MultiYieldFormer: A Multisource Transformer Framework for Time-Series Crop Yield Prediction Using Satellite, Climate, and Soil Data

Accurate prediction of crop yields is fundamental to achieving food security, enhancing farm management, and making data driven decisions due to changing climates. Conventional statistical and machine learning techniques typically do not adequately capture the complex ways that different sources of agricultural data interact over both space and time. Therefore, this research introduces MultiYieldFormer - a novel transformer-based method to forecast crop yield using a wide variety of time-series data collected from remote sensing and environmental factors in combination.

MultiYieldFormer is a framework that integrates Sentinel-1 SAR observation data, Sentinel-2 multispectral imagery, climate data including temperature & rainfall, soil moisture levels and seasonal growth data to improve the modelling of how crops grow and change over time. It uses self-attention and transformer network structures to capture how crops grow and change from one time to another as well as how these changes are related across different types of features. By combining multi-type feature information into seasonal phenology patterns, it allows users to gain insights from the seasonal fluctuations in their crop yields.

Using comprehensive multi-year crop datasets representing kharif crops including rice, maize, and moong bean in the Ludhiana area of Punjab state in India, the results of this experiment have shown that the MultiYieldFormer model improves prediction performance by capturing effective nonlinear relationships between weather and environmental conditions through satellite observations. Comparisons of performance suggest that the model outperforms traditional machine learning and deep learning approaches in terms of generalization and robustness. This proposed framework provides a scalable and transferable approach to estimating regional yield from crops and can serve as a tool for precision agriculture applications, agricultural planning, and sustainable management of crops.