Structured Multilayer Framework for Predicting Crop Yield and Indicator Compounds Using Sparse and Semantic Time-Series Climate Data With HPC Scalabil...
TL;DR
This article introduces a structured multilayer neural network model for predicting crop yield and indicator compounds by integrating semantic climate data grouping, interaction learning, and time-series analysis with HPC scalability.
Structured Multilayer Framework for Predicting Crop Yield and Indicator Compounds Using Sparse and Semantic Time-Series Climate Data With HPC Scalability
Hyunjo Lee; Kimoon Jeong; Hyun Mi Jung; Cheol-Joo Chae
https://doi.org/10.1109/ACCESS.2025.3646142
Volume 14
The accurate prediction of crop yield and key indicator compounds has become crucial in high-value agriculture under complex environmental conditions. This article proposes a structured multilayer neural network model that integrates the semantic grouping of climate variables, intragroup and intergroup interaction learning, and time-series cumulative response analysis. Climate and environmental va...