Disease Detection in Banana Leaf Plants using DenseNet and Inception Method
Abstract
Diseases that attack banana plants can affect the growth and productivity of the fruit produced. The disease can be identified by looking at changes in the pattern and color of the leaves. Infected leaves will experience an increased transpiration process and the photosynthesis process is almost non-existent. Furthermore, disease on banana leaves can cause yield losses of up to 50%. Therefore, early detection is needed so that diseases on banana leaves can be overcome as soon as possible by using deep learning. This study aims to compare the performance of DenseNet and Inception methods in detecting disease on banana leaves. DenseNet is a transfer learning architecture model with fewer parameters and computations to achieve good performance. Inception, on the other hand, is a transfer learning architectural model that applies cross-channel correlation, executes at lower resolution inputs, and avoids spatial dimensions. In conducting the test, this study uses several data handling schemes to test the two methods, namely without data handling, under-sampling, and oversampling. Furthermore, the data is separated into training data and test data with a ratio of 80:20. The result is that the model using the DenseNet method with an oversampling scheme is superior to other models with a percentage value of 84.73% accuracy, 84.80% precision, 84.73% recall, and 84.62% f1 score. In addition, the machine learning model using the DenseNet method in all schemes is also superior to the machine learning model using the Inception method.
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References
S. Deenan, S. Janakiraman, and S. Nagachandrabose, “Image Segmentation Algorithms for Banana Leaf Disease Diagnosis,” J. Inst. Eng. Ser. C, vol. 101, no. 5, pp. 807–820, 2020, DOI: 10.1007/s40032-020-00592-5.
J. Chen, J. Chen, D. Zhang, Y. Sun, and Y. A. Nanehkaran, “Using deep transfer learning for image-based plant disease identification,” Comput. Electron. Agric., vol. 173, no. March, p. 105393, 2020, DOI: 10.1016/j.compag.2020.105393.
M. Sardogan, A. Tuncer, and Y. Ozen, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm,” UBMK 2018 - 3rd Int. Conf. Comput. Sci. Eng., pp. 382–385, 2018, DOI: 10.1109/UBMK.2018.8566635.
R. Drienovsky, C. Rujescu, F. Sala, and P. Nutrition, “Scan Sick & Healthy Leaf – A SOFTWARE APPLICATION FOR THE DETERMINATION OF THE DEGREE OF THE LEAVES ATTACK,” vol. 49, no. 4, pp. 225–233, 2017.
S. Moscatello et al., “Peach leaf curl disease shifts sugar metabolism in severely infected leaves from source to sink,” Plant Physiol. Biochem., vol. 112, pp. 9–18, 2017, DOI: 10.1016/j.plaphy.2016.12.001.
J. Amara, B. Bouaziz, and A. Algergawy, “A deep learning-based approach for banana leaf diseases classification,” Lect. Notes Informatics (LNI), Proc. - Ser. Gesellschaft fur Inform., vol. 266, pp. 79–88, 2017.
K. Al Mahmud, "Banana Leaf Dataset", Kaggle.com, 2022. [Online]. Available: https://www.kaggle.com/kaiesalmahmud/banana-leaf-dataset. [Accessed: 20- Feb- 2022].
F. Rayhan, S. Ahmed, A. Mahbub, R. Jani, S. Shatabda, and D. M. Farid, “CUSBoost: Cluster-Based Under-Sampling with Boosting for Imbalanced Classification,” 2nd Int. Conf. Comput. Syst. Inf. Technol. Sustain. Solut. CSITSS 2017, pp. 1–5, 2018, DOI: 10.1109/CSITSS.2017.8447534.
R. Mohammed, J. Rawashdeh and M. Abdullah, "Machine learning with Oversampling and Undersampling Techniques: Overview Study and Experimental Results," 2020 11th International Conference on Information and Communication Systems (ICICS), 2020, pp. 243-248, DOI: 10.1109/ICICS49469.2020.239556.
B. Eagan, M. Misfeldt, and A. Siebert-Evenstone, Advances in Quantitative Ethnography, vol. 1. 2019.
H. Salehinejad, S. Valaee, T. Dowdell and J. Barfett, "Image Augmentation Using Radial Transform for Training Deep Neural Networks," 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2018, pp. 3016-3020, DOI: 10.1109/ICASSP.2018.8462241.
G. Huang and K. Q. Weinberger, “Densely Connected Convolutional Networks.”
M. Hasan, B. Tanawala, and K. J. Patel, “Deep learning Precision Farming: Tomato Leaf Disease Detection by Transfer Learning,” SSRN Electron. J., pp. 1–5, 2019, DOI: 10.2139/ssrn.3349597.
"Transfer Learning dan Fine Tuning – 3 – SkillPlus", Skillplus.web.id, 2022. [Online]. Available: https://skillplus.web.id/transfer-learning-dan-fine-tuning-3/. [Accessed: 20- Feb- 2022].
J. Xu, Y. Zhang, and D. Miao, “Three-way confusion matrix for classification: A measure driven view,” Inf. Sci. (NY)., vol. 507, pp. 772–794, 2020, DOI: 10.1016/j.ins.2019.06.064
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