Plant Disease Detection Using Image Processing for the Android Operating system


  • Muhammad Zunnurain Hussaina Bahria University Lahore Campus
  • Muhammad Zulkifl Hasan NCBAE Lahore, Pakistan
  • Zaka Ullah r LGU Lahore


Image Acquisition, Pre-Processing, Features extraction, Segmentation, SD (Standard Deviation), Mean, GLCM (Gray Level Co-Occurrence Matrix), K-Means, Skewness, PNN (probabilistic neural network), KNN (k-nearest neighbor classifier), SVM (support vector machine).


Agriculture is the most important in the development of the country and plays a vital role in
the economy. Image processing techniques are used for detecting the disease in plants. The
first symptoms of disease in plants are difficult to detect from the human eye at an early stage.
In this paper, the fungus disease is detected by using the image processing techniques. This
paper also focuses on detecting healthy or unhealthy plants on their first symptoms of the
disease with accuracy through the image processing technique. Disease detection involves
steps like image acquisition, image pre-processing, image segmentation, feature extraction,
and classification. This paper provides an android platform for detecting the fungus disease
for users. It will use the picture of the affected leaf of plants. Further, an algorithm will be
used for identification of the disease involve steps like loading the image, contrast
enhancement, segmentation, extracting of features. At the final step, the classification result
by using a support vector machine algorithm along with the necessary control measures will
be presented through the mobile application.



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How to Cite

Muhammad Zunnurain Hussaina, Muhammad Zulkifl Hasan, & Zaka Ullah. (2021). Plant Disease Detection Using Image Processing for the Android Operating system. University of Wah Journal of Computer Science, 3(1). Retrieved from