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Advancements in plant protection - the application of machine learning to the detection of maize infestations

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cris.virtual.author-orcid0000-0002-5461-9170
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dc.abstract.enPlant infestations cause significant economic losses in agriculture, necessitating rapid and accurate detection for optimized agrotechnical operations and reduced environmental pollution. This study addresses this challenge by proposing a customized convolutional neural network (CNN) architecture for detecting corn leaf worm infestations in maize. The research focuses on developing unique CNN models through extensive experimentation, systematically adjusting hyperparameters like optimizers, filter numbers, and kernel sizes. The study’s main contributions include the design of an accurate CNN classifier, and its implementation in a user-friendly smartphone application. The research highlights the importance of hyperparameter tuning in CNN performance, demonstrating that optimal configurations lead to high accuracy (up to 95% for accuracy, precision, recall, specificity, and F1-score). While the current model focuses on a single pest, the findings underscore the potential of custom CNN classifiers in vision systems for automated crop inspection, offering a promising solution for minimizing crop losses and the environmental impact of chemical plant protection products.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorKasprowicz, Michał
dc.contributor.authorPentoś, Katarzyna
dc.contributor.authorWojciechowski, Wiesław
dc.contributor.authorKujawa, Sebastian
dc.contributor.authorMbah, Jasper Tembeck
dc.date.access2026-01-29
dc.date.accessioned2026-01-29T11:41:57Z
dc.date.available2026-01-29T11:41:57Z
dc.date.copyright2025-12-31
dc.date.issued2025
dc.description.abstract<jats:p>Plant infestations cause significant economic losses in agriculture, necessitating rapid and accurate detection for optimized agrotechnical operations and reduced environmental pollution. This study addresses this challenge by proposing a customized convolutional neural network (CNN) architecture for detecting corn leaf worm infestations in maize. The research focuses on developing unique CNN models through extensive experimentation, systematically adjusting hyperparameters like optimizers, filter numbers, and kernel sizes. The study’s main contributions include the design of an accurate CNN classifier, and its implementation in a user-friendly smartphone application. The research highlights the importance of hyperparameter tuning in CNN performance, demonstrating that optimal configurations lead to high accuracy (up to 95% for accuracy, precision, recall, specificity, and F1-score). While the current model focuses on a single pest, the findings underscore the potential of custom CNN classifiers in vision systems for automated crop inspection, offering a promising solution for minimizing crop losses and the environmental impact of chemical plant protection products.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.number4
dc.description.points70
dc.description.versionfinal_published
dc.description.volume80
dc.identifier.doi10.24326/as.2025.5569
dc.identifier.eissn2544-798X
dc.identifier.issn2544-4476
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/7129
dc.identifier.weblinkhttps://czasopisma.up.lublin.pl/as/article/view/5569
dc.languageen
dc.language.otherpl
dc.pbn.affiliationmechanical engineering
dc.relation.ispartofAgronomy Science
dc.relation.pages39-55
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.encorn leaf worm
dc.subject.enconvolutional neural network
dc.subject.enplant protection
dc.subject.enimage recognition
dc.titleAdvancements in plant protection - the application of machine learning to the detection of maize infestations
dc.title.alternativeZastosowanie uczenia maszynowego w wykrywaniu szkodników kukurydzy
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue4
oaire.citation.volume80