| Sumario: | Crop productivity is poorly assessed globally. Here, we provide a deep learning-based approach for
estimating rice yield using RGB images. During ripening stage and at harvest, over 22,000 digital images
were captured vertically downwards over the rice canopy from a distance of 0.8 to 0.9 m, and rice yields
were obtained in the corresponding area ranging from 0.1 and 16.1 t ha
−1
. A convolutional neural network
(CNN) applied to these data at harvest predicted 70% variation in rice yield with a relative root mean
square error (rRMSE) of 0.22. Images obtained during the ripening stage can also be used to forecast the
final rice yield. Our work suggests that this low-cost, hands-on, and rapid approach can provide a
breakthrough solution to assess the impact of productivity-enhancing interventions and identify fields
where these are needed to sustainably increase crop production
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