Spatiotemporal Deep Learning for Forecasting Vector-Borne Crop Disease Risk across Indian States

Vector-borne crop diseases pose a severe threat to agriculture in India, reducing crop yields and threatening food security. Effective forecasting of disease outbreaks can enable timely interventions, but traditional epidemiological models often struggle with complex spatiotemporal dynamics and climate influences. This paper explores the application of spatiotemporal deep learning models to forecast the risk of major vector-borne and spatially spreading crop diseases (such as rice blast, wheat yellow rust, and leaf curl virus diseases) across different Indian states. We review recent advances in deep learning – including convolutional Long Short-Term Memory (ConvLSTM) networks and spatiotemporal graph convolutional networks (ST-GCN) – which can capture both spatial spread and temporal progression of diseases. Multi-modal data, including weather variables, remote sensing indices, and historical disease incidence, are integrated to train these models. In experimental evaluations, deep learning models significantly outperformed baseline statistical models, achieving high predictive accuracy (e.g., area under ROC > 0.90 in some cases) for outbreak risk forecasting. A case study is presented illustrating how a ConvLSTM model accurately predicts seasonal disease incidence patterns in a sample state. The results demonstrate that spatiotemporal deep learning can provide early warning of crop disease outbreaks, potentially guiding preemptive management strategies. However, challenges remain in terms of data availability, model interpretability, and generalization across diverse crops and regions. This study highlights the promise of deep learning-based forecasting as a component of next-generation crop protection for India, enabling data-driven decisions to mitigate vector-borne disease impacts under current and future climate conditions.

Crop disease forecasting; Spatiotemporal deep learning

1. Chakraborty, S., & Newton, A. C. (2011). Climate change, plant diseases and food security: an overview. Plant Pathology, 60(1), 2–14. [Google Scholar] [Crossref]

2. Gopalakrishnan, M. A., Chellappan, G., Patil, S. G., Rathod, S., Ayyanar, K., Ramasamy, J., … & Swaminathan, M. (2024). Climate-based prediction of rice blast disease using count time series and machine learning approaches. AgriEngineering, 6(4), 4353–4371. [Google Scholar] [Crossref]

3. He, T., Li, M., & Jin, D. (2025). Deep learning-based time series prediction for precision field crop protection. Frontiers in Plant Science, 16, 1575796. [Google Scholar] [Crossref]

4. Jensen, A., et al. (2025). Next generation crop protection: A systematic review of trends in modelling approaches for disease prediction. Computers and Electronics in Agriculture, 204, 107459. [Google Scholar] [Crossref]

5. Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90. [Google Scholar] [Crossref]

6. Kumari, P., Mishra, G., & Srivastava, C. (2014). Time series forecasting of losses due to pod borer, pod fly and productivity of pigeonpea in NWPZ by using artificial neural network. International Journal of Agricultural and Statistical Sciences, 10(1), 15–21. [Google Scholar] [Crossref]

7. Lee, S., & Yun, C. M. (2023). A deep learning model for predicting risks of crop pests and diseases from sequential environmental data. Plant Methods, 19, 145. [Google Scholar] [Crossref]

8. Paul, R., Vennila, S., Bhat, M., Yadav, S., Sharma, V., Nisar, S., & Panwar, S. (2019). Prediction of early blight severity in tomato (Solanum lycopersicum) by machine learning technique. Indian Journal of Agricultural Sciences, 89(1), 169–175. [Google Scholar] [Crossref]

9. Sahoo, S., Singha, C., Govind, A., & Sharma, M. (2025). Leveraging machine learning to predict climate change impact on rice crop disease in Eastern India. Environmental Monitoring and Assessment, 197(4), 366. [Google Scholar] [Crossref]

10. Savary, S., Ficke, A., Aubertot, J. N., & Hollier, C. (2019). The global burden of pathogens and pests on major food crops. Nature Ecology & Evolution, 3(3), 430–439. [Google Scholar] [Crossref]

11. Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. In Advances in Neural Information Processing Systems 28 (NIPS 2015). [Google Scholar] [Crossref]

12. Varma, A., & Malathi, V. G. (2003). Emerging geminivirus problems: A serious threat to crop production. Annals of Applied Biology, 142(2), 145–164. [Google Scholar] [Crossref]

13. Wahyono, Y., Heryadi, Y., Soeparno, H., & Abbas, B. S. (2020). Enhanced LSTM multivariate time series forecasting for crop pest attack prediction. ICIC Express Letters, 14(12), 1193–1200. [Google Scholar] [Crossref]

14. Xu, W., Wang, Q., & Chen, R. (2018). Spatio-temporal prediction of crop disease severity for agricultural emergency management based on recurrent neural networks. GeoInformatica, 22(2), 363–381. [Google Scholar] [Crossref]

15. Zhao, L., Song, Y., Zhang, C., Liu, Y., Wang, X., & Deng, M. (2020). T-GCN: A Temporal Graph Convolutional Network for Traffic Prediction. IEEE Transactions on Intelligent Transportation Systems, 21(9), 3848–3858. [Google Scholar] [Crossref]

• The Impact Of UI/UX Design on User Trust and Task Completion in Civic Tech Platforms
• Solar Cell Photovoltaic Model Shell Sp 75
• Development of an Intelligent Traffic Management System to Address Visibility Obstruction at Urban Intersections: A Case Study of Ibadan Metropolis
• Optimum Placement of Facts Devices on an Interconnected Power Systems Using Particle Swarm Optimisation Technique
• Assessing Construction Transformation and Implication on Future Production Flow System