Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt

Forecasting crop yields is becoming increasingly important under the current context in which food
security needs to be ensured despite the challenges brought by climate change, an expanding world
population accompanied by rising incomes, increasing soil erosion, and decreasing water resources.
Temperature, radiation, water availability and other environmental conditions influence crop growth,
development, and final grain yield in a complex nonlinear manner. Machine learning (ML)
techniques, and deep learning (DL) methods in particular, can account for such nonlinear relations
between yield and its covariates. However, they typically lack transparency and interpretability, since
the way the predictions are derived is not directly evident. Yet, in the context of yield forecasting,
understanding which are the underlying factors behind both a predicted loss or gain is of great
relevance. Here, we explore how to benefit from the increased predictive performance of DL methods
while maintaining the ability to interpret how the models achieve their results. To do so, we applied a
deep neural network to multivariate time series of vegetation and meteorological data to estimate the
wheat yield in the Indian Wheat Belt. Then, we visualized and analyzed the features and yield drivers
learned by the model with the use of regression activation maps. The DL model outperformed other
tested models (ridge regression and random forest) and facilitated the interpretation of variables and
processes that lead to yield variability. The learned features were mostly related to the length of the
growing season, and temperature and light conditions during this time. For example, our results
showed that high yields in 2012 were associated with low temperatures accompanied by sunny
conditions during the growing period. The proposed methodology can be used for other crops and
regions in order to facilitate application of DL models in agriculture.