Analysis for Detecting Banana Leaf Disease Using the CNN Method
DOI:
https://doi.org/10.30595/juita.v13i1.24514Keywords:
agricultural sector, banana leaf disease detection, CNN model.Abstract
Banana farmers face major challenges due to banana leaf diseases such as Cordana, Pestalotiopsis and Sigatoka, which severely affect the quality and quantity of the crop. Early detection of these diseases is particularly challenging as the initial symptoms are often similar to other disorders. To solve this problem, fast and accurate automated detection is needed to help farmers effectively identify diseases on banana leaves. This research focuses on developing a banana leaf disease detection model using Convolutional Neural Network (CNN) method with MobileNetV2 architecture. The dataset used consists of 937 images of both infected and healthy banana leaves. These images were collected under various lighting conditions and viewing angles to simulate real field situations. The dataset was divided into 70% for training, 20% for validation, and 10% for testing, to ensure robust model evaluation. The CNN model was trained to recognize important visual features on banana leaves that indicate disease infection. The results showed that the model was able to detect banana leaf diseases with an accuracy of 90.62%, indicating high effectiveness. This accuracy confirms the potential of CNN in significantly improving the disease detection process on banana plants. This research is expected to help farmers identify diseases more quickly and accurately, thereby minimizing yield losses and increasing productivity. In addition, this research provides valuable insights into the application of technology in agriculture, particularly in plant disease detection which opens up opportunities for further advancements in this sector.
References
[1] M. George, K. Anita Cherian, and D. Mathew, “Symptomatology of Sigatoka leaf spot disease in banana landraces and identification of its pathogen as Mycosphaerella eumusae,” Journal of the Saudi Society of Agricultural Sciences, vol. 21, no. 4, pp. 278–287, May 2022, doi: 10.1016/j.jssas.2021.09.004.
[2] T. Thorat, B. K. Patle, and S. K. Kashyap, “Intelligent insecticide and fertilizer recommendation system based on TPF-CNN for smart farming,” Smart Agricultural Technology, vol. 3, Feb. 2023, doi: 10.1016/j.atech.2022.100114.
[3] Andreanov Ridhovan, Aries Suharso, and Chaerur Rozikin, “Disease Detection in Banana Leaf Plants using DenseNet and Inception Method,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 6, no. 5, pp. 710–718, Oct. 2022, doi: 10.29207/resti.v6i5.4202.
[4] L. Tresnawati and D. B. Sukriyansah, “Image Classification on Garutan Batik Using Convolutional Neural Network with Data Augmentation,” e-ISSN: 2579-8901; Vol. 11, No. 1, May 2023, doi: 10.30595/juita.v11i1.16166. [Online]. Available: www.kaggle.com/datasets/ionisiusdh/indones
[5] A. Kumar, N. Gaur, and A. Nanthaamornphong, “Machine learning RNNs, SVM and NN Algorithm for Massive-MIMO-OTFS 6G Waveform with Rician and Rayleigh channel,” Egyptian Informatics Journal, vol. 27, Sep. 2024, doi: 10.1016/j.eij.2024.100531.
[6] Y. Fonseca, C. Bautista, C. Pardo-Beainy, and C. Parra, “A plum selection system that uses a multi-class Convolutional Neural Network (CNN),” J Agric Food Res, vol. 14, Dec. 2023, doi: 10.1016/j.jafr.2023.100793.
[7] P. Sajitha, A. Diana Andrushia, N. Mostafa, A. Younes Shdefat, S. S. Suni, and N. Anand, “Smart farming application using knowledge embedded-graph convolutional neural network (KEGCNN) for banana quality detection,” J Agric Food Res, vol. 14, Dec. 2023, doi: 10.1016/j.jafr.2023.100767.
[8] M. Al-Dairi and P. B. Pathare, “Evaluation of physio-chemical characteristics of ‘Fard’ banana using computer vision system,” J Agric Food Res, vol. 15, Mar. 2024, doi: 10.1016/j.jafr.2024.101057.
[9] M. Rosyda, “Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network,” e-ISSN: 2579-8901; Vol. 10, No. 1, May 2022, doi:10.30595/juita.v10i1.12573.
[10] N. Mduma and J. Leo, “Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania,” Data Brief, vol. 49, Aug. 2023, doi: 10.1016/j.dib.2023.109322.
[11] M Gomez Selvaraj, A Vergara, F Montenegro, H A Ruiz,N Safari, D Raymaekers, W Ocimati, J Ntamwira, L Tits, A B Omondi, G Blomme, “Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 169, pp. 110–124, Nov. 2020, doi: 10.1016/j.isprsjprs.2020.08.025.
[12] L Fu, J Duan, X Zou, G Lin, S Song, Bang Ji, Zhou Yang “Banana detection based on color and texture features in the natural environment,” Comput Electron Agric, vol. 167, Dec. 2019, doi: 10.1016/j.compag.2019.105057.
