Analysis of transfer learning for deep neural network based plant classification models

By Aydın Kaya, A. Keçeli, Cagatay Catal, Hamdi Yalin Yalic, Hüseyin Temuçin, Bedir Tekinerdogan. Computers and Electronics in Agriculture, 2019

392 citations recorded by the source corpus.

Plant species classification is crucial for biodiversity protection and conservation. Manual classification is time-consuming, expensive, and requires experienced experts who are often limited available. To cope with these issues, various machine learning algorithms have been proposed to support the automated classification of plant species. Among these machine learning algorithms, Deep Neural Networks (DNNs) have been applied to different data sets. DNNs have been however often applied in isolation and no effort has been made to reuse and transfer the knowledge of different applications of DNNs. Transfer learning in the context of machine learning implies the usage of the results of multiple applications of DNNs. In this article, the results of the effect of four different transfer learning models for deep neural network-based plant classification is investigated on four public datasets. Our experimental study demonstrates that transfer learning can provide important benefits for automated plant identification and can improve low-performance plant classification models.

Subfields: Analytical Chemistry, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/1399fe83b724996b8aeeba0e213cb1895c539951

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Citations in the corpus

Deep Learning for Classification and Severity Estimation of Coffee Leaf Biotic Stress (2019, methodology): “Several studies were carried out using the PlantVillage dataset (Mohanty et al., 2016; Too et al., 2019; Kaya et al., 2019), most of these studies presented results greater than 99% of accuracy.”
Crop Yield Prediction Using Deep Reinforcement Learning Model for Sustainable Agrarian Applications (2020, background): “On observing the most prominent models in agriculture, we can see the utilization of artiﬁcial and deep neural networks [14].”
Plant Diseases recognition on images using Convolutional Neural Networks: A Systematic Review (2020, neutral): “It should be noted that 19 studies (S1, S6, S7, S11, S12, S13, S22, S35, S43, S61, S68, S70, S75, S84, S86, S91, S100, S116, S120) propose new architectures and 15 (S9, S18, S19, S23, S32, S34, S39, S41, S42, S49, S50, S51, S53, S65, S103) investigations customize classic algorithms to solve the pro”