Android Application of Leaf Identification System

Authors

  • Hazwan Hakim Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia
  • Sharifa Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia
  • Dr Kadir Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia
  • Umi Fadlilah Teknik Elektro, Fakultas Teknik, Kampus 2, Universitas Muhammadiyah Surakarta

DOI:

https://doi.org/10.3991/ijim.v16i15.31881

Keywords:

leaf identification, CNN, Android application, TensorFlow Lite

Abstract


Leaf identification image is consistently a difficult task when using computer vision. The convolutional component extraction methods on images have their impediment and limitation, such as low accuracy, are not adaptable and less promising when converted to genuine application. The reasons are the lack of dataset needed to build a recognition model. Likewise, using the computer as a tool is bothering as it restricts the task in the research lab only. Convolutional Neural Network (CNN) shows a great solution for the computer version. Subsequently, this project utilizes the CNN’s properties to solve the image classification task, and the CNN model chosen is run in Phyton coding in TensorFlow Lite. It is similar to TensorFlow’s running code, but this project focused on building an Android application. It can perform faster and produce high accuracy results. There are four types of leaves involved in this project: betik, kari, pudina, and cengal. As a result, the model could reach around 99% accuracy with a 0.176 error rate. Ultimately, an Android application called Leaf identification is created. The model is sent and integrated into the apps that work with a concentrated information base to help put away and deal with the pictures. Hence, an Android leaf image identifier using CNN is proposed to solve the stated problem and is believed to contribute to education and research.

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Published

2022-08-17

How to Cite

Minhat, M. H. H., Saon, S., Mahamad, A. K., & Umi Fadlillah. (2022). Android Application of Leaf Identification System. International Journal of Interactive Mobile Technologies (iJIM), 16(15), pp. 62–77. https://doi.org/10.3991/ijim.v16i15.31881

Issue

Section

Papers