Explainable deep learning to predict and understand crop yield estimates
Authors: Wolanin Aleksandra , Mateo-García Gonzalo , Camps-Valls Gustau , Gómez-Chova Luis , Meroni Michele , Duveiller Gregory , Liangzhi You , Guanter Luis 
Published: March 2020

&lt;p&gt;&lt;span&gt;Estimating crop yields &lt;/span&gt;&lt;span&gt;is &lt;/span&gt;&lt;span&gt;becom&lt;/span&gt;&lt;span&gt;ing&lt;/span&gt;&lt;span&gt; increasingly &lt;/span&gt;&lt;span&gt;relevant &lt;/span&gt;&lt;span&gt;under the current context &lt;/span&gt;&lt;span&gt;of &lt;/span&gt;&lt;span&gt;an expanding world population accompanied by rising incomes &lt;/span&gt;&lt;span&gt;in a changing climate&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;C&lt;/span&gt;&lt;span&gt;rop growth, &lt;/span&gt;&lt;span&gt;crop &lt;/span&gt;&lt;span&gt;development, and final grain yield &lt;/span&gt;&lt;span&gt;are all determined by environmental conditions &lt;/span&gt;&lt;span&gt;in a complex nonlinear manner. &lt;/span&gt;&lt;span&gt;M&lt;/span&gt;&lt;span&gt;achine learning &lt;/span&gt;&lt;span&gt;(&lt;/span&gt;&lt;span&gt;ML&lt;/span&gt;&lt;span&gt;), &lt;/span&gt; &lt;span&gt;and deep learning &lt;/span&gt;&lt;span&gt;(&lt;/span&gt;&lt;span&gt;DL&lt;/span&gt;&lt;span&gt;) &lt;/span&gt;&lt;span&gt;methods in particular, can account for such nonlinear relations between yield and its &lt;/span&gt;&lt;span&gt;drivers&lt;/span&gt;&lt;span&gt;. However, they typically lack transparency and interpretability, &lt;/span&gt;&lt;span&gt;which &lt;/span&gt;&lt;span&gt;in the context of yield forecasting is of great &lt;/span&gt;&lt;span&gt;relevance&lt;/span&gt;&lt;span&gt;. Here, we explore how to bene&lt;/span&gt;&lt;span&gt;fi&lt;/span&gt;&lt;span&gt;t from the increased predictive performance of DL methods &lt;/span&gt;&lt;span&gt;without compromising&lt;/span&gt;&lt;span&gt; the ability to interpret how the models achieve their results &lt;/span&gt;&lt;span&gt;for an example of the wheat yield in the Indian Wheat Belt&lt;/span&gt;&lt;span&gt;.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;W&lt;/span&gt;&lt;span&gt;e applied a &lt;/span&gt;&lt;span&gt;convolutional neural network &lt;/span&gt;&lt;span&gt;to multivariate time series of meteorological and satellite-derived vegetation variables at a daily resolution to estimate the wheat yield in the Indian Wheat Belt. &lt;/span&gt;&lt;span&gt;Afterwards&lt;/span&gt;&lt;span&gt;, the features and yield drivers learned by the model were visualized and analyzed with the use of regression activation maps. &lt;/span&gt;&lt;span&gt;The learned features were &lt;/span&gt;&lt;span&gt;primarily &lt;/span&gt;&lt;span&gt;related to the length of the growing season, temperature, and light conditions during the growing season. &lt;/span&gt;&lt;span&gt;O&lt;/span&gt;&lt;span&gt;ur &lt;/span&gt;&lt;span&gt;analysis &lt;/span&gt;&lt;span&gt;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.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&lt;span&gt;References:&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Wolanin A., Mateo-Garc&amp;#305;&amp;#769;a G., Camps-Valls G., G&amp;#243;mez-Chova L. ,Meroni, M., Duveiller, G., You, L., Guanter L. (2020) Estimating and Understanding Crop Yields with Explainable Deep Learning in the Indian Wheat Belt. Environmental Research Letters. &lt;/span&gt;&lt;/p&gt;