Climate-aware Crop Yield Prediction across Indian Agro-climatic Zones Using Hybrid Deep Learning and Zone-specific Transfer Learning
Abstract
Predicting crop yield has remained difficult for India, primarily due to uneven agro-climatic conditions, where factors like rainfall, temperature, soil type, and crop management vary greatly and restrict the application of existing predictive models. Most machine-learning and statistical models take uniform climate conditions as a given, and thus their performance declines sharply when used in different agro-climatic zones. This study addresses this gap and proposes a climate-aware crop yield prediction model combining hybrid deep learning with zone-specific transfer learning to improve robustness and adaptability to different climate conditions. The model integrates agro-meteorological data with climate data and a constructed variable representation obtained from a combination of different sources, comprising weather data, soil data, and remote sensing data (satellite vegetation indices) over time. The first step involves training a global deep learning model on the national agricultural data to capture crop-climate relationships. In the second step, the global model is fine-tuned using zone-specific transfer learning for each agro-climatic zone with a few samples. Evaluation experiments on different crops show that the machine learning approaches’ prediction error is reduced by 12 to 25%, and combined with other zone-agnostic deep learning models by an additional 9 to 18%. The model demonstrates more stable learning, faster convergence, and lower inter-seasonal variability, indicating robust performance under variable climate conditions. The findings validating the integration of agro-climatic awareness and transfer learning as a means to achieve scalable and climate-resilient forecasting of crop yields further offer a sound basis for precision agriculture and agro-policy framework in India.
References
D. Lobell and C. Field, “Global scale climate-crop yield relationships and the impacts of recent warming,” Environmental Research Letters, vol. 2, no. 1, pp. 1–7, 2007.
Y. You et al., “Progress in joint application of crop models and hydrological models,” Agricultural Water Management, vol. 295, p. 108746, Apr. 2024.
L. K. Subramaniam and R. Marimuthu, “Crop yield prediction using effective deep learning and dimensionality reduction approaches for Indian regional crops,” e-Prime – Advances in Electrical Engineering, Electronics and Energy, vol. 8, p. 100611, May 2024.
A. S. Menon, A. J., R. Sankaran, and K. P., “Deep learning based farm-level crop yield prediction using multi-temporal satellite data for complex engineering application,” Smart Agricultural Technology, vol. 12, p. 101562, Dec. 2025.
S. Khaki and L. Wang, “Crop yield prediction using deep neural networks,” Frontiers in Plant Science, vol. 10, May 2019.
A. Joshi, B. Pradhan, S. Chakraborty, R. Varatharajoo, S. Gite, and A. Alamri, “Deep-transfer-learning strategies for crop yield prediction using climate records and satellite image time-series data,” Remote Sensing, vol. 16, no. 24, p. 4804, Dec. 2024.
A. Crane-Droesch, “Machine learning methods for crop yield prediction and climate change impact assessment in agriculture,” Environmental Research Letters, vol. 13, no. 11, p. 114003, Oct. 2018.
J. Lu et al., “Deep learning for multi-source data-driven crop yield prediction in Northeast China,” Agriculture, vol. 14, no. 6, p. 794, May 2024.
A. Kumar, S. Dwivedi, C. S. P. Ojha, and V. P. Singh, “Wheat yield modelling in selected agro-climatic zones of India,” Irrigation & Drainage Systems Engineering, vol. 11, no. 10, pp. 1–9, Oct. 2022.
T. van Klompenburg, A. Kassahun, and C. Catal, “Crop yield prediction using machine learning: A systematic literature review,” Computers and Electronics in Agriculture, vol. 177, p. 105709, Oct. 2020.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, Nov. 1997.
A. Vaswani et al., “Attention is all you need,” in Proc. Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 5998–6008.
L. Kaur, S. Yadav, D. Kalkal, and H. S. Jat, “Climate smart agriculture (CSA) for sustainable resource management,” in Climate Smart Agriculture and Sustainable Resource Management, Nov. 2024, pp. 233–248.
H. Park, S. Song, T. H. Nguyen, and L. Park, “Machine learning for Internet of Things: Applications and discussions,” in Proc. International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Osaka, Japan, Feb. 2024, pp. 459–462.
R. Kashyap and A. Piersson, “Big data challenges and solutions in the medical industries,” in Handbook of Research on Pattern Engineering System Development for Big Data Analytics, 2018, pp. 1–24.
R. Kashyap and P. Gautam, “Fast level set method for segmentation of medical images,” in Proc. ACM International Conference Proceeding Series, Jaipur, India, Aug. 2016, pp. 1–7.
T. He, M. Li, and D. Jin, “Deep learning-based time series prediction for precision field crop protection,” Frontiers in Plant Science, vol. 16, Jun. 2025.
H. Yao, Y. Liu, Y. Wei, X. Tang, and Z. Li, “Learning from multiple cities: A meta-learning approach for spatial-temporal prediction,” arXiv preprint, 2019.
R. Akhter and S. A. Sofi, “Precision agriculture using IoT data analytics and machine learning,” Journal of King Saud University – Computer and Information Sciences, vol. 34, no. 8, pp. 5602–5618, Jun. 2021.
J. V. Gripsy, M. J. Mythili, S. Mohanapriya, G. K. Varshini, and R. D. Shri, “Hybrid deep learning framework for crop yield prediction and weather impact analysis,” International Journal for Research in Applied Science and Engineering Technology, vol. 8, no. 18, pp. 1563–1568, Aug. 2025.
A. Arunachalam and A. K. Misra, DARE-ICAR Global Reach. New Delhi, India: Indian Council of Agricultural Research, 2020, p. 30.
K. P. Mayuri and S. Kathavate, “Zone-aware greenhouse control and multitask crop yield prediction using ConvLSTM and EMMYP-Net,” Engineering, Technology & Applied Science Research, vol. 16, no. 1, pp. 31874–31879, 2026.
