Identification of 29 Types of Plant Diseases using Deep Learning EfficientNetB3

Adhitio Satyo Bayangkari Karno, Widi Hastomo, Indra Sari Kusuma Wardhana, Sutarno Sutarno, Dodi Arif

Abstract


To supply the world's food needs in the midst of the existing food crisis, farmers urgently need to expand crop production. By establishing it simple to recognize the kind of plant disease so that earlier control efforts could be conducted, farmers' harvest failures driven on by disease attacks must be prevented. In this study, one of the Convolutional Neural Network (CNN) architectures known EfficeintNetB3 is applied to generate a classification model for 29 different types of plant diseases. A model is created after 3,170 image data are used for validation and 57,067 image data were utilized for training. 3,171 image data tests were conducted as part of the model testing phase, and the total test results were produced an extraordinarily high accuracy score of 0.99 percentage and an F1-score

Keywords


Convolution Neural Network, Deep Learning, EfficeintNetB3

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References


and M. P. Dario Caldara, Sarah Conlisk, Matteo Iacoviello, “The Effect of the War in Ukraine on Global Activity and Inflation,” https://www.federalreserve.gov/, 2022. https://www.federalreserve.gov/econres/notes/feds-notes/the-effect-of-the-war-in-ukraine-on-global-activity-and-inflation-20220527.htm

John Ciempa, “The benefits of sustainable agriculture and how we get there,” https://www.ibm.com/, 2021. https://www.ibm.com/blogs/internet-of-things/the-benefits-of-sustainable-agriculture-and-how-we-get-there/

M. R. Timaeus, J., Ruigrok, T., Siegmeier, T., & Finckh, “Adoption of Food Species Mixtures from Farmers’ Perspectives in Germany: Managing Complexity and Harnessing Advantages.,” Agric. Ecosyst. Environ., vol. 12, no. 5, p. 697, 2022.

H. Y. PAN, S. Q., QIAO, J. F., Rui, W. A. N. G., YU, H. L., Cheng, W. A. N. G., TAYLOR, K., & PAN, “Intelligent diagnosis of northern corn leaf blight with deep learning model,” J. Integr. Agric., vol. 21, no. 4, pp. 1094–1105, 2022.

S. Nagpal, P., Chaudhary, S., & Kumar, “Detection of Disease in Plants with Android Integration Using Machine Learning,” Int. Conf. Comput. Eng. Technol. Springer, Singapore., pp. 144–151, 2022.

S. Collinge, D. B., & Sarrocco, “Transgenic approaches for plant disease control: Status and prospects 2021,” Plant Pathol., vol. 71, no. 1, pp. 207–225, 2022.

T. Hasanaliyeva, G., Si Ammour, M., Yaseen, T., Rossi, V., & Caffi, “Innovations in Disease Detection and Forecasting: A Digital Roadmap for Sustainable Management of Fruit and Foliar Disease,” Agronomy, vol. 12, no. 7, p. 1707, 2022.

D. Couliably, S., Kamsu-Foguem, B., Kamissoko, D., & Traore, “Deep learning for precision agriculture: a bibliometric analysis,” Intell. Syst. with Appl., 2022.

W. Hastomo, “Klasifikasi Covid-19 Chest X-Ray Dengan Tiga Arsitektur Cnn (Resnet-152, Inceptionresnet-V2, Mobilenet-V2),” vol. 5, no. Dl, 2021.

W. Hastomo, “Convolution Neural Network Arsitektur Mobilenet-V2 Untuk Mendeteksi Tumor Otak,” vol. 5, no. Gambar 1, 2021.

W. Hastomo and S. Bayangkari, “Diagnosa Covid-19 Chest X-Ray Dengan Convolution Neural Network Arsitektur Resnet-152,” vol. 2, no. 1, pp. 26–33, 2021.

