Ensemble Transfer Learning Using Maizeset: a Dataset for Weed and Maize Crop Recognition at Different Growth Stages

dc.authorid Khan, Muhammad/0000-0002-9195-3477
dc.authorscopusid 59224840400
dc.authorscopusid 57189388538
dc.authorscopusid 57209876827
dc.authorwosid Khan, Muhammad/N-5478-2016
dc.contributor.author Das, Zeynep Dilan
dc.contributor.author Alam, Muhammad Shahab
dc.contributor.author Khan, Muhammad Umer
dc.contributor.other Mechatronics Engineering
dc.date.accessioned 2024-09-10T21:33:40Z
dc.date.available 2024-09-10T21:33:40Z
dc.date.issued 2024
dc.department Atılım University en_US
dc.department-temp [Das, Zeynep Dilan; Khan, Muhammad Umer] Atilim Univ, Dept Mechatron Engn, TR-06830 Ankara, Turkiye; [Alam, Muhammad Shahab] Gebze Tech Univ, Def Technol Inst, TR-41400 Kocaeli, Turkiye en_US
dc.description Khan, Muhammad/0000-0002-9195-3477 en_US
dc.description.abstract Maize holds significant importance as a staple food source globally. Increasing maize yield requires the effective removal of weeds from maize fields, as they pose a detrimental threat to the growth of maize plants. In recent years, there has been a drive towards Precision Agriculture (PA), involving the integration of farming methods with artificial intelligence and advanced automation techniques. In the realm of PA, deep learning techniques present a promising solution for addressing the complex challenge of classifying maize plants and weeds. In this work, a deep learning method based on transfer learning and ensemble techniques is developed. The proposed method is implementable on any number of existing CNN models irrespective of their architecture and complexity. The developed ensemble model is trained and tested on our custom-built dataset, namely MaizeSet, comprising 3330 images of maize plants and weeds under varying environmental conditions. The performance of the ensemble model is compared against individual pre-trained VGG16 and InceptionV3 models using two experiments: the identification of weeds and maize plants, and the identification of the various vegetative growth stages of maize plants. VGG16 attained an accuracy of 83% in Experiment 1 and 71% in Experiment 2, while InceptionV3 showcased improved performance, boasting an accuracy of 98% in Experiment 1 and 81% in Experiment 2. With the proposed ensemble approach, VGG16 when combined with InceptionV3, achieved an accuracy of 90% for Experiment 1 and 80% for Experiment 2. The findings demonstrate that integrating a suboptimal pre-defined classifier, specifically VGG16, with a more proficient model like InceptionV3, yields enhanced performance across various analytical metrics. This underscores the efficacy of ensemble techniques in the context of maize classification and analogous applications within the agricultural domain. en_US
dc.description.sponsorship Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.citationcount 0
dc.identifier.doi 10.1016/j.cropro.2024.106849
dc.identifier.issn 0261-2194
dc.identifier.issn 1873-6904
dc.identifier.scopus 2-s2.0-85198984154
dc.identifier.uri https://doi.org/10.1016/j.cropro.2024.106849
dc.identifier.uri https://hdl.handle.net/20.500.14411/7301
dc.identifier.volume 184 en_US
dc.identifier.wos WOS:001275513800001
dc.identifier.wosquality Q2
dc.institutionauthor Khan, Muhammad Umer
dc.language.iso en en_US
dc.publisher Elsevier Sci Ltd en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 1
dc.subject Crop and weed identification en_US
dc.subject cnn en_US
dc.subject Deep learning en_US
dc.subject Ensemble learning en_US
dc.subject Precision agriculture en_US
dc.subject Transfer learning en_US
dc.title Ensemble Transfer Learning Using Maizeset: a Dataset for Weed and Maize Crop Recognition at Different Growth Stages en_US
dc.type Article en_US
dc.wos.citedbyCount 2
dspace.entity.type Publication
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