An Efficient Deep Neural Network for Disease Detection in Rice Plant Using XGBOOST Ensemble Learning Framework
Keywords:Rice Leaf, XGBOSST Ensemble learning, Disease detection, Feature Extraction
Rice disease has a substantial impact on agriculture production, and accurate rice disease diagnosis is more significant for farmers' economic development. Deep learning algorithms have made a massive impression in the context of agricultural disease identification in past few years. However, there are a number of detection techniques available, some of which may not be quite as effective as they might be. Timely detection and identification of specific disease help significantly in disease control and management. In this regard, deep learning-based convolution neural network model is developed to equip the diagnosis process for early detection. The proposed prototype is introduced by integrating XGBOOST ensemble learning model with Keras Inception ResNet V2 Framework for solving various tasks like classification of input images, object segmentation and image feature extraction. Initially, rice plant images undergo pre-processing stage for rotating, flipping, cropping and scaling to enhance image quality for the process of training and classification. The Adam optimizer is used to further optimize the proposed framework by making the learning and training process more efficient. The proposed model is applied to the augmented dataset and establishes a benchmark performance in terms of accuracy, precision, and recall. The findings of this investigation will help to increase the practice of deep learning technology in agriculture for earlier plant disease diagnosis and prevention.
Albert, B. A. (2020). Deep learning from limited training data: novel segmentation and ensemble algorithms applied to automatic melanoma diagnosis. IEEE Access 8, 31254–31269.
Nouby M. Ghazaly, M. M. A. . (2022). A Review on Engine Fault Diagnosis through Vibration Analysis . International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(2), 01–06. https://doi.org/10.17762/ijrmee.v9i2.364
Arnal Barbedo, J. G. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosyst. Eng. 180, 96–107.
Baresel, J. P., Rischbeck, P., Hu, Y., Kipp, S., Hu, Y., Barmeier, G., et al. (2017). Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat. Comput. Electron. Agric. 140, 25–33. doi: 10.1016/j.compag.2017.05.032
Chung, C.-L., Huang, K.-J., Chen, S.-Y., Lai, M.-H., Chen, Y.-C., and Kuo, Y.-F. (2016). Detecting bakanae disease in rice seedlings by machine vision. Computers and electronics in agriculture, 121:404–411
Goceri (2019b). Challenges and recent solutions for image segmentation in the era of deep learning. In 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA), pages 1–6
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
Rafeed Rahman, C., Saha Arko, P., Eunus Ali, M., Khan, M. A. I., Hasan Apon, S., Nowrin, F., and Wasif, A. (2018). Identification and recognition of rice diseases and pests using convolutional neural networks. arXiv, pages arXiv–1812.
S, R. D., L. . Shyamala, and S. . Saraswathi. “Adaptive Learning Based Whale Optimization and Convolutional Neural Network Algorithm for Distributed Denial of Service Attack Detection in Software Defined Network Environment”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 10, no. 6, June 2022, pp. 80-93, doi:10.17762/ijritcc.v10i6.5557.
G. Anthonys and N. Wickramarachchi, “An image recognition system for crop disease identification of paddy fields in Sri Lanka,” Int. Conf. on Industrial and Information Systems, IEEE, 2009, pp. 403-407.
S. Phadikar, J. Sil, and A. Das, “Classification of rice leaf diseases based on morphological changes,” Int. Journal of Information and Electronics Engineering, vol. 2, no. 3, pp. 460-463, 2012
Simonyan, K., Vedaldi, A., and Zisserman, A. (2013). Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034.
