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Innovative Applications of Multimodal Deep Learning Fusion Technology in Intelligent Diagnosis and Green Control Decision-Making for Crop Pests and Diseases

Author(s)

Wang Zixuan

Affiliation(s)

College of International Education, Henan University, Zhengzhou, Henan, China

Abstract

This study addresses the limitations of current crop pest and disease diagnosis accuracy due to reliance on single-modal data and insufficient intelligence in green control decision-making. We propose a multimodal deep learning fusion framework to enhance diagnostic precision and the scientific validity of control decisions. The research integrates multisource modal data, including images, spectra, and text, and develops a multimodal feature extraction module through improved convolutional neural networks (CNNs), Transformers, and graph neural networks (GNNs). Three fusion strategies—early, late, and hybrid—are designed to achieve deep cross-modal feature integration, optimized by an attention mechanism for intermodal information exchange. An intelligent diagnosis system based on the fusion model is developed, incorporating an expert knowledge graph for green control decision-making. The experimental results from multiple regional datasets demonstrate that the proposed multimodal fusion model improves diagnostic accuracy by 12.3%-15.8% compared to single-modal methods, significantly enhancing the timeliness and adaptability of control decisions. The constructed intelligent system exhibits robust performance across different crop varieties and environmental conditions, providing an efficient and reliable technical pathway for precise diagnosis and green control in agriculture, with significant practical implications for smart agriculture development.

Keywords

Multimodal Deep Learning; Crop Pest and Disease Diagnosis; Feature Fusion; Green Control Decision-Making; Intelligent System

References

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