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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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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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.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128591602&origin=inward; http://dx.doi.org/10.1007/978-3-031-03918-8_9; https://link.springer.com/10.1007/978-3-031-03918-8_9; https://dx.doi.org/10.1007/978-3-031-03918-8_9; https://link.springer.com/chapter/10.1007/978-3-031-03918-8_9
Springer Science and Business Media LLC
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