Estimation of Crop Production by Fusing Images and Crop Features

Ángela Casado-García, Jónathan Heras, Jon Miranda-Apodaca, Xabier Simon Martínez-Goñi, Usue Pérez-López; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2023, pp. 525-530

The increasing global population and the growing frequency of droughts shows the necessity to enhance global food production and meet future food demands. However, achieving long-term food security and effectively mitigating the impact of climate change require a critical emphasis on sustainable systems to increase food production. Hence, automatic estimation of crop production can enable breeders and farmers to make data-driven decisions to optimise resources and maximise efficiency and sustainability. In this work, we have tackled this estimation task by applying deep learning methods to images taken from a digital RGB camera. Moreover, we have improved the results of those models by feeding the models with not only images but also crop features, such as the amount of fertilisers or the amount of water. The proposed data fusion approach can be applied to convolutional-and transformer-based models obtaining good results in both cases. As a result of our work, we have produced a model that estimates crop production of wheat and spelt with an MAE of 0.666, and is a first step towards optimising resources and food production.