Flower Disease Diagnosis ModelUsing Deep Convolutional Neural Network

Bisrat Getnet; Desalegn Ashebir; Birhanu Gardie

doi:10.17485/IJST/v16i27.1109

Article

Flower Disease Diagnosis ModelUsing Deep Convolutional Neural Network

VIEWS 578
PDF 126

Indian Journal of Science and Technology

DOI: 10.17485/IJST/v16i27.1109

Year: 2022, Volume: 16, Issue: 27, Pages: 2047-2057

Original Article

Flower Disease Diagnosis ModelUsing Deep Convolutional Neural Network

Bisrat Getnet^1*, Desalegn Ashebir², Birhanu Gardie¹

¹Department of Information Technology, School of Computing & Informatics, Mizan-Tepi University, Ethiopia
²Department of Software Engineering, School of Computing & Informatics, Mizan-Tepi University, Ethiopia

*Corresponding Author
Email: [email protected]

Received Date:23 May 2022, Accepted Date:14 November 2022, Published Date:18 July 2022

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: Design a convolutional neural network model for flower disease detection that can recognize and categorize flower diseases according to their disease types. Method: A 10-way SoftMax classification system is used to group the majority of diseases that affect flowers into distinct disease classes (normal, powdery mildew, rose aphid, botrytis blight, downey mildew, red spider mites, Japanese beetles, rose resettle, gray mold, and black spot). A total of 4200 images—70% for training, 15% for validation, and 15% for testing—were used to train and validate the model’s performance. From the Tana Flora flower cultivation center in Bahir Dar Amhara, Ethiopia, we obtained a dataset. The proposed model was trained by generating additional images using image augmentation techniques to overcome over fitting problem. Findings: Our flower disease identification model produced cutting-edge results with a test accuracy of 94.67%. When used on the same dataset, state-of-the-art models like Alex Net’s test accuracy was 85.6%, Google Net’s test accuracy was 90.98, and VGG19’s test accuracy was 89.3%. Our flower disease detection model has been shown to be faster to train and has a smaller model size thanks to the median filter that we used to improve the quality of the photos and a novel segmentation method that fit our dataset. Novelty: We proposed a new CNN architecture and new segmentation algorithm to identify flower disease. Through our experiments, we have shown median filtering, and ways of segmentation improve the performance of our model.

Keywords: Deep learning; Feature Learning; Segmentation; Flower Disease; Median Filtering

References

Sustainability to Ethiopia’s Growing Floriculture Industry _ SourceTrace Systems_1. Available from: https://www.sourcetrace.com/blog/bringing-in-sustainability-to-ethiopia-booming-floriculture-industry/
Ethiopia Flowers _ Exporter & Supplier - Good Prices_best. Available from: https://www.selinawamucii.com/produce/flowers/ethiopia-flowers/
The Ethiopian flower industry has shown remarkable growth over the past years. Available from: https://www.floraldaily.com/article/9319845/the-ethiopian-flower-industry-has-shown-remarkable-growth-over-the-past-years/
Tigistu G, Assabie Y. Automatic identification of flower diseases using artificial neural networks. AFRICON 2015. 2015. Available from: https://doi.org/10.1109/AFRCON.2015.7332020
Jain V, Yadav A. Analysis of Performance of Machine Learning. 2019 IEEE International Conference on E-health Networking. 2021;p. 706–709. Available from: https://doi.org/10.1109/HealthCom46333.2019.9009442
Ganore KA, Tigistu G. Ethiopian Enset Diseases Diagnosis Model Using Image Processing and Machine Learning Techniques. International Journal of Intelligent Information Systems. 2020;9(1):1–5. Available from: https://doi.org/10.11648/j.ijiis.20200901.11
Iqbal MA, Talukder KH. Detection of Potato Disease Using Image Segmentation and Machine Learning. 2020 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET). 2020;p. 43–47. Available from: https://doi.org/10.1109/WiSPNET48689.2020.9198563
Shrestha G, Deepsikha, Das M, Dey N. Plant Disease Detection Using CNN. 2020 IEEE Applied Signal Processing Conference (ASPCON). 2020;12:109–113. Available from: https://doi.org/10.1109/ASPCON49795.2020.9276722
Yuan Y, Chen L, Wu H, Li L. Advanced agricultural disease image recognition technologies: A review. Information Processing in Agriculture. 2022;9(1):48–59. Available from: https://doi.org/10.1016/j.inpa.2021.01.003
Thaiyalnayaki K, Joseph C. Classification of plant disease using SVM and deep learning. Materials Today: Proceedings. 2021;47:468–470. Available from: https://doi.org/10.1016/j.matpr.2021.05.029
Wagle SA, Harikrishnan R, Ali SHM, Faseehuddin M. Classification of Plant Leaves Using New Compact Convolutional Neural Network Models. Plants. 2022;11(1):24. Available from: https://doi.org/10.3390/plants11010024
Rao DS. Plant disease classification using deep bilinear cnn. Intelligent Automation and Soft Computing. 2022;31(1):161–176. Available from: http://dx.doi.org/10.5373/JARDCS/V12SP1/20201058
Panchal P, Raman VC, Mantri S. Plant Diseases Detection and Classification using Machine Learning Models. 2019 4th International Conference on Computational Systems and Information Technology for Sustainable Solution (CSITSS). 2019;4:1–6. Available from: https://doi.org/10.1109/CSITSS47250.2019.9031029
Orbien C. Identification of Carabao Mango Leaf Disease using Convolutional Neural Network. Journal of Advanced Research in Dynamical and Control Systems. 2020;12:152–158. Available from: http://dx.doi.org/10.5373/JARDCS/V12SP1/20201058
Negi A, Kumar K, Chauhan P. Deep Neural Network‐Based Multi‐Class Image Classification for Plant Diseases. Agricultural Informatics. 2021;p. 117–129. Available from: https://doi.org/10.1002/9781119769231.ch6
Rahman CR, Arko PS, Ali ME, Khan MAI, Apon SH, Nowrin F, et al. Identification and recognition of rice diseases and pests using convolutional neural networks. Biosystems Engineering. 2020;194:112–120. Available from: https://doi.org/10.48550/arXiv.1812.01043
Shreya K, Singh M, Kuma K. Prediction of Liver Disease Using Grouping of Machine Learning Classifiers. Materials Today: Proceedings. 2021. Available from: https:// DOI: 10.1007/978-3-030-67187-7_35
Dabral I, Singh M, Kumar K. Cancer Detection Using Convolutional Neural Network. Conference Proceedings of ICDLAIR2019. 2021;p. 290–298. Available from: http://dx.doi.org/10.1007/978-3-030-67187-7_30
Kumar K, Kumar M, Samsonu C, Krishna KV. Role of convolutional neural networks for any real time image classification, recognition and analysis. Materials Today: Proceedings. 2021. Available from: https://doi.org/10.1016/j.matpr.2021.02.186

Copyright

© 2023 Getnet et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)