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A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases

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Bibliographic Details
Published in:International Journal of Engineering and Manufacturing
Format: Online Article RSS Article
Published: 2026
Subjects:
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container_title International Journal of Engineering and Manufacturing
description
discipline_display Technology & Engineering
discipline_facet Technology & Engineering
format Online Article
RSS Article
genre Journal Article
id rss_article:12667
institution FRELIP
journal_source_facet International Journal of Engineering and Manufacturing
publishDate 2026
publishDateSort 2026
record_format rss_article
spellingShingle A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases
Manufacturing and Technology
Technology & Engineering — Computing
Technology & Engineering
sub_discipline_display Technology & Engineering — Computing
sub_discipline_facet Technology & Engineering — Computing
subject_display Manufacturing and Technology
Technology & Engineering — Computing
Technology & Engineering
Manufacturing and Technology
Technology & Engineering — Computing
Technology & Engineering
subject_facet Manufacturing and Technology
Technology & Engineering — Computing
Technology & Engineering
title A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases
title_auth A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases
title_full A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases
title_fullStr A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases
title_full_unstemmed A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases
title_short A Mobile-Integrated Deep Learning Framework for Early Detection of Maize Diseases
title_sort a mobile-integrated deep learning framework for early detection of maize diseases
topic Manufacturing and Technology
Technology & Engineering — Computing
Technology & Engineering
url https://www.mecs-press.org/ijem/ijem-v16-n2/v16n2-12.html