Garg, D. K. . (2022). Understanding the Purpose of Object Detection, Models to Detect Objects, Application Use and Benefits. International Journal on Future Revolution in Computer Science &Amp; Communication Engineering, 8(2), 01–04. https://doi.org/10.17762/ijfrcsce.v8i2.2066
Zeiler, M. D. and Fergus, R. (2014). Visualizing and understanding convolutional networks. In European conference on computer vision, pages 818–833. Springer
Q. H. Cap, H. Tani, H. Uga, S. Kagiwada and H. Iyatomi, "Super-Resolution for Practical Automated Plant Disease Diagnosis System," 2019 53rd Annual Conference on Information Sciences and Systems (CISS), Baltimore, MD, USA, 2019, pp. 1-6
Fuentes A., Im D.H., Yoon S., Park D.S. (2017) Spectral Analysis of CNN for Tomato Disease Identification. In: Rutkowski L., Korytkowski M., Scherer R., Tadeusiewicz R., Zadeh L., Zurada J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science, vol 10245. Springer
Agarwal, A. . (2022). Symmetric, e-Projective Topoi of Non-Solvable, Trivially Fourier Random Variables and Selberg’s Conjecture. International Journal on Recent Trends in Life Science and Mathematics, 9(1), 01–10. https://doi.org/10.17762/ijlsm.v9i1.136
H. F. Pardede, E. Suryawati, R. Sustika and V. Zilvan, "Unsupervised Convolutional Autoencoder-Based Feature Learning for Automatic Detection of Plant Diseases," 2018 International Conference on Computer, Control, Informatics and its Applications (IC3INA), pp. 158-162, 2018.
Ghosh M, Guha R, Singh PK, Bhateja V, Sarkar R (2019) A histogram based fuzzy ensemble technique for feature selection. Evol Intell 12(4):713–724
H. Wang, G. Li, Z. Ma, and X. Li, "Image recognition of plant diseases based on Backpropagation Networks," 5th Int. Congress on Image and Signal Processing (CISP), IEEE, 2012, pp. 894-900.
H. B. Prajapati, J. P. Shah, and V. K. Dabhi, (2017). “Detection and classification of rice plant diseases,” Intell. Decis. Technol., vol. 11, no. 3, pp. 357–373.
K. Archana and A. Sahayadhas, (2018). “Automatic rice leaf disease 892 segmentation using image processing techniques,” Int J Eng Technol, vol. 7, no. 893 3.27, pp. 182–185.
Sujatha, R., Chatterjee, J. M., Jhanjhi, N. and Brohi, S. N. (2021). Performance of deep learning vs machine learning in plant leaf disease detection, Microprocessors and Microsystems 80: 103615.
Nanehkaran, Y.A., Zhang, D., Chen, J. et al. Recognition of plant leaf diseases based on computer vision. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02505-x
Ahmed Cherif Megri, Sameer Hamoush, Ismail Zayd Megri, Yao Yu. (2021). Advanced Manufacturing Online STEM Education Pipeline for Early-College and High School Students. Journal of Online Engineering Education, 12(2), 01–06. Retrieved from http://onlineengineeringeducation.com/index.php/joee/article/view/47
Thomas, S., Kuska, M. T., Bohnenkamp, D., Brugger, A., Alisaac, E., Wahabzada, M., Mahlein, A.-K. (2017). Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. Journal of Plant Diseases and Protection, 125(1), 5–20. doi:10.1007/s41348-017-0124-6
D. Ashourloo, H. Aghighi, A. A. Matkan, M. R. Mobasheri and A. M. Rad, "An Investigation into Machine Learning Regression Techniques for the Leaf Rust Disease Detection Using Hyperspectral Measurement," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 9, pp. 4344-4351, 2016.
Raza, S.-A., Prince, G., Clarkson, J. P., Rajpoot, N. M., et al. (2015). Automatic detection of diseased tomato plants using thermal and stereo visible light images. PLoS ONE 10:e0123262. doi: 10.1371/journal.pone.0123262
Ali, Iftikhar; Cawkwell, Fiona; Dwyer, Edward; Green, Stuart (2016). Modeling Managed Grassland Biomass Estimation by Using Multitemporal Remote Sensing Data—A Machine Learning Approach. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, (), 1–16. doi:10.1109/JSTARS.2016.2561618
Su J, Liu C, Coombes M, Hu X, Wang C, Xu X, Li Q, Guo L, Chen WH. Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. 2018; Comput. Electron. Agric. 155:157-166
R. A. Kumar and V. S. Rajpurohit, “A novel approach for pomegranate image preprocessing using wavelet denoising,” in International Proceedings on Advances in Soft Computing, Intelligent Systems and Applications, pp. 123–134, Springer, 2018.
