Identification of weeds in fields based on computer vision technology

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.284600

Keywords:

computer vision, image segmentation, neural network model, pattern recognition algorithms

Abstract

The problem of multiple zones in computer vision, including pattern recognition in the agricultural sector, occupies a special place in the field of artificial intelligence in the modern aspect.

The object of the study is the recognition of weeds based on deep learning and computer vision. The subject of the study is the effective use of neural network models in training, involving classification and processing using datasets of plants and weeds. The relevance of the study lies in the demand of the modern world in the use of new information technologies in industrial agriculture, which contributes to improving the efficiency of agro-industrial complexes. The interest of private agricultural enterprises and the state is caused by an increase in the yield of agricultural products. To recognize weeds, machine learning methods, in particular neural networks, were used. The process of weed recognition is described using the Mark model, as a result of processing 1,562 pictures, segmented images are obtained. Due to the annual increase in weeds on the territory of Kazakhstan and in the course of solving these problems, a new plant recognition code was developed and written in the scanner software module. The scanner, in turn, provides automatic detection of weeds. Based on the results of a trained neural network based on the MaskRCNN neural network model written in the scanner software module meeting new time standards, the automated plant scanning and recognition system was improved. The weed was recognized in an average of 0.2 seconds with an accuracy of 89 %, while the additional human factor was completely removed. The use of new technology helps to control weeds and contributes to solving the problem of controlling them

Author Biographies

Mira Kaldarova, S. Seifullin Kazakh Agro Technical Research University

Doctoral Student

Department of Computer Engineering and Software

Akerke Аkanova, S. Seifullin Kazakh Agro Technical Research University

PhD, Senior Lecture

Department of Computer Engineering and Software

Aizhan Nazyrova, Astana International University

Senior Lecture

Higher School of Information Technology and Engineering

Assel Mukanova, Astana International University

PhD, Associate Professor, Dean

Higher School of Information Technology and Engineering

Assemgul Tynykulova, S. Seifullin Kazakh Agro Technical Research University

Senior Lecture

Department of Computer Engineering and Software

References

  1. Patrício, D. I., Rieder, R. (2018). Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review. Computers and Electronics in Agriculture, 153, 69–81. doi: https://doi.org/10.1016/j.compag.2018.08.001
  2. Gomes, J. F. S., Leta, F. R. (2012). Applications of computer vision techniques in the agriculture and food industry: a review. European Food Research and Technology, 235 (6), 989–1000. doi: https://doi.org/10.1007/s00217-012-1844-2
  3. 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. doi: https://doi.org/10.15587/1729-4061.2021.246706
  4. Sunil, G. C., Zhang, Y., Koparan, C., Ahmed, M. R., Howatt, K., Sun, X. (2022). Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions. Journal of Agriculture and Food Research, 9, 100325. doi: https://doi.org/10.1016/j.jafr.2022.100325
  5. Tian, H., Wang, T., Liu, Y., Qiao, X., Li, Y. (2020). Computer vision technology in agricultural automation – A review. Information Processing in Agriculture, 7 (1), 1–19. doi: https://doi.org/10.1016/j.inpa.2019.09.006
  6. Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., Ahmad, F. (2020). Network intrusion detection system: A systematic study of machine learning and deep learning approaches. Transactions on Emerging Telecommunications Technologies, 32 (1). doi: https://doi.org/10.1002/ett.4150
  7. Li, Y., Randall, C. J., Woesik, R. van, Ribeiro, E. (2019). Underwater video mosaicing using topology and superpixel-based pairwise stitching. Expert Systems with Applications, 119, 171–183. doi: https://doi.org/10.1016/j.eswa.2018.10.041
  8. Sivarajan, S., Maharlooei, M., Bajwa, S. G., Nowatzki, J. (2018). Impact of soil compaction due to wheel traffic on corn and soybean growth, development and yield. Soil and Tillage Research, 175, 234–243. doi: https://doi.org/10.1016/j.still.2017.09.001
  9. Liu, H., Lee, S.-H., Chahl, J. S. (2016). A review of recent sensing technologies to detect invertebrates on crops. Precision Agriculture, 18 (4), 635–666. doi: https://doi.org/10.1007/s11119-016-9473-6
  10. Sabzi, S., Abbaspour-Gilandeh, Y., García-Mateos, G. (2018). A fast and accurate expert system for weed identification in potato crops using metaheuristic algorithms. Computers in Industry, 98, 80–89. doi: https://doi.org/10.1016/j.compind.2018.03.001
  11. Toseef, M., Khan, M. J. (2018). An intelligent mobile application for diagnosis of crop diseases in Pakistan using fuzzy inference system. Computers and Electronics in Agriculture, 153, 1–11. doi: https://doi.org/10.1016/j.compag.2018.07.034
  12. Magic, M., Magic, J. (2019). Image Classification Using Python and Techniques of Computer Vision and Machine Learning. Independently published, 114.
  13. Huang, Y., Jiang, L., Han, T., Xu, S., Liu, Y., Fu, J. (2022). High-Accuracy Insulator Defect Detection for Overhead Transmission Lines Based on Improved YOLOv5. Applied Sciences, 12 (24), 12682. doi: https://doi.org/10.3390/app122412682
  14. Kubo, S., Yamane, T., Chun, P. (2022). Study on Accuracy Improvement of Slope Failure Region Detection Using Mask R-CNN with Augmentation Method. Sensors, 22 (17), 6412. doi: https://doi.org/10.3390/s22176412
  15. Osorio, K., Puerto, A., Pedraza, C., Jamaica, D., Rodríguez, L. (2020). A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images. AgriEngineering, 2 (3), 471–488. doi: https://doi.org/10.3390/agriengineering2030032
  16. Almodaresi, S. A., Mohammadrezaei, M., Dolatabadi, M., Nateghi, M. R. (2019). Qualitative Analysis of Groundwater Quality Indicators Based on Schuler and Wilcox Diagrams: IDW and Kriging Models. Journal of Environmental Health and Sustainable Development. doi: https://doi.org/10.18502/jehsd.v4i4.2023
  17. Tseng, H.-H., Yang, M.-D., Saminathan, R., Hsu, Y.-C., Yang, C.-Y., Wu, D.-H. (2022). Rice Seedling Detection in UAV Images Using Transfer Learning and Machine Learning. Remote Sensing, 14 (12), 2837. doi: https://doi.org/10.3390/rs14122837
  18. Chilukuri, D. M., Yi, S., Seong, Y. (2022). A robust object detection system with occlusion handling for mobile devices. Computational Intelligence, 38 (4), 1338–1364. doi: https://doi.org/10.1111/coin.12511
  19. Yu, Y., Zhang, K., Yang, L., Zhang, D. (2019). Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN. Computers and Electronics in Agriculture, 163, 104846. doi: https://doi.org/10.1016/j.compag.2019.06.001
  20. Su, W.-H., Zhang, J., Yang, C., Page, R., Szinyei, T., Hirsch, C. D., Steffenson, B. J. (2020). Automatic Evaluation of Wheat Resistance to Fusarium Head Blight Using Dual Mask-RCNN Deep Learning Frameworks in Computer Vision. Remote Sensing, 13 (1), 26. doi: https://doi.org/10.3390/rs13010026
  21. Valladares, S., Toscano, M., Tufiño, R., Morillo, P., Vallejo-Huanga, D. (2021). Performance Evaluation of the Nvidia Jetson Nano Through a Real-Time Machine Learning Application. Intelligent Human Systems Integration 2021, 343–349. doi: https://doi.org/10.1007/978-3-030-68017-6_51
  22. Jain, N., Gupta, V., Shubham, S., Madan, A., Chaudhary, A., Santosh, K. C. (2021). Understanding cartoon emotion using integrated deep neural network on large dataset. Neural Computing and Applications, 34 (24), 21481–21501. doi: https://doi.org/10.1007/s00521-021-06003-9
  23. Liu, W., Chen, S., Guo, L., Zhu, X., Liu, J. (2021). CPTR: Full transformer network for image captioning. arXiv. doi: https://doi.org/10.48550/arXiv.2101.10804
  24. Rashid, K. M Louis, J. (2019). Times-series data augmentation and deep learning for construction equipment activity recognition. Advanced Engineering Informatics, 42, 100944. doi: https://doi.org/10.1016/j.aei.2019.100944
  25. Srivastava, S., Divekar, A. V., Anilkumar, C., Naik, I., Kulkarni, V., Pattabiraman, V. (2021). Comparative analysis of deep learning image detection algorithms. Journal of Big Data, 8 (1). doi: https://doi.org/10.1186/s40537-021-00434-w
  26. Ghayour, L., Neshat, A., Paryani, S., Shahabi, H., Shirzadi, A., Chen, W. et al. (2021). Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms. Remote Sensing, 13 (7), 1349. doi: https://doi.org/10.3390/rs13071349
  27. Liu, Y.-C., Ma, C.-Y., He, Z., Kuo, C.-W., Chen, K., Zhang, P. et al. (2021). Unbiased teacher for semi-supervised object detection. arXiv. doi: https://doi.org/10.48550/arXiv.2102.09480
  28. Yeshmukhametov, A. N., Koganezawa, K., Buribayev, Z., Amirgaliyev, Y., Yamamoto, Y. (2020). Study on multi-section continuum robot wire-tension feedback control and load manipulability. Industrial Robot: The International Journal of Robotics Research and Application, 47 (6), 837–845. doi: https://doi.org/10.1108/ir-03-2020-0054
Identification of weeds in fields based on computer vision technology

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Published

2023-08-31

How to Cite

Kaldarova, M., Аkanova A., Nazyrova, A., Mukanova, A., & Tynykulova, A. (2023). Identification of weeds in fields based on computer vision technology . Eastern-European Journal of Enterprise Technologies, 4(2 (124), 44–52. https://doi.org/10.15587/1729-4061.2023.284600

Issue

Vol. 4 No. 2 (124) (2023): Information technology. Industry control systems

Section

Information technology

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Copyright (c) 2023 Mira Kaldarova, Akerke Аkanova, Aizhan Nazyrova, Assel Mukanova, Assemgul Tynykulova

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