A. Satyo, B. Karno, W. Hastomo, Y. Efendi, and R. Irawati, “Arsitektur Alexnet Convolution Neural Network ( CNN ) Untuk Mendeteksi Covid-19 Image Chest-Xray,” pp. 482–485, 2021.

W. Hastomo, N. Aini, A. Satyo, B. Karno, and L. M. R. Rere, “Metode Pembelajaran Mesin untuk Memprediksi Emisi Manure Management,” vol. 11, no. 2, pp. 131–139, 2022.

R. Razfar, N., True, J., Bassiouny, R., Venkatesh, V., & Kashef, “Weed detection in soybean crops using custom lightweight deep learning models,” J. Agric. Food Res., vol. 8, 2022.

H. Peng, Y., Dallas, M. M., Ascencio-Ibáñez, J. T., Hoyer, J. S., Legg, J., Hanley-Bowdoin, L., ... & Yin, “Early detection of plant virus infection using multispectral imaging and spatial–spectral machine learning,” Sci. Reports Nat., vol. 12, no. 1, pp. 1–14, 2022.

V. B. Paymode, A. S., & Malode, “Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG,” Artif. Intell. Agric., vol. 6, pp. 23–33, 2022.

L. M. Tassis and R. A. Krohling, “Few-shot learning for biotic stress classification of coffee leaves,” Artif. Intell. Agric., vol. 6, pp. 55–67, 2022, doi: https://doi.org/10.1016/j.aiia.2022.04.001.

D. Wang, J. Wang, W. Li, and P. Guan, “T-CNN: Trilinear convolutional neural networks model for visual detection of plant diseases,” Comput. Electron. Agric., vol. 190, p. 106468, 2021, doi: https://doi.org/10.1016/j.compag.2021.106468.

X. Tang et al., “Deep6mAPred: A CNN and Bi-LSTM-based deep learning method for predicting DNA N6-methyladenosine sites across plant species,” Methods, vol. 204, pp. 142–150, 2022, doi: https://doi.org/10.1016/j.ymeth.2022.04.011.

B. Ahmad, A., Saraswat, D., Aggarwal, V., Etienne, A., & Hancock, “Performance of deep learning models for classifying and detecting common weeds in corn and soybean production systems,” Comput. Electron. Agric., vol. 184, p. 106081, 2021.

Q. Tan, M., & Le, “Efficientnet: Rethinking model scaling for convolutional neural networks,” Int. Conf. Mach. Learn., pp. 6105–6114, 2019.

S. M. Sakib, “Plant Disease Expert,” kaggle.com, 2022. https://www.kaggle.com/datasets/sadmansakibmahi/plant-disease-expert

W. Hastomo, A. S. Karno, S. Sutarno, D. Arif, & Moreta, E., and S. Sudjiran, “Mengatasi Ketimpangan Data Deep Neural Network dengan Pelipatan Fitur Data Klasifikasi Spektroskopi Darah,” J. Ilm., vol. 8, no. 2, pp. 579–591, 2022, doi: https://doi.org/10.35326/pencerah.v8i2.2251.

N. A. Alhichri, H., Alswayed, A. S., Bazi, Y., Ammour, N., & Alajlan, “Classification of remote sensing images using EfficientNet-B3 CNN model with attention,” IEEE Access, vol. 9, pp. 14078–14094, 2021.

Vardan Agarwal, “Complete Architectural Details of all EfficientNet Models,” Medium Toward Data Science, 2020. https://medium.com/towards-data-science/complete-architectural-details-of-all-efficientnet-models-5fd5b736142

C. O. Lee, Y., Park, J., & Lee, “Two-level group convolution.,” Neural Networks, 2022.




DOI: https://doi.org/10.15548/isrj.v2i02.4389

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Gedung Fakultas Sains dan Teknologi 
Kampus III Universitas Islam Negeri Imam Bonjol Padang
Sungai Bangek, Kec. Koto Tangah, Kota Padang, Sumatera Barat

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