Comparison of deep learning-based models for detection of diseased trees using an image compression algorithm

Authors

DOI:

https://doi.org/10.15587/1729-4061.2024.314148

Keywords:

YOLO model, image compression, computer vision, forest management, deep learning

Abstract

The object of the research is the application of deep learning algorithms using an improved mathematical lossless image compression method for recognizing and identifying dead trees in aerospace images.

The main problem that has been solved is the archiving of images due to their large volume on disk and the possibility of their further processing by deep learning methods such as convolutional and capsule neural networks, which have shown high efficiency and accuracy in image recognition and classification tasks using the proposed new image compression method.

The article presents a comparative analysis of the performance of three YOLO (You Only Look Once) models with different types of architectures, such as YOLOv5, YOLOv7 and YOLOv8, to assess the effectiveness of their work for the task of recognizing aerospace tree images obtained from satellites, drones, and aircrafts.

Comprehensive analysis of YOLO models presents that model YOLO v8 turned out to be most effective with a positive accuracy of 88.2 %, a recall of 77.4 %, and a mAP50 score of 87.2 %. Moreover, the average detection time was only 0.052 seconds for each image, even though the model size remains very small – 21.5 MB. These results suggest a much better usage of time and precise identification of dead trees, and classified targets with high efficiency.

From the research, there is significant prospects of global forest management especially on forest reduction and protection of ecosystems through accurate assessment on the health of forestry. The proposed approach is universal and can be used in real life conditions, providing a good compromise of the speed, accuracy and resources required for forest monitoring and management

Author Biographies

Assiya Sarinova, Astana IT University

PhD

Department of Intelligent Systems and Cybersecurity

Leila Rzayeva, Astana IT University

PhD

Department of Intelligent Systems and Cybersecurity

Gulnara Abitova, Astana IT University

PhD

Department of Intelligent Systems and Cybersecurity

Alimzhan Yessenov, Astana IT University

MSc

Department of Intelligent Systems and Cybersecurity

Ansar Sansyzbayev, Astana IT University

MSc

Department of Intelligent Systems and Cybersecurity

Yerassyl Omirtay, Astana IT University

MSc

Department of Intelligent Systems and Cybersecurity

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Comparison of deep learning-based models for detection of diseased trees using an image compression algorithm

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Published

2024-10-30

How to Cite

Sarinova, A., Rzayeva, L., Abitova, G., Yessenov, A., Sansyzbayev, A., & Omirtay, Y. (2024). Comparison of deep learning-based models for detection of diseased trees using an image compression algorithm. Eastern-European Journal of Enterprise Technologies, 5(2 (131), 24–35. https://doi.org/10.15587/1729-4061.2024.314148

Issue

Vol. 5 No. 2 (131) (2024): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2024 Assiya Sarinova, Leila Rzayeva, Gulnara Abitova, Alimzhan Yessenov, Ansar Sansyzbayev, Yerassyl Omirtay

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