Improving the accuracy of real field pomegranate fruit diseases detection and visualisation using convolution neural networks and grad-CAM
by Vaishali Nirgude; Sheetal Rathi
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 15, No. 1/2, 2023

Abstract: Pomegranate (Punica granatum L) is one of the vital cash fruit crops of arid and semiarid regions in India. The occurrence of pests and diseases affects the development and quality of fruits. Our objective is to develop an automated pomegranate disease detection system on an actual field image dataset using convolution neural networks. The collected images are classified into six categories namely healthy, bacterial blight, anthracnose, fruit spot, fusarium wilt, and fruit borer. In this paper, we have measured the performance of CNN-based architectures VGG16, VGG19, InceptionV3, Resnet50, and Xception with hyperparameter tuning. The experimental results show that Resnet50 is a suitable model for our dataset with a disease detection accuracy of 98.55%. To deal with DL 'black box' problem, the gradient-weighted class activation mapping (Grad-CAM) model is integrated with ResNet50 to highlight the important regions on the fruits to locate accurate diseases and recommend appropriate disease treatment to farmers.

Online publication date: Fri, 28-Jul-2023

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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:

Username:        Password:          Forgotten your 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