation vol. 13, no. 1 (2025), p. 4 |
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| Glavni autor: | |
| Daljnji autori: | , , , , |
| Izdano: |
MDPI AG
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| Teme: | |
| Online pristup: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2079-3197 | ||
| 024 | 7 | |a 10.3390/computation13010004 |2 doi | |
| 035 | |a 3159418329 | ||
| 045 | 2 | |b d20250101 |b d20250131 | |
| 084 | |a 231446 |2 nlm | ||
| 100 | 1 | |a Ingole, Vikram S |u Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, Maharashtra, India; <email>vikramingole1@gmail.com</email>; Department of Electronics and Telecommunication Engineering, Shri Sant Gajanan Maharaj College of Engineeing, Shegaon 444203, Maharashtra, India | |
| 245 | 1 | |a A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Soybean yield prediction is one of the most critical activities for increasing agricultural productivity and ensuring food security. Traditional models often underestimate yields because of limitations associated with single data sources and simplistic model architectures. These prevent complex, multifaceted factors influencing crop growth and yield from being captured. In this line, this work fuses multi-source data—satellite imagery, weather data, and soil properties—through the approach of multi-modal fusion using Convolutional Neural Networks and Recurrent Neural Networks. While satellite imagery provides information on spatial data regarding crop health, weather data provides temporal insights, and the soil properties provide important fertility information. Fusing these heterogeneous data sources embeds an overall understanding of yield-determining factors in the model, decreasing the RMSE by 15% and improving R2 by 20% over single-source models. We further push the frontier of feature engineering by using Temporal Convolutional Networks (TCNs) and Graph Convolutional Networks (GCNs) to capture time series trends, geographic and topological information, and pest/disease incidence. TCNs can capture long-range temporal dependencies well, while the GCN model has complex spatial relationships and enhanced the features for making yield predictions. This increases the prediction accuracy by 10% and boosts the F1 score for low-yield area identification by 5%. Additionally, we introduce other improved model architectures: a custom UNet with attention mechanisms, Heterogeneous Graph Neural Networks (HGNNs), and Variational Auto-encoders. The attention mechanism enables more effective spatial feature encoding by focusing on critical image regions, while the HGNN captures interaction patterns that are complex between diverse data types. Finally, VAEs can generate robust feature representation. Such state-of-the-art architectures could then achieve an MAE improvement of 12%, while R2 for yield prediction improves by 25%. In this paper, the state of the art in yield prediction has been advanced due to the employment of multi-source data fusion, sophisticated feature engineering, and advanced neural network architectures. This provides a more accurate and reliable soybean yield forecast. Thus, the fusion of Convolutional Neural Networks with Recurrent Neural Networks and Graph Networks enhances the efficiency of the detection process. | |
| 653 | |a Accuracy | ||
| 653 | |a Humidity | ||
| 653 | |a Deep learning | ||
| 653 | |a Agricultural production | ||
| 653 | |a Trends | ||
| 653 | |a Soil properties | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Data sources | ||
| 653 | |a Satellite imagery | ||
| 653 | |a Food supply | ||
| 653 | |a Data integration | ||
| 653 | |a Crop diseases | ||
| 653 | |a Soybeans | ||
| 653 | |a Agriculture | ||
| 653 | |a Precipitation | ||
| 653 | |a Spatial data | ||
| 653 | |a Crop growth | ||
| 653 | |a Soil fertility | ||
| 653 | |a Temperature | ||
| 653 | |a Graph neural networks | ||
| 653 | |a Decision making | ||
| 653 | |a Neural networks | ||
| 653 | |a Recurrent neural networks | ||
| 653 | |a Satellites | ||
| 653 | |a Multisensor fusion | ||
| 653 | |a Rain | ||
| 653 | |a Meteorological data | ||
| 700 | 1 | |a Kshirsagar, Ujwala A |u Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University) (SIU), Lavale, Pune 412115, Maharashtra, India; <email>vikramingole1@gmail.com</email> | |
| 700 | 1 | |a Singh, Vikash |u Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India; <email>bipins.krishna@manipal.edu</email> | |
| 700 | 1 | |a Manish Varun Yadav |u Department of Aeronautical & Automobile Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India | |
| 700 | 1 | |a Krishna, Bipin |u Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, Karnataka, India; <email>bipins.krishna@manipal.edu</email> | |
| 700 | 1 | |a Kumar, Roshan |u Department of Electronic and Information Technology, Miami College, Henan University, Kaifeng 475004, China; <email>roshan.iit123@henu.edu.cn</email> | |
| 773 | 0 | |t Computation |g vol. 13, no. 1 (2025), p. 4 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3159418329/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3159418329/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3159418329/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |