Implementation of Large Language Models and Agricultural Knowledge Graphs for Efficient Plant Disease Detection

Xinyan Zhao, Baiyan Chen, Mengxue Ji, Xinyue Wang, Yuhan Yan, Jinming Zhang, Shiyingjie Liu, Muyang Ye and Chunli Lv ()
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Xinyan Zhao: China Agricultural University, Beijing 100083, China
Baiyan Chen: China Agricultural University, Beijing 100083, China
Mengxue Ji: China Agricultural University, Beijing 100083, China
Xinyue Wang: China Agricultural University, Beijing 100083, China
Yuhan Yan: China Agricultural University, Beijing 100083, China
Jinming Zhang: China Agricultural University, Beijing 100083, China
Shiyingjie Liu: China Agricultural University, Beijing 100083, China
Muyang Ye: China Agricultural University, Beijing 100083, China
Chunli Lv: China Agricultural University, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 8, 1-24

Abstract: This study addresses the challenges of elaeagnus angustifolia disease detection in smart agriculture by developing a detection system that integrates advanced deep learning technologies, including Large Language Models (LLMs), Agricultural Knowledge Graphs (KGs), Graph Neural Networks (GNNs), representation learning, and neural-symbolic reasoning techniques. The system significantly enhances the accuracy and efficiency of disease detection through an innovative graph attention mechanism and optimized loss functions. Experimental results demonstrate that this system significantly outperforms traditional methods across key metrics such as precision, recall, and accuracy, with the graph attention mechanism excelling in all aspects, particularly achieving a precision of 0.94, a recall of 0.92, and an accuracy of 0.93. Furthermore, comparative experiments with various loss functions further validate the effectiveness of the graph attention loss mechanism in enhancing model performance. This research not only advances the application of deep learning in agricultural disease detection theoretically but also provides robust technological tools for disease management and decision support in actual agricultural production, showcasing broad application prospects and profound practical value.

Keywords: plant disease detection; agricultural knowledge graphs; agricultural large model; deep learning; smart agriculture (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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