Neural Architecture Search for Crop Yield Prediction using Multimodal Data

Accurate crop yield prediction is essential for sustainable agriculture and food security. Traditional methods often fall short in addressing the complex factors influencing crop growth. This paper explores the use of neural architecture search (NAS) combined with multimodal data integration to improve prediction accuracy. By incorporating diverse data sources such as unmanned aerial vehicle (UAV) imagery, and weather data, the research develops a framework for crop yield prediction. NAS techniques systematically identify optimal neural network architectures to handle these varied datasets effectively. The approach is validated with real-world agricultural data, showing that NAS-optimised models significantly outperform traditional methods. This work enhances precision agriculture, enabling better resource allocation and sustainable farming practices.