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Journals / Sakarya University Journal of Computer and Information Sciences / 2020 / Cilt: 3 Sayı: 3

Identification of Plant Species by Deep Learning and Providing as A Mobile Application

Muhammed Fatih ADAK
Pages
231–238
Publication Date
2020-12-30
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Up to 90% accuracy is reached in this study with the current version of the application.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Image processing techniques give highly successful results when used deep learning in classification studies.
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Key Findings & Data:Currently, the application is capable to classify 43 different plants in mobile environment, and its classification capacity is planned to be expanded with new plant species as a future study.
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Conclusion & Impact:Up to 90% accuracy is reached in this study with the current version of the application.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:plant classification · deep learning · CNN

Abstract

Image processing techniques give highly successful results when used deep learning in classification studies. Applications benefit from this kind of work to make life easier. In this study, a mobile application is developed that takes photo of a plant and makes image processing on it to provide information about its name, the time to change the soil, the amount of sun light and nutrition it needs. The model is trained using the Convolutional Neural Networks, and dataset is successfully applied to the network. Currently, the application is capable to classify 43 different plants in mobile environment, and its classification capacity is planned to be expanded with new plant species as a future study. Up to 90% accuracy is reached in this study with the current version of the application.

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Cite This Article

ADAK, M. F. (2020). Identification of Plant Species by Deep Learning and Providing as A Mobile Application. Sakarya University Journal of Computer and Information Sciences, Cilt: 3 Sayı: 3, 231–238. https://ajindex.com/articles/946673

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References (1)

  1. M. Dyrmann, H. Karstoft, and H. S. Midtiby, “Plant species classification using deep convolutional neural network,” Biosyst. Eng., vol. 151, pp. 72–80, Nov. 2016, doi: 10.1016/j.biosystemseng.2016.08.024. P. Barré, B. C. Stöver, K. F. Müller, and V. Steinhage, “LeafNet: A computer vision system for automatic plant species identification,” Ecol. Inform., vol. 40, pp. 50–56, Jul. 2017, doi: 10.1016/j.ecoinf.2017.05.005. G. L. Grinblat, L. C. Uzal, M. G. Larese, and P. M. Granitto, “Deep learning for plant identification using vein morphological patterns,” Comput. Electron. Agric., vol. 127, pp. 418–424, Sep. 2016, doi: 10.1016/j.compag.2016.07.003. J. W. Tan, S.-W. Chang, S. Binti Abdul Kareem, H. J. Yap, and K.-T. Yong, “Deep Learning for Plant Species Classification using Leaf Vein Morphometric,” IEEE/ACM Trans. Comput. Biol. Bioinforma., pp. 1–1, 2018, doi: 10.1109/TCBB.2018.2848653. A. Ambarwari, Q. J. Adrian, Y. Herdiyeni, and I. Hermadi, “Plant species identification based on leaf venation features using SVM,” TELKOMNIKA (Telecommunication Comput. Electron. Control., vol. 18, no. 2, p. 726, Apr. 2020, doi: 10.12928/telkomnika.v18i2.14062. H. F. Eid and A. Abraham, “Plant species identification using leaf biometrics and swarm optimization: A hybrid PSO, GWO, SVM model,” Int. J. Hybrid Intell. Syst., vol. 14, no. 3, pp. 155–165, Mar. 2018, doi: 10.3233/HIS-180248. M. A. Islama, S. I. Yousuf, and M. M. Billah, “Automatic Plant Detection Using HOG and LBP Features With SVM,” Int. J. Comput., vol. 33, no. 1, pp. 26–38, 2019. I. Gogul and V. S. Kumar, “Flower species recognition system using convolution neural networks and transfer learning,” in 2017 Fourth International Conference on Signal Processing, Communication and Networking (ICSCN), 2017, pp. 1–6, doi: 10.1109/ICSCN.2017.8085675. M. Toğaçar, B. Ergen, and Z. Cömert, “Classification of flower species by using features extracted from the intersection of feature selection methods in convolutional neural network models,” Measurement, vol. 158, p. 107703, Jul. 2020, doi: 10.1016/j.measurement.2020.107703. M. Momeny, A. Jahanbakhshi, K. Jafarnezhad, and Y.-D. Zhang, “Accurate classification of cherry fruit using deep CNN based on hybrid pooling approach,” Postharvest Biol. Technol., vol. 166, p. 111204, Aug. 2020, doi: 10.1016/j.postharvbio.2020.111204. Y. Ren, N. Wang, M. Li, and Z. Xu, “Deep density-based image clustering,” Knowledge-Based Syst., vol. 197, p. 105841, Jun. 2020, doi: 10.1016/j.knosys.2020