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Deep Learning-Based Apple Leaves Disease Identification Approach with Imbalanced Data

Lecture Notes on Data Engineering and Communications Technologies, ISSN: 2367-4520, Vol: 113, Page: 89-98
2022
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Book Chapter Description

Plant diseases pose a significant threat to global food security. Rapid identification of infected plants can significantly impact the overall health of the plant crops and reduce the loss caused by infection spread. Deep learning technologies have been widely used to automate the process of plant disease detection from digital images and accurately identify infected plants promptly. This paper develops a hybrid model by utilizing deep neural networks and support vector machines to classify four classes of apple leaves, namely healthy, rust, scab, and multiple diseases, from digital images with an accuracy of 95.36%. The datasets used in this paper suffered from a class imbalance in its class representation; hence the random oversampling technique has been used to increase the number of samples in the minority class.

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