CropAIQ: AI Framework for Accurate Crop Yield Prediction

CropAIQ is a software framework developed to provide data preprocessing for spatiotemporal agricultural yield data and remote sensing data for modeling using artificial neural networks (ANNs) to predict subfield crop yield estimates. It allows the training, validation, and testing of ANN models and the inference of new remote-sensing data. The software aims to help agricultural and bioenergy industry stakeholders needing crop yield prediction capabilities, such as precision agriculture applications or integrating alternative crops at a field/subfield level. It uses state-of-the-art machine learning and advanced remote sensing methods that have shown high accuracy during model development and testing and are built on open-source software and publicly available remote sensing data to minimize implementation costs. The solution results are accurate subfield estimates at potential landscape levels which greatly support agronomic and land management decision-makers and can have significant economic and environmental impacts.

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