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基于BERT-BiGRU-CapsNet的作物疾病-害虫问题的分类,并集中关注

Ting Zhang¹, Dengwu Wang¹

¹College of Computing, Xijing University, Xi'an, China.

Frontiers in plant science

|December 25, 2023

概括

一个新的BERT-BiGRU-CapsNet与注意力聚合 (BBGCAP) 模型显著改善了作物疾病-害虫问题的分类. 这种先进的方法达到90%以上的准确性,增强了智能害虫知识系统.

科学领域:

农业科学农业科学
计算机科学计算机科学
人工智能的人工智能

背景情况:

准确的作物病害虫问题分类对于智能农业知识系统至关重要.
现有的方法经常与非结构化害虫相关查询的复杂性和细微差别作斗争.

研究的目的:

开发和评估一种新的,高性能的作物病害虫问题分类方法.
整合多个深度学习架构以提高分类准确性和稳定性.

主要方法:

提出了一个混合模型:BERT-BiGRU-CapsNet与注意力聚合 (BBGCAP).
使用BERT进行文本向量化,BiGRU用于深度特征提取,注意力聚合用于权重,CapsNet用于分类.
在黄瓜害虫问题数据库上测试了BBGCAP模型.

主要成果:

与传统的模板匹配,SVM和LSTM方法相比,BBGCAP模型显示出更高的性能.
在黄瓜害虫问题数据集上获得了超过90%的精度,回忆和F1分数.
综合方法有效地利用每个组件模型的优势.

结论:

BBGCAP模型为作物病害虫问题分类提供了强大而准确的解决方案.
这种方法为开发农业中先进的智能问答系统提供了必要的技术支持.
这些发现突显了混合深度学习模型在专门农业信息学中的潜力.

关键词:

伯特-BiGRU-CapsNet与注意力聚合 (BBGCAP) 在一起聚合注意力聚合注意力双向封闭单元 (BiGRU) 是一个双向封闭单元.囊网络 (CapsNet) 是一个农作物疾病-害虫问题

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