• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 18, Pages: 1323-1331

Original Article

Using Image Processing and Deep Learning Techniques Detect and Identify Pomegranate Leaf Diseases

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


  1. Chakali R. Effective pomegranate plant leaf disease detection using deep learning. International Journal of Circuit, Computing and Networking. 2020;1(2):8–10. Available from: https://www.computersciencejournals.com/ijccn/article/13/1-1-14-682.pdf
  2. Lamba M, Gigras Y, Dhull A. Classification of plant diseases using machine and deep learning. Open Computer Science. 2021;11(1):491–508. Available from: https://doi.org/10.1515/comp-2020-0122
  3. Pawar S. Manoj Kharde Deep Learning-based Disease Detection using Pomegranate Leaf Image. Smart Technologies, Communication and Robotics. 2022. Available from: https://doi.org/10.1109/STCR55312.2022.10009185
  4. Sreekanth GR, Suganthe R. Automatic Detection of Tea Leaf Diseases using Deep Convolution Neural Network. International Conference on Computer Communication and Informatics (ICCCI). 2021 . Available from: https://doi.org/10.1109/ICCCI50826.2021.9402225
  5. Kuswidiyanto LW, Noh HH, Han X. Plant Disease Diagnosis Using Deep Learning Based on Aerial Hyperspectral Images: A Review. Remote Sensing. 2022;14(23):6031. Available from: https://doi.org/10.3390/rs14236031
  6. Wakhare PB, Neduncheliyan S, Thakur KR. Study of Disease Identification in Pomegranate Using Leaf Detection Technique. 2022 International Conference on Emerging Smart Computing and Informatics (ESCI). 2022;p. 1–6. Available from: https://doi.org/10.1109/ESCI53509.2022.9758262
  7. Wu Y. Identification of Maize Leaf Diseases based on Convolutional Neural Network. Journal of Physics: Conference Series. 2021;1748(3):032004. Available from: https://doi.org/10.1088/1742-6596/1748/3/032004
  8. Arjaria S, Gupta &S. ToLeD: Tomato Leaf Disease Detection using Convolution Neural Network. Procedia Computer Science. 2020;167. Available from: https://doi.org/10.1016/j.procs.2020.03.225


© 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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