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Application of the Backpropagation ANN to Assess the Adoption Level of Farmers to Integrated Pest Management in the Province of Soc Trang (Vietnam)

Received: 14 December 2022     Accepted: 3 January 2023     Published: 13 January 2023
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Abstract

The integrated pest management (IPM) program was implemented in 2015 and 2016 in the province of Soc Trang. The research question is whether Artificial Neural Networks (ANNs) with pattern recognition can be useful for classifying farmers for a more realistic assessment of the performance of an IPM program. To evaluate the performance of the program, three datasets were collected, including dataset S1i with 450 farmers interviewed before conducting the IPM program, S2i with 250 farmers in the pilot area (communes/villages), and S3i with 50 farmers outside the pilot area. The conventional statistical assessment method (CAM) assumes that all farmers in each dataset behave similarly related to IPM concerning the seed, spray frequency, and dosage. This means that the original datasets were used to estimate the required statistical parameters. Thus, the traditional approach wastes information hidden in all surveyed data. Based on ANN, we can classify and determine the percentage of farmers in the six groups or the level of IPM adoption (3 neutral groups and 3 active groups) as well as the actual benefits of the IPM program. ANN-based assessment method (ANN-M) has been proven to be better than CAM in evaluating the performance of the project.

Published in American Journal of Science, Engineering and Technology (Volume 8, Issue 1)
DOI 10.11648/j.ajset.20230801.12
Page(s) 13-22
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Artificial Neural Networks, ANN-Based Classification, Farmers, IPM Adoption

References
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[2] Abodun, O. I. et al., 2017. Comprehensive Review of Artificial Neural Network Application to Pattern Recognition. s. l., IEEE.
[3] Anders, U., 1997. Statistische neuronale Netze. München: Verlag Vahlen.
[4] Bala, R. & Kumar, D., 2017. Classification Using ANN: A Review. International Journal of Computational Intelligence Research, Volume 13 (7), pp. 1811-1820.
[5] Jain, A., 2000. Statistical Pattern Recognition: A Review. IEEE transactions on pattern analysis and machine intelligence, vol. 22, No. 1. January, pp. 4-37.
[6] Nikzad, M., Movagharnejad, K. & Talebnia, F., 2012. Comparative study between neural network model and mathematical models for prediction of glucose concentration during enzymatic hydrolysis. International Journal of Computer Applications, Volume 56 (1), pp. 43-48.
[7] McAvoy, T. J. et al., 1992. A comparison of neural networks and partial least squares for deconvoluting fluorescence spectra. Biotechnology and bioengineering, Volume 40, pp. 53-62.
[8] Alwang, J., Norton, G. & Larochelle, C., 2019. Obstacles to widespread diffusion of IPM in developing countries: Lessons from the field. Journal of Integrated Pest Management, (2019) 10 (1): 10 (doi: 10.1093/jipm/pmz008), pp. 1-8.
[9] Dung, N. T., 2018. Economic and environmental effects of Integrated Pest Management program: A case study of Hau Giang province (Mekong Delta). J. Viet. Env. 2018, 9 (2), pp. 77-85.
[10] Dung, N. T., Anh, N. T. N. & Kien, P. D., 2021. IPM Program Combined with “Rice Fields, Flower Banks” or BIO-IPM Program in Soc Trang Province (Vietnam). Journal Sustainability in Environment, pp. 1-18.
[11] Kabir, M. & Rainis, R., 2015. Do Farmers Not Widely Adopt Environmentally Friendly Technologies? Lesson from Integrated Pest Management (IPM). Journal Modern Applied Science Vol. 9, No. 3, pp. 208-215.
[12] Fan, L., et al., 2015. Factors affecting farmers' behaviour in pesticide use: Insights from a field study in northern China. Journal of science total environment, Volume 537, pp. 360-368.
[13] Hadi, V., 2012. Exploring the determinants of adoption behaviour of clean technologies in agriculture: a case of integrated pest management. Asian Journal of Technology Innovation 20 (1), pp. 67-82.
[14] Kusek, J. & Rist, R., 2005. Ten Steps to a Results-Based Monitoring and Evaluation System: A Handbook for Development Practitioners. 1. ed. Washington DC: The International Bank for Reconstruction and Development.
[15] Rogers, E. M., Singhal, A. & Quinland, M. M., 2009. Diffusion of innovations. In: An integrated approach to communication theory and research. NY: Routledge, pp. 418-434.
[16] Gullu, M., Yilmaz, M. & Yilmaz, I., 2011. Application of Back Propagation Artificial Neural Network for Modelling Local GPS/Levelling Geoid Undulations: A Comparative Study. Marocco, FIG Working Week 2011.
[17] WB, 2016. Transforming Vietnamese Agriculture: Gaining More from Less (Vietnam Development Report 2016), Washington: The World Bank.
[18] Wiedmann, K.-P., Buckler, F. & Buxel, H., 2001. Data Mining - ein führendes Überblick. In: F. B. Klaus-Peter Wiedmann, ed. Neuronale Netze im Marketing-Management: Praxisorientierte Einführung in modernes Data-mining. Wiesbaden: Gabler, pp. 15-34.
[19] Arockiaraj, M., 2013. Applications of Neural Networks in data mining. Research Inventy: International Journal Of Engineering And Science, Vol. 3, Issue 1 (May 2013), pp. 08-11.
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[21] Saravan, K. & Sasithra, S., 2014. Review on classification based on artificial neural networks. International Journal of Ambient Systems and Applications (IJASA) Vol. 2, No. 4, pp. 11-18.
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Cite This Article
  • APA Style

    Nguyen Trung Dung, Bui Thi Thu Hoa, Nguyen Tuan Anh. (2023). Application of the Backpropagation ANN to Assess the Adoption Level of Farmers to Integrated Pest Management in the Province of Soc Trang (Vietnam). American Journal of Science, Engineering and Technology, 8(1), 13-22. https://doi.org/10.11648/j.ajset.20230801.12

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    ACS Style

    Nguyen Trung Dung; Bui Thi Thu Hoa; Nguyen Tuan Anh. Application of the Backpropagation ANN to Assess the Adoption Level of Farmers to Integrated Pest Management in the Province of Soc Trang (Vietnam). Am. J. Sci. Eng. Technol. 2023, 8(1), 13-22. doi: 10.11648/j.ajset.20230801.12

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    AMA Style

    Nguyen Trung Dung, Bui Thi Thu Hoa, Nguyen Tuan Anh. Application of the Backpropagation ANN to Assess the Adoption Level of Farmers to Integrated Pest Management in the Province of Soc Trang (Vietnam). Am J Sci Eng Technol. 2023;8(1):13-22. doi: 10.11648/j.ajset.20230801.12

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  • @article{10.11648/j.ajset.20230801.12,
      author = {Nguyen Trung Dung and Bui Thi Thu Hoa and Nguyen Tuan Anh},
      title = {Application of the Backpropagation ANN to Assess the Adoption Level of Farmers to Integrated Pest Management in the Province of Soc Trang (Vietnam)},
      journal = {American Journal of Science, Engineering and Technology},
      volume = {8},
      number = {1},
      pages = {13-22},
      doi = {10.11648/j.ajset.20230801.12},
      url = {https://doi.org/10.11648/j.ajset.20230801.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajset.20230801.12},
      abstract = {The integrated pest management (IPM) program was implemented in 2015 and 2016 in the province of Soc Trang. The research question is whether Artificial Neural Networks (ANNs) with pattern recognition can be useful for classifying farmers for a more realistic assessment of the performance of an IPM program. To evaluate the performance of the program, three datasets were collected, including dataset S1i with 450 farmers interviewed before conducting the IPM program, S2i with 250 farmers in the pilot area (communes/villages), and S3i with 50 farmers outside the pilot area. The conventional statistical assessment method (CAM) assumes that all farmers in each dataset behave similarly related to IPM concerning the seed, spray frequency, and dosage. This means that the original datasets were used to estimate the required statistical parameters. Thus, the traditional approach wastes information hidden in all surveyed data. Based on ANN, we can classify and determine the percentage of farmers in the six groups or the level of IPM adoption (3 neutral groups and 3 active groups) as well as the actual benefits of the IPM program. ANN-based assessment method (ANN-M) has been proven to be better than CAM in evaluating the performance of the project.},
     year = {2023}
    }
    

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    T1  - Application of the Backpropagation ANN to Assess the Adoption Level of Farmers to Integrated Pest Management in the Province of Soc Trang (Vietnam)
    AU  - Nguyen Trung Dung
    AU  - Bui Thi Thu Hoa
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    Y1  - 2023/01/13
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    DO  - 10.11648/j.ajset.20230801.12
    T2  - American Journal of Science, Engineering and Technology
    JF  - American Journal of Science, Engineering and Technology
    JO  - American Journal of Science, Engineering and Technology
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    SN  - 2578-8353
    UR  - https://doi.org/10.11648/j.ajset.20230801.12
    AB  - The integrated pest management (IPM) program was implemented in 2015 and 2016 in the province of Soc Trang. The research question is whether Artificial Neural Networks (ANNs) with pattern recognition can be useful for classifying farmers for a more realistic assessment of the performance of an IPM program. To evaluate the performance of the program, three datasets were collected, including dataset S1i with 450 farmers interviewed before conducting the IPM program, S2i with 250 farmers in the pilot area (communes/villages), and S3i with 50 farmers outside the pilot area. The conventional statistical assessment method (CAM) assumes that all farmers in each dataset behave similarly related to IPM concerning the seed, spray frequency, and dosage. This means that the original datasets were used to estimate the required statistical parameters. Thus, the traditional approach wastes information hidden in all surveyed data. Based on ANN, we can classify and determine the percentage of farmers in the six groups or the level of IPM adoption (3 neutral groups and 3 active groups) as well as the actual benefits of the IPM program. ANN-based assessment method (ANN-M) has been proven to be better than CAM in evaluating the performance of the project.
    VL  - 8
    IS  - 1
    ER  - 

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Author Information
  • Faculty of Economics and Management, Thuyloi University, Hanoi, Vietnam

  • Faculty of Economics and Management, Thuyloi University, Hanoi, Vietnam

  • Institute of Water Economics and Management, Vietnam Academy for Water Resources, Hanoi, Vietnam

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