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A Hybrid Deep Learning Approach to Classify the Plant Leaf Species

Javed Rashid1,2, Imran Khan1, Irshad Ahmed Abbasi3, Muhammad Rizwan Saeed4, Mubbashar Saddique5,*, Mohamed Abbas6,7

1 Department of CS&SE, Islamic International University, Islamabad, 44000, Pakistan
2 Department of IT Services, University of Okara, Okara, 56310, Pakistan
3 Faculty of Science and Arts Belqarn, University of Bisha, Sabtul Alaya, 61985, Saudi Arabia
4 Department of CS, University of Okara, Okara, 56310, Pakistan
5 Department of Computer Science & Engineering, University of Engineering & Technology Lahore, Narowal, Campus, Narowal, 51601, Pakistan
6 Electrical Engineering Department, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia
7 Research Center for Advanced Materials Sciences (RCAMS), King Khalid University, Abha, Saudi Arabia

* Corresponding Author: Mubbashar Saddique. Email: email

(This article belongs to the Special Issue: Recent Advances in Deep Learning and Saliency Methods for Agriculture)

Computers, Materials & Continua 2023, 76(3), 3897-3920.


Many plant species have a startling degree of morphological similarity, making it difficult to split and categorize them reliably. Unknown plant species can be challenging to classify and segment using deep learning. While using deep learning architectures has helped improve classification accuracy, the resulting models often need to be more flexible and require a large dataset to train. For the sake of taxonomy, this research proposes a hybrid method for categorizing guava, potato, and java plum leaves. Two new approaches are used to form the hybrid model suggested here. The guava, potato, and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture. As a second model, we use a Plant Species Detection Stacking Ensemble Deep Learning Model (PSD-SE-DLM) to identify potatoes, java plums, and guava. The proposed models were trained using data collected in Punjab, Pakistan, consisting of images of healthy and sick leaves from guava, java plum, and potatoes. These datasets are known as PLSD and PLSSD. Accuracy levels of 99.84% and 96.38% were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models, respectively.


Cite This Article

APA Style
Rashid, J., Khan, I., Abbasi, I.A., Saeed, M.R., Saddique, M. et al. (2023). A hybrid deep learning approach to classify the plant leaf species. Computers, Materials & Continua, 76(3), 3897-3920.
Vancouver Style
Rashid J, Khan I, Abbasi IA, Saeed MR, Saddique M, Abbas M. A hybrid deep learning approach to classify the plant leaf species. Comput Mater Contin. 2023;76(3):3897-3920
IEEE Style
J. Rashid, I. Khan, I.A. Abbasi, M.R. Saeed, M. Saddique, and M. Abbas "A Hybrid Deep Learning Approach to Classify the Plant Leaf Species," Comput. Mater. Contin., vol. 76, no. 3, pp. 3897-3920. 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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