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  2. Vol 20, No 2 (2023)
  3. Terisia

Comparison of EfficientNet B5-B6 for Detection of 29 Diseases of Fruit Plants

Vany Terisia, Widi Hastomo, Adhitio Satyo Bayangkari Karno, Ellya Sestri, Diana Yusuf, Shevty Arbekti Arman, Nada Kamilia

Abstract


In initiatives to meet food needs and enhance the wellbeing of farmers and society at large, crop production performance is essential. For early attempts to be made for quick handling to prevent crop failure, farmers must be able to readily and quickly receive information in order to detect plant illnesses. In this study, two Convolutional Neural Network (CNN) architectures namely, EfficientNet versions B5 and B6 are used to develop a classification model for plant disease using Deep Learning (DL). The 66,556 visuals in the dataset, which is from Kaggle.com, are used. To create a model, the training method uses 57,067 images data and 3,170 image data for validation. The EfficientNet architecture versions B5 and B6 received very good accuracy scores for the total test results, namely 0.9905 and 0.9927. The model testing phase was carried out through testing phases utilising 3.171 images data. Future analysis can compare CNN architectures and try it with different datasets.


Keywords


Convolutional Neural Network; Deep Learning; EfficientNet

References


Arun, Y. and Viknesh, G.S. (2022) ‘Leaf Classification for Plant Recognition Using EfficientNet Architecture’, in 2022 IEEE Fourth International Conference on Advances in Electronics, Computers and Communications (ICAECC), pp. 1–5. Available at: https://doi.org/10.1109/ICAECC54045.2022.9716637.

Atila, Ü. et al. (2021) ‘Plant leaf disease classification using EfficientNet deep learning model’, Ecological Informatics, 61, p. 101182. Available at: https://doi.org/https://doi.org/10.1016/j.ecoinf.2020.101182.

Ferentinos, K.P. (2018) ‘Deep learning models for plant disease detection and diagnosis’, Computers and Electronics in Agriculture, 145, pp. 311–318. Available at: https://doi.org/https://doi.org/10.1016/j.compag.2018.01.009.

Gehlot, M. and Gandhi, G.C. (2023) ‘“EffiNet-TS”: A deep interpretable architecture using EfficientNet for plant disease detection and visualization’, Journal of Plant Diseases and Protection, 130(2), pp. 413–430. Available at: https://doi.org/10.1007/s41348-023-00707-x.

Hanh, B.T., Van Manh, H. and Nguyen, N.-V. (2022) ‘Enhancing the performance of transferred efficientnet models in leaf image-based plant disease classification’, Journal of Plant Diseases and Protection, 129(3), pp. 623–634. Available at: https://doi.org/10.1007/s41348-022-00601-y.

Hridoy, R.H. and Tuli, M.R.A. (2021) ‘A Deep Ensemble Approach for Recognition of Papaya Diseases using EfficientNet Models’, in 2021 5th International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1–6. Available at: https://doi.org/10.1109/ICEEICT53905.2021.9667825.

Huang, C. et al. (2022) ‘Tuberculosis Diagnosis using Deep Transferred EfficientNet’, IEEE/ACM Transactions on Computational Biology and Bioinformatics, pp. 1–9. Available at: https://doi.org/10.1109/TCBB.2022.3199572.

Jeddi, A.B., Shafieezadeh, A. and Nateghi, R. (2023) ‘PDP-CNN: A Deep Learning Model for Post-Hurricane Reconnaissance of Electricity Infrastructure on Resource-Constrained Embedded Systems at the Edge’, IEEE Transactions on Instrumentation and Measurement, 72, pp. 1–9. Available at: https://doi.org/10.1109/TIM.2023.3236321.

Riehl, K., Neunteufel, M. and Hemberg, M. (2023) ‘Hierarchical confusion matrix for classification performance evaluation’, Journal of the Royal Statistical Society Series C: Applied Statistics [Preprint]. Available at: https://doi.org/10.1093/jrsssc/qlad057.

Sankaran, S. et al. (2010) ‘A review of advanced techniques for detecting plant diseases’, Computers and Electronics in Agriculture, 72(1), pp. 1–13. Available at: https://doi.org/https://doi.org/10.1016/j.compag.2010.02.007.

Shah, H.A. et al. (2022) ‘A Robust Approach for Brain Tumor Detection in Magnetic Resonance Images Using Finetuned EfficientNet’, IEEE Access, 10, pp. 65426–65438. Available at: https://doi.org/10.1109/ACCESS.2022.3184113.

Srinidhi, V. V, Sahay, A. and Deeba, K. (2021) ‘Plant Pathology Disease Detection in Apple Leaves Using Deep Convolutional Neural Networks : Apple Leaves Disease Detection using EfficientNet and DenseNet’, in 2021 5th International Conference on Computing Methodologies and Communication (ICCMC), pp. 1119–1127. Available at: https://doi.org/10.1109/ICCMC51019.2021.9418268.

Straub, J. (2021) ‘Machine learning performance validation and training using a “perfect” expert system’, MethodsX, 8, p. 101477. Available at: https://doi.org/https://doi.org/10.1016/j.mex.2021.101477.

Sun, X. et al. (2022) ‘Research on plant disease identification based on CNN’, Cognitive Robotics, 2, pp. 155–163. Available at: https://doi.org/https://doi.org/10.1016/j.cogr.2022.07.001.

Tan, M., & Le, Q. (2019) ‘Efficientnet: Rethinking model scaling for convolutional neural networks’, International conference on machine learning (PMLR), pp. 6105–6114.

Tan, M. and Le, Q. (2019) ‘EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks’, in K. Chaudhuri and R. Salakhutdinov (eds) Proceedings of the 36th International Conference on Machine Learning. PMLR (Proceedings of Machine Learning Research), pp. 6105–6114. Available at: https://proceedings.mlr.press/v97/tan19a.html.

Xenakis, A. et al. (2020) ‘Applying a Convolutional Neural Network in an IoT Robotic System for Plant Disease Diagnosis’, in 2020 11th International Conference on Information, Intelligence, Systems and Applications (IISA, pp. 1–8. Available at: https://doi.org/10.1109/IISA50023.2020.9284356.

Y, V. et al. (2022) ‘Early Detection of Casava Plant Leaf Diseases using EfficientNet-B0’, in 2022 IEEE Delhi Section Conference (DELCON), pp. 1–5. Available at: https://doi.org/10.1109/DELCON54057.2022.9753210.

Zhang, P., Yang, L. and Li, D. (2020) ‘EfficientNet-B4-Ranger: A novel method for greenhouse cucumber disease recognition under natural complex environment’, Computers and Electronics in Agriculture, 176, p. 105652. Available at: https://doi.org/https://doi.org/10.1016/j.compag.2020.105652.


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DOI: 10.30595/sainteks.v20i2.18691

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ISSN: 2686-0546