W. Jia, Y. Zheng, D. Zhao, X. Yin, X. Liu, and R. Du, “Preprocessing method of night vision image application in apple harvesting robot,” International Journal of Agricultural and Biological Engineering, vol. 11, no. 2, pp. 158–163, 2018.
C. Deisy and M. Francis, “Image segmentation for feature extraction: A study on disease diagnosis in agricultural plants,” in Feature Dimension Reduction for Content-Based Image Identification, pp. 232–257, IGI Global, 2018.
K. Vani, S. Poongodi, and B. Harikrishna, “K-means cluster based leaf disease identification in cotton plants.,” Indian Journal of Public Health Research & Development, vol. 9, no. 10, 2018.
J. D. Pujari, R. Yakkundimath, and A. S. Byadgi, “Automatic fungal disease detection based on wavelet feature extraction and pca analysis in commercial 73 crops,” International Journal of Image, Graphics and Signal Processing, vol. 6, no. 1, pp. 24–31, 2013.
A. T. Sapkal and U. V. Kulkarni, “Comparative study of leaf disease diagnosis system using texture features and deep learning features,” International Journal of Applied Engineering Research, vol. 13, no. 19, pp. 14334–14340, 2018.
Krishnaswamy Rangarajan Aravind & Purushothaman Raja (2020) Automated disease classification in (Selected) agricultural crops using transfer learning, Automatika, 61:2, 260-272, DOI: 10.1080/00051144.2020.1728911
Hassan, S.M.; Maji, A.K.; Jasi ´nski, M.; Leonowicz, Z.; Jasi ´nska, E. Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach. Electronics 2021, 10, 1388. https://doi.org/10.3390/ electronics10121388
N. Ashwin, U. K. Adusumilli, N. Kemparaju, and L. Kurra, "A machine learning approach to prediction of soybean disease," International Journal of Scientific Research in Science, Engineering and Technology, vol. 9, pp. 78-88, 2021
T. S. Xian and R. Ngadiran, "Plant diseases classification using machine learning," Journal of Physics: Conference Series, vol. 1962, p. 012024, 2021.
P. Bedi and P. Gole, "Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network," Artificial Intelligence in Agriculture, vol. 5, pp. 90-101, 2021
Anwar Abdullah Alatawi, Shahd Maadi Alomani, Najd Ibrahim Alhawiti, Muhammad Ayaz, Plant Disease Detection using AI based VGG-16 Model, International Journal of Advanced Computer Science and Applications, Vol. 13, No. 4, 2022
M. Lamba, Y. Gigras, and A. Dhull, "Classification of plant diseases using machine and deep learning," Open Computer Science, vol. 11, pp. 491-508, 2021.
Baes, A. M. M. ., Adoptante, A. J. M. ., Catilo, J. C. A. ., Lucero, P. K. L. ., Peralta, J. F. P., & de Ocampo, A. L. P. (2022). A Novel Screening Tool System for Depressive Disorders using Social Media and Artificial Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 10(1), 116–121. https://doi.org/10.18201/ijisae.2022.274
K. Kishore Kumar and E. Kannan, Detection of rice plant disease using AdaBoostSVM classifier, Agronomy journal, 2022, https://doi.org/10.1002/agj2.21070.
Ravikumar, S. and Kannan, E., 2021. Machine Learning Techniques for Identifying Fetal Risk During Pregnancy. International Journal of Image and Graphics, p.2250045.
Kannan, E., Ravikumar, S., Anitha, A., Kumar, S.A. and Vijayasarathy, M., 2021. Analyzing uncertainty in cardiotocogram data for the prediction of fetal risks based on machine learning techniques using rough set. Journal of Ambient Intelligence and Humanized Computing, pp.1-13.
Joy, P., Thanka, R. and Edwin, B., 2022. Smart Self-Pollination for Future Agricultural-A Computational Structure for Micro Air Vehicles with Man-Made and Artificial Intelligence. International Journal of Intelligent Systems and Applications in Engineering, 10(2), pp.170-174.
Cinar, I. and Koklu, M., 2019. Classification of rice varieties using artificial intelligence methods. International Journal of Intelligent Systems and Applications in Engineering, 7(3), pp.188-194.
How to Cite
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.