Wang Kun, Miao Yuyuan, Wang Xu, Li Yuze, Li Fuzhong, Song Haiyan
Software College, Shanxi Agricultural University, Jinzhong, Shanxi, China.
Agricultural Engineering College, Shanxi Agricultural University, Jinzhong, Shanxi, China.
Front Plant Sci. 2024 Nov 7;15:1482275. doi: 10.3389/fpls.2024.1482275. eCollection 2024.
Tomato leaf pests and diseases pose a significant threat to the yield and quality of Q6 tomatoes, highlighting the necessity for comprehensive studies on effective control methods.
Current control measures predominantly rely on experience and manual observation, hindering the integration of multi-source data. To address this, we integrated information resources related to tomato leaf pests and diseases from agricultural standards documents, knowledge websites, and relevant literature. Guided by domain experts, we preprocessed this data to construct a sample set.
We utilized the Named Entity Recognition (NER) model ALBERT-BiLSTM-CRF to conduct end-to-end knowledge extraction experiments, which outperformed traditional models such as 1DCNN-CRF and BiLSTM-CRF, achieving a recall rate of 95.03%. The extracted knowledge was then stored in the Neo4j graph database, effectively visualizing the internal structure of the knowledge graph.
We developed a digital diagnostic system for tomato leaf pests and diseases based on the knowledge graph, enabling graphical management and visualization of pest and disease knowledge. The constructed knowledge graph offers insights for controlling tomato leaf pests and diseases and provides new research directions for pest control in other crops.
番茄叶病虫害对Q6番茄的产量和品质构成重大威胁，凸显了对有效防治方法进行全面研究的必要性。
当前的防治措施主要依赖经验和人工观察，阻碍了多源数据的整合。为解决这一问题，我们整合了来自农业标准文件、知识网站和相关文献的与番茄叶病虫害相关的信息资源。在领域专家的指导下，我们对这些数据进行预处理以构建样本集。
我们利用命名实体识别（NER）模型ALBERT-BiLSTM-CRF进行端到端知识提取实验，其性能优于1DCNN-CRF和BiLSTM-CRF等传统模型，召回率达到95.03%。提取的知识随后存储在Neo4j图数据库中，有效地可视化了知识图谱的内部结构。
我们基于知识图谱开发了番茄叶病虫害数字诊断系统，实现了病虫害知识的图形化管理和可视化。构建的知识图谱为番茄叶病虫害防治提供了见解，并为其他作物的病虫害防治提供了新的研究方向。
