Vol. 2024 (2024)
Articles

Enhancing Agriculture Crop Classification with Deep Learning

Yasmin Makki Mohialden
Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq.
Nadia Mahmood Hussien
Department of Computer Science, College of Science, Mustansiriyah University, Baghdad, Iraq.
Saba Abdulbaqi Salman
Department of Computer Science, College of Science, Aliraqia University, Baghdad, Iraq.
Ahmed Bahaaulddin A. Alwahhab
Department of Informatics, Technical College of management, Middle Technical University
Mumtaz Ali
Deakin-SWU Joint Research Centre on Big Data, School of Information Technology, Deakin University, Burwood 3125, VIC, Australia.

Published 2024-03-02

Keywords

  • Agriculture,
  • crop classification,
  • deep learning,
  • rice crop,
  • convolutional neural networks

How to Cite

Mohialden, Y. M., Hussien , N. M., Salman, S. A., Ahmed Bahaaulddin A. Alwahhab, & Mumtaz Ali. (2024). Enhancing Agriculture Crop Classification with Deep Learning. Babylonian Journal of Artificial Intelligence, 2024, 20–26. https://doi.org/10.58496/BJAI/2024/004

Abstract

To classify rice crops, the paper applies deep learning to agricultural crop images to classify rice crops. The collection includes images of wheat, rice, sugarcane, jute, and maize.

We improved variety by horizontally flipping, rotating, and shifting rice image data sets. A CNN structure classifies rice and non-rice crops.

The model has 100% accuracy on training and testing datasets; however, the classification report shows label imbalance problems for precision, recall, and F-score.

Deep learning can help classify crops as well as make decisions in agriculture based on research.

The study recommends more studies and improvements to enhance model performance and address dataset concerns. The research advances agricultural technology and emphasizes machine learning for crop management and production.

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