Deep learning for apple diseases: classification and identification Online publication date: Wed, 31-Mar-2021
by Asif Iqbal Khan; S.M.K. Quadri; Saba Banday
International Journal of Computational Intelligence Studies (IJCISTUDIES), Vol. 10, No. 1, 2021
Abstract: Diseases cause huge economic loss to the apple industry every year. Timely identification of these diseases is challenging for the farmers as the symptoms produced by different diseases may be similar and sometimes present simultaneously. This paper is an attempt to provide the timely and accurate identification of apple diseases from plant leaves. In this study, we propose a deep learning approach for identification and classification of apple diseases. First part of this study is dataset creation which includes data collection and labelling. Next, we train a convolutional neural network (CNN) model on the prepared dataset for automatic classification of apple diseases. CNNs are end-to-end learning algorithms which perform automatic feature extraction and learn complex features directly from raw images, making them suitable for a wide variety of computer vision tasks. The model parameters were initialised using transfer learning enabling the proposed model to achieve 97.18% accuracy on the prepared dataset.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computational Intelligence Studies (IJCISTUDIES):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook