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المؤلفKeceli, Ali Seydi
المؤلفKaya, Aydin
المؤلفCatal, Cagatay
المؤلفTekinerdogan, Bedir
تاريخ الإتاحة2022-11-30T11:23:20Z
تاريخ النشر2022
اسم المنشورEcological Informatics
المصدرScopus
المصدر2-s2.0-85130545264
معرّف المصادر الموحدhttp://dx.doi.org/10.1016/j.ecoinf.2022.101679
معرّف المصادر الموحدhttp://hdl.handle.net/10576/36793
الملخصThe 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.
اللغةen
الناشرElsevier
الموضوعConvolutional neural networks; Deep neural networks; Multi-task learning; Plant classification; Transfer learning
العنوانDeep learning-based multi-task prediction system for plant disease and species detection
النوعArticle
رقم المجلد69
dc.accessType Abstract Only


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