Classification of Banana Leaf Diseases Using a GoogleNet-Based Convolutional Neural Network Architecture

Authors

  • Nurul Arifin Teknik Informatika, Universitas Yudharta Pasuruan, East Java, Indonesia

DOI:

https://doi.org/10.58982/krisnadana.v4i2.749

Keywords:

Analysis Results, CNN Method, Classification, GoogleNet

Abstract

Farmers generally identify the type of pest on banana leaves based on the size, shape, and color of the leaves alone. CNN GoogleNet architecture algorithm was used in this study to classify banana leaf disease and divide it into each class. This study was divided into 3 classes, namely healthy leaves, sigatoka, and Fusarium wilt. In summary, GoogleNet has five to thirteen layers. From the results of the analysis of the trial process that has been carried out from the research stage, from epoch 10 to 100 in summary from each experiment GoogleNet produced the best accuracy results model from batch size 35 and epoch 60 in the healthy leaf class produced an accuracy of 90.59%, epoch 80 in the layufusarium produced 90.30% and at epoch 70 in sigatoka produced 90.49%.

References

B. Haveri and K. S. Raj, “Review on Plant Disease Detection using Deep Learning,” in 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS), 2022, pp. 359–365. doi: 10.1109/ICAIS53314.2022.9742921.

G. Gudero Mengesha, H. Terefe Yetayew, and A. Kesho Sako, “Spatial distribution and association of banana (Musa spp.) Fusarium wilt (Fusarium oxysporum f. sp. cubense) epidemics with biophysical factors in southwestern Ethiopia,” Arch. Phytopathol. Plant Prot., vol. 51, no. 11–12, pp. 575–601, 2018, doi: 10.1080/03235408.2018.1502067.

A. Ridhovan, A. Suharso, and C. Rozikin, “Disease detection in banana leaf plants using densenet and inception method,” J. RESTI (Rekayasa Sist. Dan Teknol. Informasi), vol. 6, no. 5, pp. 710–718, 2022, doi: 10.29207/resti.v6i5.4202.

N. Ani Brown Mary, A. Robert Singh, and S. Athisayamani, “Classification of Banana Leaf Diseases Using Enhanced Gabor Feature Descriptor BT - Inventive Communication and Computational Technologies,” 2021, pp. 229–242.

C. Science, “Designing Automatic Banana Leaf Diseases Identification Model Using Machine Learning Techniques,” 2021.

J. D. Thiagarajan et al., “Analysis of banana plant health using machine learning techniques,” Sci. Rep., vol. 14, no. 1, p. 15041, 2024, doi: 10.1038/s41598-024-63930-y.

L. C. Ngugi, M. Abelwahab, and M. Abo-Zahhad, “Recent advances in image processing techniques for automated leaf pest and disease recognition–A review,” Inf. Process. Agric., vol. 8, no. 1, pp. 27–51, 2021, doi: 10.1016/j.inpa.2020.04.004.

J. Ni, J. Gao, L. Deng, and Z. Han, “Monitoring the Change Process of Banana Freshness by GoogLeNet,” IEEE Access, vol. 8, pp. 228369–228376, 2020, doi: 10.1109/ACCESS.2020.3045394.

C. Sarkar, D. Gupta, U. Gupta, and B. B. Hazarika, “Leaf disease detection using machine learning and deep learning: Review and challenges,” Appl. Soft Comput., vol. 145, p. 110534, 2023, doi: https://doi.org/10.1016/j.asoc.2023.110534.

A. Faizin, F. Teknik, U. Y. Pasuruan, and C. N. Network, “Perbandingan arsitektur lenet dan googlenet dalam klasifikasi diabetic retinopathy pada citra retina fundus,” vol. 6, no. 1, 2022.

K. Seetharaman and T. Mahendran, “Leaf Disease Detection in Banana Plant using Gabor Extraction and Region-Based Convolution Neural Network (RCNN),” J. Inst. Eng. Ser. A, vol. 103, no. 2, pp. 501–507, 2022, doi: 10.1007/s40030-022-00628-2.

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.

N. B. Raja and P. S. Rajendran, “Comparative Analysis of Banana Leaf Disease Detection and Classification Methods,” in 2022 6th International Conference on Computing Methodologies and Communication (ICCMC), 2022, pp. 1215–1222. doi: 10.1109/ICCMC53470.2022.9753840.

R. Sangeetha, J. Logeshwaran, J. Rocher, and J. Lloret, “An Improved Agro Deep Learning Model for Detection of Panama Wilts Disease in Banana Leaves,” AgriEngineering, vol. 5, no. 2. pp. 660–679, 2023. doi: 10.3390/agriengineering5020042.

V. G. Krishnan, J. Deepa, P. V. Rao, V. Divya, and S. Kaviarasan, “An automated segmentation and classification model for banana leaf disease detection,” vol. 10, no. 01, pp. 213–220, 2022, doi: 10.7324/JABB.2021.100126.

S. Ratna, “Pengolahan Citra Digital Dan Histogram Dengan Phyton Dan Text Editor Phycharm,” Technol. J. Ilm., vol. 11, no. 3, p. 181, 2020, doi: 10.31602/tji.v11i3.3294.

S. Ilahiyah and A. Nilogiri, “Implementasi Deep Learning Pada Identifikasi Jenis Tumbuhan Berdasarkan Citra Daun Menggunakan Convolutional Neural Network,” pp. 49–56, 2000.

F. Felix, S. Faisal, T. F. M. Butarbutar, and P. Sirait, “Implementasi CNN dan SVM untuk Identifikasi Penyakit Tomat via Daun,” J. SIFO Mikroskil, vol. 20, no. 2, pp. 117–134, 2019, doi: 10.55601/jsm.v20i2.670.

A. J. Rozaqi, A. Sunyoto, and M. rudyanto Arief, “Deteksi Penyakit Pada Daun Kentang Menggunakan Pengolahan Citra dengan Metode Convolutional Neural Network,” Creat. Inf. Technol. J., vol. 8, no. 1, p. 22, 2021, doi: 10.24076/citec.2021v8i1.263.

I. A. Sabilla, “ARSITEKTUR CONVOLUTIONAL NEURAL NETWORK ( CNN ) UNTUK KLASIFIKASI JENIS,” 2020.

M. I. Rosadi and M. Lutfi, “Identifikasi Jenis Penyakit Daun Jagung Menggunakan Deep Learning Pre-Trained Model,” Explor. IT! J. Keilmuan dan Apl. Tek. Inform., vol. 13, no. 2, pp. 35–42, 2021.

Downloads

Published

2025-01-31

How to Cite

Arifin, N. (2025). Classification of Banana Leaf Diseases Using a GoogleNet-Based Convolutional Neural Network Architecture. Krisnadana Journal, 4(2), 95-102. https://doi.org/10.58982/krisnadana.v4i2.749