Using AI for sustainable agriculture and forecasting

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Using AI for sustainable agriculture and forecasting

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  • Applying nutrients to agricultural systems is critical to maximizing crop yields while minimizing the negative environmental impacts of fertilizers, such as nitrous oxide emissions or groundwater pollution. These goals can be achieved by timely calculating crop nutrient demand, which allows for precise fertilization management. In this talk, I will discuss non-invasive AI techniques to detect nutrient deficiencies from RGB images captured by smartphones or UAVs. Since it is very difficult to acquire training data of crops with nutrient deficiencies in the field, I will discuss how generative adversarial networks (GANs) can be trained in low data regimes where only one or very few training images are available. I will show that the  generated images can be used as a source for data augmentation to improve the segmentation accuracy of rare classes, one-shot semantic image segmentation, or one-shot video object segmentation. In the second part of the talk, I will discuss AI approaches for forecasting wildfires and vegetation.                            


    This live event includes a 15-minute networking event hosted on the AI for Good Neural Network. This is your opportunity to ask questions, interact with the panelists and participants and build connections with the AI for Good community.

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