Plant Leaf Disease Prediction Using Deep Dense Net Slice Fragmentation and Segmentation Feature Selection Using Convolution Neural Network
Keywords:Plant leaf disease, feature selection, segmentation, classification, Dn CNN, early identification, deep learning
Plant disease affects the agriculture production seasonally on variety of ways. Identifying and early detection of disease is an important factor for production management to improve the economic growth. Most commonly the disease symptoms are identified by observing the disease in the plant leaf region. Existing machine learning models use images with improper and high dimensional features that lead to inaccurate classification of plant leaf disease. To tackle this issues, we introduces a deep dense net slice fragmentation and segmentation feature selection and classification through optimized convolution neural network. Initially the wavelet Filters features are applied to enhance the image through structure normalization model. The slice fragment segmentation is applied to segment the disease covered region by identifying the realistic variation based on spectral histogram feature difference. Then cascaded edges and features are extracted and trained using deep Densenet Convolution Neural Network (DnCNN) to identify the plant disease effectively. The proposed system achieves best result compared to the other existing approaches in terms of precision rate, recall, f-measure and also superior due to the fact that the diseases are identified at an earlier stage.
R. I. Hasan, S. M. Yusuf, and L. Alzubaidi, “Review of the state of the art of deep learning for plant diseases: a broad analysis and discussion,” Plants, vol. 9, no. 10, article 1302, 2020.
V. K. Vishnoi, K. Kumar, B. Kumar, S. Mohan and A. A. Khan, "Detection of Apple Plant Diseases Using Leaf Images Through Convolutional Neural Network," in IEEE Access, vol. 11, pp. 6594-6609, 2023, doi: 10.1109/ACCESS.2022.3232917.
Y. Zhao et al., "Plant Disease Detection Using Generated Leaves Based on DoubleGAN," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 19, no. 3, pp. 1817-1826, 1 May-June 2022, doi: 10.1109/TCBB.2021.3056683.
M. Lv, G. Zhou, M. He, A. Chen, W. Zhang and Y. Hu, "Maize Leaf Disease Identification Based on Feature Enhancement and DMS-Robust Alexnet," in IEEE Access, vol. 8, pp. 57952-57966, 2020, doi: 10.1109/ACCESS.2020.2982443.
T. N. Pham, L. V. Tran and S. V. T. Dao, "Early Disease Classification of Mango Leaves Using Feed-Forward Neural Network and Hybrid Metaheuristic Feature Selection," in IEEE Access, vol. 8, pp. 189960-189973, 2020, doi: 10.1109/ACCESS.2020.3031914.
Z. Zhang, Q. Gao, L. Liu and Y. He, "A High-Quality Rice Leaf Disease Image Data Augmentation Method Based on a Dual GAN," in IEEE Access, vol. 11, pp. 21176-21191, 2023, doi: 10.1109/ACCESS.2023.3251098.
B. Liu, C. Tan, S. Li, J. He and H. Wang, "A Data Augmentation Method Based on Generative Adversarial Networks for Grape Leaf Disease Identification," in IEEE Access, vol. 8, pp. 102188-102198, 2020, doi: 10.1109/ACCESS.2020.2998839.
A. Ahmad, A. E. Gamal and D. Saraswat, "Toward Generalization of Deep Learning-Based Plant Disease Identification Under Controlled and Field Conditions," in IEEE Access, vol. 11, pp. 9042-9057, 2023, doi: 10.1109/ACCESS.2023.3240100.
S. Barburiceanu, S. Meza, B. Orza, R. Malutan and R. Terebes, "Convolutional Neural Networks for Texture Feature Extraction. Applications to Leaf Disease Classification in Precision Agriculture," in IEEE Access, vol. 9, pp. 160085-160103, 2021, doi: 10.1109/ACCESS.2021.3131002.
H. Yu et al., "Corn Leaf Diseases Diagnosis Based on K-Means Clustering and Deep Learning," in IEEE Access, vol. 9, pp. 143824-143835, 2021, doi: 10.1109/ACCESS.2021.3120379.
Q. Zeng, X. Ma, B. Cheng, E. Zhou and W. Pang, "GANs-Based Data Augmentation for Citrus Disease Severity Detection Using Deep Learning," in IEEE Access, vol. 8, pp. 172882-172891, 2020, doi: 10.1109/ACCESS.2020.3025196.
Y. Wu, X. Feng and G. Chen, "Plant Leaf Diseases Fine-Grained Categorization Using Convolutional Neural Networks," in IEEE Access, vol. 10, pp. 41087-41096, 2022, doi: 10.1109/ACCESS.2022.3167513.
S. Das, A. Biswas, V. C and P. Sinha, "Deep Learning Analysis of Rice Blast Disease Using Remote Sensing Images," in IEEE Geoscience and Remote Sensing Letters, vol. 20, pp. 1-5, 2023, Art no. 2500905, doi: 10.1109/LGRS.2023.3244324.
M. Kumar, A. Kumar and V. S. Palaparthy, "Soil Sensors-Based Prediction System for Plant Diseases Using Exploratory Data Analysis and Machine Learning," in IEEE Sensors Journal, vol. 21, no. 16, pp. 17455-17468, 15 Aug.15, 2021, doi: 10.1109/JSEN.2020.3046295.
R. Saini, K. S. Patle, A. Kumar, S. G. Surya and V. S. Palaparthy, "Attention-Based Multi-Input Multi-Output Neural Network for Plant Disease Prediction Using Multisensor System," in IEEE Sensors Journal, vol. 22, no. 24, pp. 24242-24252, 15 Dec.15, 2022, doi: 10.1109/JSEN.2022.3219601.
S. Ahmed, M. B. Hasan, T. Ahmed, M. R. K. Sony and M. H. Kabir, "Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease Classification," in IEEE Access, vol. 10, pp. 68868-68884, 2022, doi: 10.1109/ACCESS.2022.3187203.
C. Zhou, Z. Zhang, S. Zhou, J. Xing, Q. Wu and J. Song, "Grape Leaf Spot Identification Under Limited Samples by Fine Grained-GAN," in IEEE Access, vol. 9, pp. 100480-100489, 2021, doi: 10.1109/ACCESS.2021.3097050.
Liu, W. Min, S. Mei, L. Wang and S. Jiang, "Plant Disease Recognition: A Large-Scale Benchmark Dataset and a Visual Region and Loss Reweighting Approach," in IEEE Transactions on Image Processing, vol. 30, pp. 2003-2015, 2021, doi: 10.1109/TIP.2021.3049334.
How to Cite
Copyright (c) 2023 S. Jana, S. D. Thilagavathy, S. T. Shenbagavalli, G. Srividhya, . V. S. Gowtham Prasad, R. Hemavathy
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.