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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 8, 2020.
Abstract: Plant is exposed to many attacks from various micro-organism, bacterial disease and pests. The symptoms of the attacks are usually distinguished through the leaves, stem or fruit inspection. Disease that are commonly attack plants are Powdery Mildew and Leaf Blight and it may cause severe damaged if not controlled in early stages. Image processing has widely being used for identification, detection, grading and quality inspection in the agriculture field. Detection and identification disease of a plant is very important especially, in producing a high-quality fruit. Leaves of a plant can be used to determine the health status of that plant. The objective of this work is to develop a system that capable to detect and identify the type of disease based on Blobs Detection and Statistical Analysis. A total 45 sample leaves images from different colour and type were used and the accuracy is analysed. The Blobs Detection technique are used to detect the healthiness of plant leaves. While Statistical Analysis is used by calculating the Standard Deviation and Mean value to identify the type disease. Result is compared with manual inspection and it is found that the system has 86% in accuracy compared to manual detection process.
N. S. A. M Taujuddin, A.I.A Mazlan, R. Ibrahim, S. Sari, A. R. A Ghani, N. Senan and W.H.N.W Muda, “Detection of Plant Disease on Leaves using Blobs Detection and Statistical Analysis” International Journal of Advanced Computer Science and Applications(IJACSA), 11(8), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110852
@article{Taujuddin2020,
title = {Detection of Plant Disease on Leaves using Blobs Detection and Statistical Analysis},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110852},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110852},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {8},
author = {N. S. A. M Taujuddin and A.I.A Mazlan and R. Ibrahim and S. Sari and A. R. A Ghani and N. Senan and W.H.N.W Muda}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.