[13] T. Phattaraworamet, S. Sangsuriyun, P. Kutchomsri, and S. Chokphoemphun, “Image classification of lotus in Nong Han Chaloem Phrakiat Lotus Park using convolutional neural networks,” Artificial Intelligence in Agriculture, vol. 11, pp. 23–33, Mar. 2024, doi: 10.1016/j.aiia.2023.12.003.
[14] M. A. Syafrudin and S. Warsito, “K-Means Clustering for Grouping Rivers in DIY based on Water Quality Parameters,” 155-163, 2023, doi: 10.30595/juita.v11i1.16986.
[15] S. Shetty and T. R. Mahesh, “SKGDC: Effective Segmentation Based Deep Learning Methodology for Banana Leaf, Fruit, and Stem Disease Prediction,” SN Comput Sci, vol. 5, no. 6, Aug. 2024, doi: 10.1007/s42979-024-03031-9.
[16] J. Deng, W. Huang, G. Zhou, Y. Hu, L. Li, and Y. Wang, “Identification of banana leaf disease based on KVA and GR-ARNet,” J Integr Agric, vol. 23, no. 10, pp. 3554–3575, Oct. 2024, doi: 10.1016/j.jia.2023.11.037.
[17] D. J. Maulana, S. Saadah, and P. E. Yunanto, “54-61 Data in Classifying Financial Distress Companies using SVM and Naïve Bayes,” J. RESTI (Rekayasa Sist. Teknol. Inf.), vol. 10, no. 1, pp. 54–61, 2024, doi: 10.29207/resti.v8i1.5150.
[18] M. A. B. Bhuiyan, H. M. Abdullah, S. E. Arman, S. Saminur Rahman, and K. Al Mahmud, “BananaSqueezeNet: A very fast, lightweight convolutional neural network for the diagnosis of three prominent banana leaf diseases,” Smart Agricultural Technology, vol. 4, Aug. 2023, doi: 10.1016/j.atech.2023.100214.
[19] A. Prasetyo and E. Utami, “Detection and Classification of Banana Leaf Diseases: Systematic Literature Review,” Telematika, vol. 17, no. 2, pp. 128–141, Aug. 2024, doi: 10.35671/telematika.v17i2.2809.
[20] E. Correa, M. Garcia, G. Grosso, J. Huamantoma, and W. Ipanaque, “Design and implementation of a CNN architecture to classify images of banana leaves with diseases,” in 2021 IEEE International Conference on Automation/24th Congress of the Chilean Association of Automatic Control, ICA-ACCA 2021, Institute of Electrical and Electronics Engineers Inc., Mar. 2021. doi: 10.1109/ICAACCA51523.2021.9465178.
[21] Tejaswini, P. Rastogi, S. Dua, Manikanta, and V. Dagar, “Early Disease Detection in Plants using CNN,” in Procedia Computer Science, Elsevier B.V., 2024, pp. 3468–3478. doi: 10.1016/j.procs.2024.04.327.
[22] Brianorman and D. Utami, “Comparative Analysis of CNN Architectures for SIBI Image Classification,” e-ISSN: 2579-8901; Vol. 12, No. 1, May 2024, doi: 10.30595/juita.v12i1.20608. [Online]. Available: https://bit.ly/3trwIlH
[23] M. Kumar and A. Kumar, “Deep Learning Meets Support Vector Machines: An Effective Hybrid Model for Banana Leaf Wilt Disease Severity Assessment,” in 2024 2nd International Conference on Disruptive Technologies, ICDT 2024, Institute of Electrical and Electronics Engineers Inc., 2024, pp. 386–390. doi: 10.1109/ICDT61202.2024.10489081.
[24] N. B. Raja and P. Selvi Rajendran, “Comparative Analysis of Banana Leaf Disease Detection and Classification Methods,” in Proceedings - 6th International Conference on Computing Methodologies and Communication, ICCMC 2022, Institute of Electrical and Electronics Engineers Inc., 2022, pp. 1215–1222. doi: 10.1109/ICCMC53470.2022.9753840.
[25] D. Rustandi, Sony Hartono Wijaya, Mushthofa, and Ratih Damayanti, “Anatomy Identification of Bamboo Stems with The Convolutional Neural Networks (CNN) Method,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 8, no. 1, pp. 62–71, Feb. 2024, doi: 10.29207/resti.v8i1.5370.
[26] D. Tribuana, Hazriani, and A. L. Arda, “Image Preprocessing Approaches Toward Better Learning Performance with CNN,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 8, no. 1, pp. 1–9, Jan. 2024, doi: 10.29207/resti.v8i1.5417.
[27] K. Seetharaman and T. Mahendran, “Leaf Disease Detection in Banana Plant using Gabor Extraction and Region-Based Convolution Neural Network (RCNN),” Journal of The Institution of Engineers (India): Series A, vol. 103, no. 2, pp. 501–507, Jun. 2022, doi: 10.1007/s40030-022-00628-2.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 JUITA: Jurnal Informatika
This work is licensed under a Creative Commons Attribution 4.0 International License.
JUITA: Jurnal Informatika is licensed under a Creative Commons Attribution 4.0 International License.
