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AuthorKeceli, Ali Seydi
AuthorKaya, Aydin
AuthorCatal, Cagatay
AuthorTekinerdogan, Bedir
Available date2022-11-30T11:23:20Z
Publication Date2022
Publication NameEcological Informatics
ResourceScopus
Resource2-s2.0-85130545264
URIhttp://dx.doi.org/10.1016/j.ecoinf.2022.101679
URIhttp://hdl.handle.net/10576/36793
AbstractThe manual prediction of plant species and plant diseases is expensive, time-consuming, and requires expertise that is not always available. Automated approaches, including machine learning and deep learning, are increasingly being applied to surmount these challenges. For this, accurate models are needed to provide reliable predictions and guide the decision-making process. So far, these two problems have been addressed separately, and likewise, separate models have been developed for each of these two problems, but considering that plant species and plant disease prediction are often related tasks, they can be considered together. We therefore propose and validate a novel approach based on the multi-task learning strategy, using shared representations between these related tasks, because they perform better than individual models. We apply a multi-input network that uses raw images and transferred deep features extracted from a pre-trained deep model to predict each plant's type and disease. We develop an end-to-end multi-task model that carries out more than one learning task at a time and combines the Convolutional Neural Network (CNN) features and transferred features. We then evaluate this model using public datasets. The results of our experiments demonstrated that this Multi-Input Multi-Task Neural Network model increases efficiency and yields faster learning for similar detection tasks. 2022 Elsevier B.V.
Languageen
PublisherElsevier
SubjectConvolutional neural networks; Deep neural networks; Multi-task learning; Plant classification; Transfer learning
TitleDeep learning-based multi-task prediction system for plant disease and species detection
TypeArticle
Volume Number69
dc.accessType Abstract Only


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