Indian Journal of Science and Technology
Year: 2023, Volume: 16, Issue: 18, Pages: 1323-1331
Original Article
Prashant B Wakhare1*, S Neduncheliyan1
1Computer Science and Engineering, Bharath Institute of Higher Education and Research, Tamil Nadu, India
*Corresponding Author
Email: [email protected]
Received Date:02 April 2023, Accepted Date:20 April 2023, Published Date:05 May 2023
Objectives: To detect and identify diseases affecting pomegranate leaves using image processing and deep learning techniques. Method: A dataset of pomegranate leaf images was created with a total of 1844 images and split into 70% for training and 30% for testing. The model was trained using standard parameters like number of filters, activation functions and number of epochs and using Convolutional Neural Network algorithm for improved performance. Evaluation was conducted using standard metrics such as accuracy, precision, recall, and F1 score. Finding:The proposed work obtained the precision values for diseases Bacterial Blight, Fungal Diseases, Viral Diseases and Insect Damage as 98%, 98%, 98% and 97% respectively. Moreover, the classification accuracy obtained for diseases identification is 98.38%. Novelty:The proposed work uses private data set of diseased and healthy pomegranate leaves. Besides this the accuracy obtained is of the best class compared to the existing work in this domain.
Keywords: Pomegranate; Leaf Diseases; Image Processing; Deep Learning Techniques; Dataset Creation; Convolutional Neural Network
© 2023 Wakhare & Neduncheliyan. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)
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