International Journal of Education and Management Engineering (IJEME)
IJEME Vol. 13, No. 4, 8 Aug. 2023
Cover page and Table of Contents: PDF (size: 886KB)
Potato Leaf Disease Detection Using Image Processing
Full Text (PDF, 886KB), PP.9-18
Views: 0 Downloads: 0
Author(s)
Md. Abu Jubaer
1,*
Md. Nabobi Hasan
1
Mufrad Mustavi
1
Md. Tanvir Shahriar
1
Tanvir Ahmed
1
Index Terms
Potato disease detection, Image processing, Data augmentation, Image normalization, One hot encoding
Abstract
The economics of a nation is significantly influenced by agricultural productivity. Finding plant leaf disease is crucial since it significantly reduces agricultural productivity. Traditional detection methods like observing with the naked eye can lead to time-consuming and less accurate results. Farmers can’t always tell the difference between leaf diseases because sometimes they look the same. That’s why researchers have started using automation techniques to accurately detect the main diseases and their symptoms. This research proposed potato leaf disease detection using an image processing technique where the dataset was obtained online. In the proposed method, several image pre-processing techniques are used including data augmentation, gaussian smoothing, image normalization, dimensionality reduction and one hot encoding. CNN, KNN and SVC were used as classifiers. CNN gives the best result with an overall accuracy of 97%. Previous works with different classifiers had several limitations and using CNN the researchers didn’t get satisfying result. For this research a new hybrid model is introduced which can utilize the best of CNN classifier and it will be much more reliable and effective.
Cite This Paper
Md. Abu Jubaer, Md. Nabobi Hasan, Mufrad Mustavi, Md. Tanvir Shahriar, Tanvir Ahmed, " Potato Leaf Disease Detection Using Image Processing", International Journal of Education and Management Engineering (IJEME), Vol.13, No.4, pp. 9-18, 2023. DOI:10.5815/ijeme.2023.04.02
Reference
[1]C. Knaak, J. von Eßen, M. Kröger, F. Schulze, P. Abels, and A. Gillner, “A Spatio-Temporal Ensemble Deep Learning Architecture for Real-Time Defect Detection during Laser Welding on Low Power Embedded Computing Boards,” Sensors, vol. 21, no. 12, p. 4205, Jun. 2021, doi: 10.3390/s21124205.
[2]Y. M. Oo and N. C. Htun, “Plant Leaf Disease Detection and Classification using Image Processing,” International Journal of Research and Engineering, vol. 5, no. 9, pp. 516–523, Nov. 2018, doi: 10.21276/ijre.2018.5.9.4.
[3]M. A. Jasim and J. M. AL-Tuwaijari, “Plant Leaf Diseases Detection and Classification Using Image Processing and Deep Learning Techniques,” in 2020 International Conference on Computer Science and Software Engineering (CSASE), Apr. 2020, pp. 259–265. doi: 10.1109/CSASE48920.2020.9142097.
[4]G. Shrivastava and H. Patidar, “Rice Plant Disease Identification Decision Support Model Using Machine Learning,” Ictact Journal On Soft Computing, p. 3, 2022, doi: 10.21917/ijsc.2022.0365.
[5]E. A. Abusham, “Image Processing Technique for the Detection of Alberseem Leaves Diseases Based on Soft Computing,” Artificial Intelligence & Robotics Development Journal, pp. 103–115, Jun. 2021, doi: 10.52098/airdj.202127.
[6]Md. A. Iqbal and K. H. Talukder, “Detection of Potato Disease Using Image Segmentation and Machine Learning,” in 2020 International Conference on Wireless Communications Signal Processing and Networking (WiSPNET), Aug. 2020, pp. 43–47. doi: 10.1109/WiSPNET48689.2020.9198563.
[7]C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J Big Data, vol. 6, no. 1, p. 60, Dec. 2019, doi: 10.1186/s40537-019-0197-0.
[8]“Radial basis function kernel - Wikipedia.” https://en.wikipedia.org/wiki/Radial_basis_function_kernel (accessed Oct. 23, 2022).
[9]U. O. Lateef, M. Mohamad, K. Omar and R. C. Muniyandi, “Gaussian smoothing and modified histogram normalization methods to improve neural-biomarker interpretations for dyslexia classification mechanism,” PLOS One, 2021.
[10]X. Zhang, Y. Sugano and A. Bulling, "Revisiting Data Normalization for Appearance-Based Gaze Estimation," Association for Computing Machinery, pp. 1-9, 2018.
[11]L. Jie, C. Jiahao, Z. Xueqin, Z. Yue and L. Jiajun, "One-hot encoding and convolutional neural network based anomaly detection," Journal of Tsinghua University (Science and Technology), vol. 59, no. 7, pp. 523-529, 2019.
[12]B. S. K. Keerthi Vasan, "Dimensionality reduction using Principal Component Analysis for network intrusion detection," ELSEVIER, vol. 8, pp. 510-512, 2016.
[13]K. Sasan , M. A. Shahidan , A. M. Azizah , Z. Mazdak and H. Alireza , "An Overview of Principal Component Analysis," Journal of Signal and Information Processing, vol. 4, pp. 173-175, 2013.
[14]Y. Shuai, Y. Zheng and H. Huang, "Hybrid Software Obsolescence Evaluation Model Based on PCA-SVM-GridSearchCV," in IEEE, Beijing, 2018.
[15]D. Kothari , H. Mishra, V. Pandey, M. Gharat and R. Thakur, "Potato Leaf Disease Detection using Deep Learning," International Journal Of Engineering Research & Technology, vol. 11, no. 11, 2022.