LSANNet: A lightweight convolutional neural network for maize leaf disease identification

IF 4.4 1区 农林科学 Q1 AGRICULTURAL ENGINEERING
Fu Zhang , Ruofei Bao , Baoping Yan , Mengyao Wang , Yakun Zhang , Sanling Fu
{"title":"LSANNet: A lightweight convolutional neural network for maize leaf disease identification","authors":"Fu Zhang ,&nbsp;Ruofei Bao ,&nbsp;Baoping Yan ,&nbsp;Mengyao Wang ,&nbsp;Yakun Zhang ,&nbsp;Sanling Fu","doi":"10.1016/j.biosystemseng.2024.09.023","DOIUrl":null,"url":null,"abstract":"<div><div>Maize (<em>Zea Mays</em>) is a major food crop and is of great importance to ensure national food security. However, maize leaf diseases occur from time to time, which poses a serious threat to grain yield and quality, so methods for the quick identification of maize leaf diseases are particularly important. In this paper, a long-short attention neural network (LSANNet) is proposed for maize leaf diseases identification. The main component of the LSANNet is the long-short attention block (LSAB). The long-short connection method enables the fusion of multi-scale features, which enhances the model generalisation capability. The attention mechanism is applied in the block, which aims to enhance the extraction of maize leaf features. The effectiveness of separable convolution and attention modules is demonstrated by ablation studies. Experimental results on 124 unseen images show that the accuracy of the proposed model on the test sets reaches 94.35%, which is better than the accuracy of existing models, such as VGG16, ResNet50, DenseNet201, MobileNetV3S, and Xception. The practical performance of the proposed network model is verified by deploying the model on a mobile device, demonstrating strong compatibility and high recognition. In this paper, a lightweight convolutional neural work is proposed for maize leaf disease identification, and the performance of the network on the test sets meets the required requirements. This research will provide an idea for the identification of maize leaf diseases and disease prevention schemes for agricultural production.</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"248 ","pages":"Pages 97-107"},"PeriodicalIF":4.4000,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1537511024002253","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0

Abstract

Maize (Zea Mays) is a major food crop and is of great importance to ensure national food security. However, maize leaf diseases occur from time to time, which poses a serious threat to grain yield and quality, so methods for the quick identification of maize leaf diseases are particularly important. In this paper, a long-short attention neural network (LSANNet) is proposed for maize leaf diseases identification. The main component of the LSANNet is the long-short attention block (LSAB). The long-short connection method enables the fusion of multi-scale features, which enhances the model generalisation capability. The attention mechanism is applied in the block, which aims to enhance the extraction of maize leaf features. The effectiveness of separable convolution and attention modules is demonstrated by ablation studies. Experimental results on 124 unseen images show that the accuracy of the proposed model on the test sets reaches 94.35%, which is better than the accuracy of existing models, such as VGG16, ResNet50, DenseNet201, MobileNetV3S, and Xception. The practical performance of the proposed network model is verified by deploying the model on a mobile device, demonstrating strong compatibility and high recognition. In this paper, a lightweight convolutional neural work is proposed for maize leaf disease identification, and the performance of the network on the test sets meets the required requirements. This research will provide an idea for the identification of maize leaf diseases and disease prevention schemes for agricultural production.
LSANNet:用于识别玉米叶病的轻量级卷积神经网络
玉米(Zea Mays)是一种主要的粮食作物,对确保国家粮食安全至关重要。然而,玉米叶部病害时有发生,对粮食产量和质量构成严重威胁,因此快速识别玉米叶部病害的方法尤为重要。本文提出了一种用于玉米叶病识别的长短注意力神经网络(LSANNet)。LSANNet 的主要组成部分是长短注意力块(LSAB)。长短连接法实现了多尺度特征的融合,从而增强了模型的泛化能力。注意机制应用于该区块,旨在加强对玉米叶片特征的提取。通过消融研究证明了可分离卷积和注意力模块的有效性。在 124 幅未见图像上的实验结果表明,所提模型在测试集上的准确率达到 94.35%,优于现有模型,如 VGG16、ResNet50、DenseNet201、MobileNetV3S 和 Xception。通过在移动设备上部署模型,验证了所提网络模型的实用性能,证明了该模型具有很强的兼容性和很高的识别率。本文提出了一种用于玉米叶病识别的轻量级卷积神经工作,该网络在测试集上的性能达到了要求。这项研究将为玉米叶病识别和农业生产防病方案提供思路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Biosystems Engineering
Biosystems Engineering 农林科学-农业工程
CiteScore
10.60
自引率
7.80%
发文量
239
审稿时长
53 days
期刊介绍: Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信