Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach
DOI:
https://doi.org/10.57152/predatecs.v1i2.872Keywords:
Convolutional Neural Network, InceptionV3, Mango, Tranfer Learning, VGG16Abstract
Plant diseases pose a serious threat to a country's economy and food security. One way to identify diseases in plants is through the visible features on their leaves. Farmers need to conduct an active examination of the condition of the leaves of plants to eradicate this disease. In this case, automatic recognition and classification of diseases of leaf crops is required in order to obtain an accurate identification. Digital image processing technology can be used to solve this problem. One effective approach is the Convolutional Neural Network (CNN). The trial image used a dataset consisting of 4000 images of mango leaf disease, namely Anthracnose, Bacterial Canker, Cutting Weevil, Die Back, Gall Midge, Powdery Mildew, and Sooty Mould. This study aims to compare the accuracy of CNN, VGG16 and InceptionV3. Architectural modeling uses these drawings to train and test models in recognizing and classifying mango leaf diseases. The results of modeling trials in the three scenarios were most optimally obtained by VGG16 with an accuracy of 96.87%, then InceptionV3 with an acquisition of 96.50% and CNN by 81%.
References
D. Faye, I. Diop, and D. Dione, “Mango Diseases Classification Solutions Using Machine Learning or Deep Learning: A Review,” Journal of Computer and Communications, vol. 10, no. 12, pp. 16–28, 2022.
K. Maheshwari and A. Shrivastava, “A Review on Mango Leaf Diseases Identification using Convolution Neural Network,” International Journal of Scientific Research & Engineering Trends, vol. 6, no. 3, pp. 1399–1403, 2020.
P. Hari and M. P. Singh, “A lightweight convolutional neural network for disease detection of fruit leaves,” Neural Comput Appl, vol. 35, no. 20, pp. 14855–14866, 2023, doi: 10.1007/s00521-023-08496-y.
A. Singh and H. Kaur, “Comparative Study on Identification and Classification of Plant Diseases with the Support of Transfer Learning,” in International Conference on Innovative Computing and Communications: Proceedings of ICICC 2021, Volume 1, Springer, 2022, pp. 375–386.
D. DARMATASIA and A. M. SYAFAR, “Implementasi Convolutional Neural Network Untuk Klasifikasi Tanaman Rimpang Secara Virtual,” Jurnal INSTEK (Informatika Sains dan Teknologi), vol. 8, no. 1, pp. 122–131, 2023.
W. Lu, J. Li, J. Wang, and L. Qin, “A CNN-BiLSTM-AM method for stock price prediction,” Neural Comput Appl, vol. 33, no. 10, pp. 4741–4753, 2021, doi: 10.1007/s00521-020-05532-z.
M.-Y. Lee, J.-H. Lee, J.-K. Kim, B.-J. Kim, and J.-Y. Kim, “The Sparsity and Activation Analysis of Compressed CNN Networks in a HW CNN Accelerator Model,” in 2019 International SoC Design Conference (ISOCC), 2019, pp. 255–256. doi: 10.1109/ISOCC47750.2019.9027643.
S. Samala, N. Bhavith, R. Bang, D. K. Rao, C. R. Prasad, and S. Yalabaka, “Disease Identification in Tomato Leaves Using Inception V3 Convolutional Neural Networks,” in 2023 7th International Conference on Trends in Electronics and Informatics (ICOEI), 2023, pp. 865–870. doi: 10.1109/ICOEI56765.2023.10125758.
K. Kusrini, S. Suputa, A. Setyanto, I. M. A. Agastya, H. Priantoro, and S. Pariyasto, “A comparative study of mango fruit pest and disease recognition,” TELKOMNIKA (Telecommunication Computing Electronics and Control), vol. 20, no. 6, pp. 1264–1275, 2022.
T. Ayu, V. Dwi, and A. E. Minarno, “Pendiagnosa Daun Mangga Dengan Model Convolutional Neural Network,” CESS (Journal of Computer Engineering, System and Science), vol. 6, no. 2, pp. 230–235.
Y. Nagaraju, T. S. Sahana, S. Swetha, and S. U. Hegde, “Transfer learning based convolutional neural network model for classification of mango leaves infected by anthracnose,” in 2020 IEEE International Conference for Innovation in Technology (INOCON), IEEE, 2020, pp. 1–7.
M. Alencastre-Miranda, R. M. Johnson, and H. I. Krebs, “Convolutional neural networks and transfer learning for quality inspection of different sugarcane varieties,” IEEE Trans Industr Inform, vol. 17, no. 2, pp. 787–794, 2020.
M. S. Memon, P. Kumar, and R. Iqbal, “Meta Deep Learn Leaf Disease Identification Model for Cotton Crop,” Computers, vol. 11, no. 7, p. 102, 2022.
U. S. Rao, R. Swathi, V. Sanjana, L. Arpitha, K. Chandrasekhar, and P. K. Naik, “Deep learning precision farming: grapes and mango leaf disease detection by transfer learning,” Global Transitions Proceedings, vol. 2, no. 2, pp. 535–544, 2021.
A. Nana Hermana, D. Rosmala, and M. Gustiana Husada, “Transfer Learning for Classification of Fruit Ripeness Using VGG16,” in 2021 The 4th International Conference on Computers in Management and Business, 2021, pp. 139–146.
M. Bouni, B. Hssina, K. Douzi, and S. Douzi, “Impact of pretrained deep neural networks for tomato leaf disease prediction,” Journal of Electrical and Computer Engineering, vol. 2023, 2023.
A. Bhola, S. Verma, and P. Kumar, “A comparative analysis of deep learning models for cucumber disease classification using transfer learning,” J Curr Sci Technol, vol. 13, no. 1, pp. 23–35, 2023.
N. S. Shadin, S. Sanjana, and N. J. Lisa, “COVID-19 Diagnosis from Chest X-ray Images Using Convolutional Neural Network(CNN) and InceptionV3,” in 2021 International Conference on Information Technology, ICIT 2021 - Proceedings, Institute of Electrical and Electronics Engineers Inc., Jul. 2021, pp. 799–804. doi: 10.1109/ICIT52682.2021.9491752.
A. Gupta, S. Mishra, S. C. Sahu, U. Srinivasarao, and K. J. Naik, “Application of Convolutional Neural Networks for COVID-19 Detection in X-ray Images Using InceptionV3 and U-Net,” New Gener Comput, Jun. 2023, doi: 10.1007/s00354-023-00217-2.
K. Liu, S. Yu, and S. Liu, “An Improved InceptionV3 Network for Obscured Ship Classification in Remote Sensing Images,” IEEE J Sel Top Appl Earth Obs Remote Sens, vol. 13, pp. 4738–4747, 2020, doi: 10.1109/JSTARS.2020.3017676.
