Plant leaf disease detection using CNN with transfer learning and XGBoost Online publication date: Mon, 16-Jan-2023
by Divakar Yadav; Aarushi Gupta; Arti Jain; Arun Kumar Yadav
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 14, No. 3, 2022
Abstract: The plant leaf disease identification at an early stage is an important step to prevent economic and agricultural losses. It is a challenging task because infected areas are difficult to extract from different images of leaves. Here, the plant leaf disease detection method is proposed using a convolutional neural network that combines knowledge learned from the PlantVillage dataset using ResNet50 and XGBoost classifier, termed as ConRXG. The ResNet50 serves as a pre-trained model towards feature extraction for transfer learning with batch normalisation to prevent overfitting. The ReLu activation function and Adam optimiser are used to improve the accuracy of the model. This method demonstrates robustness while identifying the plant leaf diseases in the chosen dataset. ConRXG has the highest training accuracy (0.9865), validation accuracy (0.9730), recall (0.9950), and F1-score (0.8971) among all the existing baseline models.
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 Data Analysis Techniques and Strategies (IJDATS):
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