LeafNet: Using Convolutional Neural Network for Plant Leaf Detection

Authors

  • Saba Saeed Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
  • Sana Faiz Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan
  • Areej Fatemah Meghji Department of Software Engineering, Mehran University of Engineering and Technology, Jamshoro, Pakistan

DOI:

https://doi.org/10.21015/vtse.v11i2.1514

Abstract

Pakistan is home to thousands of plant species. As a result of pollution, natural disasters, and climate change, many of these species are at risk of extinction. Plant categorization and detection systems are designed to assist non-experts in automatically identifying plants based on their leaves to ensure their safety. The current study proposes a plant leaf detection system utilizing a Convolutional Neural Network architecture. Making use of the PlantVillage dataset, the proposed system can identify seven species of plants namely apple, cherry, tomato, potato, soybean, strawberry, and corn. Data augmentation strategies have been used to provide more training examples to get around the problem of bias and imbalanced data. The accuracy achieved on the training set was 98.87% which improved to 99.5% when using data augmentation. Apart from the monitoring of endangered species, the adoption of the proposed model can also aid the evaluation of weed management efforts and analysis of species distribution under climate change.

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Published

2023-06-17

How to Cite

Saeed, S., Faiz, S., & Meghji, A. F. (2023). LeafNet: Using Convolutional Neural Network for Plant Leaf Detection. VFAST Transactions on Software Engineering, 11(2), 50–57. https://doi.org/10.21015/vtse.v11i2.1514

Issue

Vol. 11 No. 2 (2023): April-June

Section

Articles

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