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

Nguyen Trung Dung; Bui Thi Thu Hoa; Nguyen Tuan Anh

doi:doi:10.11648/j.ajset.20230801.12

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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)

Nguyen Trung Dung, Bui Thi Thu Hoa, Nguyen Tuan Anh

Published in American Journal of Science, Engineering and Technology (Volume 8, Issue 1)

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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[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.
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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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TY  - JOUR
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
AU  - Nguyen Tuan Anh
Y1  - 2023/01/13
PY  - 2023
N1  - https://doi.org/10.11648/j.ajset.20230801.12
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
SP  - 13
EP  - 22
PB  - Science Publishing Group
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

Nguyen Trung Dung

Faculty of Economics and Management, Thuyloi University, Hanoi, Vietnam
Bui Thi Thu Hoa

Faculty of Economics and Management, Thuyloi University, Hanoi, Vietnam
Nguyen Tuan Anh

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

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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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TY  - JOUR
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
AU  - Nguyen Tuan Anh
Y1  - 2023/01/13
PY  - 2023
N1  - https://doi.org/10.11648/j.ajset.20230801.12
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
SP  - 13
EP  - 22
PB  - Science Publishing Group
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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