Detection of maize leaf diseases using improved MobileNet V3-small

IF 2.2 2区 农林科学 Q2 AGRICULTURAL ENGINEERING International Journal of Agricultural and Biological Engineering Pub Date : 2023-01-01 DOI:10.25165/j.ijabe.20231603.7799
Ang Gao, Aijun Geng, Yuepeng Song, Longlong Ren, Yue Zhang, Xiang Han
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Abstract

In order to realize the intelligent identification of maize leaf diseases for accurate prevention and control, this study proposed a maize disease detection method based on improved MobileNet V3-small, using a UAV to collect maize disease images and establish a maize disease dataset in a complex context, and explored the effects of data expansion and migration learning on model recognition accuracy, recall rate, and F1-score instructive evaluative indexes, and the results show that the two approaches of data expansion and migration learning effectively improved the accuracy of the model. The structured compression of MobileNet V3-small bneck layer retains only 6 layers, the expansion multiplier of each layer was redesigned, 32-fold fast downsampling was used in the first layer, and the location of the SE module was optimized. The improved model had an average accuracy of 79.52% in the test set, a recall of 77.91%, an F1-score of 78.62%, a model size of 2.36 MB, and a single image detection speed of 9.02 ms. The detection accuracy and speed of the model can meet the requirements of mobile or embedded devices. This study provides technical support for realizing the intelligent detection of maize leaf diseases. Keywords: maize leaf disease, image recognition, model compression, MobileNetV3-small DOI: 10.25165/j.ijabe.20231603.7799 Citation: Gao A, Geng A J, Song Y P, Ren L L, Zhang Y, Han X. Detection of maize leaf diseases using improved MobileNet V3-small. Int J Agric & Biol Eng, 2023; 16(3): 225–232.
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改良MobileNet V3-small对玉米叶片病害的检测
为了实现对玉米叶片病害的智能识别,实现精准防控,本研究提出了一种基于改进的MobileNet V3-small的玉米病害检测方法,利用无人机采集玉米病害图像,建立复杂环境下的玉米病害数据集,并探讨了数据扩展和迁移学习对模型识别准确率、召回率和f1分值指导性评价指标的影响。结果表明,数据扩展和迁移学习两种方法有效地提高了模型的精度。MobileNet V3-small bneck层的结构化压缩只保留了6层,重新设计了每层的扩展倍率,第一层采用32倍快速下采样,并优化了SE模块的位置。改进后的模型在测试集中的平均准确率为79.52%,召回率为77.91%,f1得分为78.62%,模型大小为2.36 MB,单幅图像检测速度为9.02 ms。该模型的检测精度和速度可以满足移动或嵌入式设备的要求。本研究为实现玉米叶片病害智能检测提供了技术支持。关键词:玉米叶片病害,图像识别,模型压缩,MobileNetV3-small DOI: 10.25165/ j.j ijabe.20231603.7799引用本文:高安,耿爱军,宋永平,任丽丽,张勇,韩鑫。基于改进MobileNetV3-small的玉米叶片病害检测农业与生物工程学报,2023;16(3): 225 - 232。
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来源期刊
CiteScore
4.30
自引率
12.50%
发文量
88
审稿时长
24 weeks
期刊介绍: International Journal of Agricultural and Biological Engineering (IJABE, https://www.ijabe.org) is a peer reviewed open access international journal. IJABE, started in 2008, is a joint publication co-sponsored by US-based Association of Agricultural, Biological and Food Engineers (AOCABFE) and China-based Chinese Society of Agricultural Engineering (CSAE). The ISSN 1934-6344 and eISSN 1934-6352 numbers for both print and online IJABE have been registered in US. Now, Int. J. Agric. & Biol. Eng (IJABE) is published in both online and print version by Chinese Academy of Agricultural Engineering.
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