Integrating multi-modal remote sensing, deep learning, and attention mechanisms for yield prediction in plant breeding experiments.

ABSTRACT: In both plant breeding and crop management, interpretability plays a crucial role in instilling trust in AI-driven approaches and enabling the provision of actionable insights. The primary objective of this research is to explore and evaluate the potential contributions of deep learning network architectures that employ stacked LSTM for end-of-season maize grain yield prediction. A secondary aim is to expand the capabilities of these networks by adapting them to better accommodate and leverage the multi-modality properties of remote sensing data. In this study, a multi-modal deep learning architecture that assimilates inputs from heterogeneous data streams, including high-resolution hyperspectral imagery, LiDAR point clouds, and environmental data, is proposed to forecast maize crop yields

SUBMITTER: Aviles Toledo C 

PROVIDER: S-EPMC11306015 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature