Apple Disease Detection and Classification using  Random Forest (One-vs-All)

Zengming Wen; Hong Lan; Muhammad Asim Khan

doi:10.4108/airo.8041

Authors

Zengming Wen Jiangxi University of Science and Technology
Hong Lan Jiangxi University of Science and Technology
Muhammad Asim Khan Jiangxi University of Science and Technology https://orcid.org/0009-0006-5945-8472

DOI:

https://doi.org/10.4108/airo.8041

Keywords:

Fruit diseases detection, digital image processing, GLCM feature extraction, LDA, random forest

Abstract

Fruit diseases detection and recognition are a common problem worldwide. Fruit Disease detection is a hot topic among researchers and is very complex due to structure and color similarity factors. In this research we proposed a new model to detect and classify the apple disease with the help of digital image processing and machine learning. First, image processing techniques were applied to enhance the image contrast and remove the noise, which helped to segment the region of interest accurately also help to extract feature without garbage data. Then K-means clustering technique with fuzzy C-mean method was implemented to segment the images. GLCM feature extraction was used after the segmentation. Real images of apple disease with multi-disease regions were used in the research method. These features were preprocessed with methods like LDA. K-Fold cross validation was used for training and testing, with combination of random forest machine learning method. The result showed high accuracy with comparison of existing techniques.

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References

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