基于深度残差的多特征多粒度农业病虫害识别研究

doi:10.14088/j.cnki.issn0439-8114.2020.16.035

湖北农业科学 ›› 2020, Vol. 59 ›› Issue (16): 153-157.doi: 10.14088/j.cnki.issn0439-8114.2020.16.035

基于深度残差的多特征多粒度农业病虫害识别研究

李艳红, 樊同科

西安外事学院,西安 710077

收稿日期:2019-12-12 出版日期:2020-08-25 发布日期:2020-10-09
作者简介:李艳红（1978-）,女,陕西武功人,讲师,硕士,研究方向为大数据、教育技术,（电话）18092318233（电子信箱）178653954@qq.com。
基金资助:
陕西省2019年重点研发计划项目（2019NY-055）; 陕西省教育科学十三五规划课题（SGH18H538）

Research on identification of agricultural insects based on depth residual network with multi-feature and multi-granularity

LI Yan-hong, FAN Tong-ke

Xi’an International University, Xi’an 710077, China

Received:2019-12-12 Online:2020-08-25 Published:2020-10-09

摘要/Abstract

摘要： 为了实现复杂农田背景下的病虫害识别,提出了一种基于深度残差学习的多特征多粒度农业病虫害识别方法。结果表明,与传统SVM和BP神经网络相比,该算法在复杂农田背景下的病虫害图像识别精度明显提高。在复杂农田背景下10种作物病虫害图像的分类问题上取得了98.67%的精度。该算法具有很高的实际应用价值,可以与当前使用的农业联网系统集成到实际的农业病虫害防治中。

关键词: 深度残差, 多特征多粒度, 农业病虫害, 识别

Abstract: In order to realize insect identification under complex farmland background, this paper proposed a multi-feature and multi-granularity insect identification method based on deep residual network. Compared with the traditional SVM and BP neural network, the accuracy of insect identification based on deep residual network was significantly improved in complex farmland background. Compared with the deep convolutional neural network such as AlexNet, the performance of our method was further improved after the depth residual learning optimization. And the accuracy of 98.67% was obtained on the classification of crop insect images under the background of 10 types of complex farmland. Therefore, this method has high practical application value and can be integrated into the actual agricultural insect control task with the currently used agricultural networking system.

Key words: depth residual, multi-feature multi-granularity, identification of agricultural insects, residual network

中图分类号:

TP391.4
S435

李艳红, 樊同科. 基于深度残差的多特征多粒度农业病虫害识别研究[J]. 湖北农业科学, 2020, 59(16): 153-157.

LI Yan-hong, FAN Tong-ke. Research on identification of agricultural insects based on depth residual network with multi-feature and multi-granularity[J]. HUBEI AGRICULTURAL SCIENCES, 2020, 59(16): 153-157.

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基于深度残差的多特征多粒度农业病虫害识别研究

Research on identification of agricultural insects based on depth residual network with multi-feature and multi-granularity

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