Unverified paper record
Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning.
Scientific reports · 22 Oct 2025 · 10.1038/s41598-025-20846-5
Abstract
This study presents a novel framework for adaptive optimization of electromagnetic vibration parameters in corn seed treatment using multi-objective deep learning approaches. A hybrid CNN-LSTM network architecture was developed to process heterogeneous sensor data and predict multiple seed phenotype characteristics simultaneously. The framework integrates genetic algorithms with particle swarm optimization for real-time parameter adjustment, addressing the complex relationships between electromagnetic treatment conditions and seed quality outcomes. Experimental validation using three corn varieties (Zhengdan 958, Xianyu 335, and Jingke 968) demonstrates significant performance improvements, with optimized treatment protocols achieving 12.8% enhancement in germination rates and 17.7% improvement in vigor indices compared to untreated controls. The multi-objective deep learning model achieved 93.7% prediction accuracy with 91.2% recall rate, outperforming conventional optimization approaches. The adaptive parameter optimization strategy successfully balanced competing objectives including treatment effectiveness, energy efficiency, and processing time while maintaining robust performance across different seed batches. This research provides a comprehensive solution for intelligent seed treatment systems, offering substantial potential for advancing precision agriculture and sustainable crop production technologies.
Plant phenotyping relevance
深層学習モデルによる種子形質の予測と技術検証が研究の中心であり、単なる処理効果の測定にとどまらない。
abstractA hybrid CNN-LSTM network architecture was developed to process heterogeneous sensor data and predict multiple seed phenotype characteristics simultaneously.
abstractThe multi-objective deep learning model achieved 93.7% prediction accuracy with 91.2% recall rate, outperforming conventional optimization approaches.
Code and data availability
The paper's corn seed phenotype datasets (electromagnetic sensor measurements, phenotype assessments, imaging data, and deep learning model parameters) are held in an institutional repository and available only upon reasonable request from the corresponding author; no public deposit URL or author analysis code is given
No evidence-backed public reproduction asset is currently recorded.
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