Development of a weed detection system using machine learning and neural network algorithms

Authors

DOI:

https://doi.org/10.15587/1729-4061.2021.246706

Keywords:

agriculture, weeds, machine learning, YOLOv5, segmentation, Otsu's method, classification, algorithm evaluation

Abstract

The detection of weeds at the stages of cultivation is very important for detecting and preventing plant diseases and eliminating significant crop losses, and traditional methods of performing this process require large costs and human resources, in addition to exposing workers to the risk of contamination with harmful chemicals. To solve the above tasks, also in order to save herbicides and pesticides, to obtain environmentally friendly products, a program for detecting agricultural pests using the classical K-Nearest Neighbors, Random Forest and Decision Tree algorithms, as well as YOLOv5 neural network, is proposed. After analyzing the geographical areas of the country, from the images of the collected weeds, a proprietary database with more than 1000 images for each class was formed. A brief review of the researchers' scientific papers describing the methods they developed for identifying, classifying and discriminating weeds based on machine learning algorithms, convolutional neural networks and deep learning algorithms is given. As a result of the research, a weed detection system based on the YOLOv5 architecture was developed and quality estimates of the above algorithms were obtained. According to the results of the assessment, the accuracy of weed detection by the K-Nearest Neighbors, Random Forest and Decision Tree classifiers was 83.3 %, 87.5 %, and 80 %. Due to the fact that the images of weeds of each species differ in resolution and level of illumination, the results of the neural network have corresponding indicators in the intervals of 0.82–0.92 for each class. Quantitative results obtained on real data demonstrate that the proposed approach can provide good results in classifying low-resolution images of weeds.

Supporting Agency

  • This work was supported by a grant from the Ministry of Education and Science of the Republic of Kazakhstan within the framework of project No. AP08857573

Author Biographies

Baydaulet Urmashev, Al-Farabi Kazakh National University

Professor

Department of Computer Science

Zholdas Buribayev, Al-Farabi Kazakh National University

Master

Department of Computer Science

Zhazira Amirgaliyeva, Institute of Information and Computational Technologies

Doctor of Technical Sciences, Professor

Laboratory of Artificial Intelligence and Robotics

Aisulu Ataniyazova, Al-Farabi Kazakh National University

Masters Student

Department of Computer Science

Mukhtar Zhassuzak, Al-Farabi Kazakh National University

PhD Student

Department of Computer Science

Amir Turegali, Al-Farabi Kazakh National University

Bachelor Student

Department of Computer Science

References

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Published

2021-12-29

How to Cite

Urmashev, B., Buribayev, Z., Amirgaliyeva, Z., Ataniyazova, A., Zhassuzak, M., & Turegali, A. (2021). Development of a weed detection system using machine learning and neural network algorithms. Eastern-European Journal of Enterprise Technologies, 6(2 (114), 70–85. https://doi.org/10.15587/1729-4061.2021.246706

Issue

Vol. 6 No. 2 (114) (2021): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2021 Baydaulet Urmashev, Zholdas Buribayev, Zhazira Amirgaliyeva, Aisulu Ataniyazova, Mukhtar Zhassuzak, Amir Turegali

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