Multi-objective optimization of electromagnetic vibration parameters for corn seed phenotype prediction based on deep learning

TL;DR

This paper introduces a deep learning framework that optimizes electromagnetic vibration parameters to improve corn seed quality, using a hybrid CNN-LSTM model and multi-objective optimization techniques.

Contribution

The novel contribution is a hybrid CNN-LSTM architecture combined with adaptive multi-objective optimization for real-time corn seed treatment parameter adjustment.

Findings

Optimized treatment protocols improved germination rates by 12.8% and vigor indices by 17.7%.

The model achieved 93.7% prediction accuracy and 91.2% recall rate, outperforming conventional methods.

The framework balanced treatment effectiveness, energy efficiency, and processing time across seed batches.

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.

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