Ding Junqi, Qiao Yan, Zhang Lingxian
China Agricultural University, Beijing, 100083, China.
Beijing Plant Protection Station, Beijing, 100029, China.
Plant Methods. 2023 Aug 26;19(1):91. doi: 10.1186/s13007-023-01070-6.
In the era of Agri 4.0 and the popularity of Plantwise systems, the availability of Plant Electronic Medical Records has provided opportunities to extract valuable disease information and treatment knowledge. However, developing an effective prescription recommendation method based on these records presents unique challenges, such as inadequate labeling data, lack of structural and linguistic specifications, incorporation of new prescriptions, and consideration of multiple factors in practical situations.
This study proposes a plant disease prescription recommendation method called PRSER, which is based on sentence embedding retrieval. The semantic matching model is created using a pre-trained language model and a sentence embedding method with contrast learning ideas, and the constructed prescription reference database is retrieved for optimal prescription recommendations. A multi-vegetable disease dataset and a multi-fruit disease dataset are constructed to compare three pre-trained language models, four pooling types, and two loss functions. The PRSER model achieves the best semantic matching performance by combining MacBERT, CoSENT, and CLS pooling, resulting in a Pearson coefficient of 86.34% and a Spearman coefficient of 77.67%. The prescription recommendation capability of the model is also verified. PRSER performs well in closed-set testing with Top-1/Top-3/Top-5 accuracy of 88.20%/96.07%/97.70%; and slightly worse in open-set testing with Top-1/Top-3/Top-5 accuracy of 82.04%/91.50%/94.90%. Finally, a plant disease prescription recommendation system for mobile terminals is constructed and its generalization ability with incomplete inputs is verified. When only symptom information is available without environment and plant information, our model shows slightly lower accuracy with Top-1/Top-3/Top-5 accuracy of 75.24%/88.35%/91.99% in closed-set testing and Top-1/Top-3/Top-5 accuracy of 75.08%/87.54%/89.84% in open-set testing.
The experiments validate the effectiveness and generalization ability of the proposed approach for recommending plant disease prescriptions. This research has significant potential to facilitate the implementation of artificial intelligence in plant disease treatment, addressing the needs of farmers and advancing scientific plant disease management.
在农业4.0时代以及植物健康管理系统普及的背景下，植物电子病历的出现为提取有价值的病害信息和治疗知识提供了契机。然而，基于这些记录开发一种有效的处方推荐方法面临着独特的挑战，例如标注数据不足、缺乏结构和语言规范、纳入新处方以及在实际情况中考虑多种因素。
本研究提出了一种名为PRSER的植物病害处方推荐方法，该方法基于句子嵌入检索。使用预训练语言模型和带有对比学习思想的句子嵌入方法创建语义匹配模型，并检索构建的处方参考数据库以获得最佳处方推荐。构建了一个多蔬菜病害数据集和一个多水果病害数据集，以比较三种预训练语言模型、四种池化类型和两种损失函数。PRSER模型通过结合MacBERT、CoSENT和CLS池化实现了最佳的语义匹配性能，皮尔逊系数为86.34%，斯皮尔曼系数为77.67%。还验证了该模型的处方推荐能力。PRSER在闭集测试中表现良好，Top-1/Top-3/Top-5准确率分别为88.20%/96.07%/97.70%；在开集测试中略差，Top-1/Top-3/Top-5准确率分别为82.04%/91.50%/94.90%。最后，构建了一个用于移动终端的植物病害处方推荐系统，并验证了其对不完整输入的泛化能力。当仅提供症状信息而没有环境和植物信息时，我们的模型在闭集测试中的Top-1/Top-3/Top-5准确率分别为75.24%/88.35%/91.99%，在开集测试中的Top-1/Top-3/Top-5准确率分别为75.08%/87.54%/89.84%，准确率略低。
实验验证了所提出的植物病害处方推荐方法的有效性和泛化能力。本研究在促进人工智能在植物病害治疗中的应用、满足农民需求以及推进科学的植物病害管理方面具有巨大潜力。
