Deep learning models for the early detection of maize streak virus and maize lethal necrosis diseases in Tanzania.

IF 3 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Frontiers in Artificial Intelligence Pub Date : 2024-08-16 eCollection Date: 2024-01-01 DOI:10.3389/frai.2024.1384709
Flavia Mayo, Ciira Maina, Mvurya Mgala, Neema Mduma
{"title":"Deep learning models for the early detection of maize streak virus and maize lethal necrosis diseases in Tanzania.","authors":"Flavia Mayo, Ciira Maina, Mvurya Mgala, Neema Mduma","doi":"10.3389/frai.2024.1384709","DOIUrl":null,"url":null,"abstract":"<p><p>Agriculture is considered the backbone of Tanzania's economy, with more than 60% of the residents depending on it for survival. Maize is the country's dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":"7 ","pages":"1384709"},"PeriodicalIF":3.0000,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11362060/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3389/frai.2024.1384709","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Agriculture is considered the backbone of Tanzania's economy, with more than 60% of the residents depending on it for survival. Maize is the country's dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
用于早期检测坦桑尼亚玉米条纹病毒和玉米致死坏死病的深度学习模型。
农业被认为是坦桑尼亚的经济支柱,60% 以上的居民依靠农业生存。玉米是该国最主要的粮食作物,占农田总产量的 45%。然而,由于无法及早发现玉米病害,玉米的产量受到了挑战。玉米条斑病毒(MSV)和玉米致死坏死病毒(MLN)是常见的病害,农民往往发现得太晚。因此,需要开发一种早期检测这些疾病的方法,以便及时治疗。本研究调查了开发深度学习模型用于早期检测坦桑尼亚玉米疾病的潜力。收集数据的地区包括阿鲁沙、乞力马扎罗和马尼亚拉。数据是通过植物观察收集的。研究提出了卷积神经网络(CNN)和视觉转换器(ViT)模型。四类图像数据被用于训练这两种模型:MLN、Healthy、MSV 和 WRONG。结果显示,ViT 模型超过了 CNN 模型,准确率分别为 93.1% 和 90.96%。进一步的研究应侧重于移动应用程序的开发和模型的部署,以便在现实生活中更精确地早期检测上述疾病。
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 去求助
来源期刊
CiteScore
6.10
自引率
2.50%
发文量
272
审稿时长
13 weeks
期刊最新文献
Advancing smart city factories: enhancing industrial mechanical operations via deep learning techniques. Inpainting of damaged temple murals using edge- and line-guided diffusion patch GAN. Catalyzing IVF outcome prediction: exploring advanced machine learning paradigms for enhanced success rate prognostication. Predicting patient reported outcome measures: a scoping review for the artificial intelligence-guided patient preference predictor. A generative AI-driven interactive listening assessment task.
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1