A NOVEL SYSTEM FOR PREDICTING THE DAMAGE OF RICE DISEASES IN AN GIANG PROVINCE, VIETNAM
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Published:
Dec 31, 2023
Keywords:
artificial neural network, genetic algorithms, rice diseases
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Abstract
Many researchers have recently considered information technology applications to support smart agriculture. Numerous solutions and information technology systems have been implemented to benefit farmers, such as an e-commerce website for exchanging agricultural products, an agricultural information system, a livestock information management system, and an agricultural environment monitoring system. This paper presents a novel system that combines artificial back-propagation neural networks with genetic algorithms for predicting the damage of rice diseases in An Giang province. The system predicts rice disease damage in the coming weeks in 11 districts in An Giang based on input parameters such as climate conditions and previous rice disease damage. The approach was evaluated on a dataset collected by the An Giang Plant Protection Department over 22 years. The rice diseases forecasting system produced promising results and received positive feedback from forecast department specialists, with an RMSE of 19.31 on the test dataset (ha).
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1.
Doan T-N, Huynh P-H. A NOVEL SYSTEM FOR PREDICTING THE DAMAGE OF RICE DISEASES IN AN GIANG PROVINCE, VIETNAM. journal [Internet]. 31Dec.2023 [cited 27Apr.2024];13(4). Available from: https://journal.tvu.edu.vn/index.php/journal/article/view/2844
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detection and warning system of rice disease based
on the GIS and IOT in Jilin Province. In: Computer
and Computing Technologies in Agriculture VIII:
8th IFIP WG 5.14 International Conference, CCTA
2014, Beijing, China. Springer International Publishing; 2015. p.337–343. https://dx.doi.org/10.1007/978-
3-319-19620-6_38.
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Pham TG, Nguyen TM. Assessing damages of
agricultural land due to flooding in a lagoon
region based on remote sensing and GIS: case
study of the Quang Dien District, Thua Thien
Hue Province, central Vietnam. Journal of
Vietnamese Environment. 2020;12(2): 100–107.
https://doi.org/10.13141/jve.vol12.no2.pp100-107.
[4] Al-Ismaili AM. GIS and remote sensing
techniques in Controlled Environment Agriculture:
A review. Journal of Agricultural and
Marine Sciences [JAMS]. 2021;26(2): 10–23.
https://doi.org/10.53541/jams.vol26iss2pp10-23.
[5] Plata IT, Panganiban EB, Alado DB, Taracatac AC,
Bartolome BB, Labuanan FRE. Drone-based Geographical Information System (GIS) Mapping of
Cassava Pythoplasma Disease (CPD) for Precision
Agriculture. International Journal of Emerging Technology and Advanced Engineering. 2022;12(2): 1–9.
https://doi.org/10.46338/ijetae0222_01.
[6] Vedaldi A, Zisserman A. Efficient additive kernels
via explicit feature maps. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2012;34(3):
480–492. http://dx.doi.org/10.1109/TPAMI.2011.153.
[7] Visa S, Ralescu A. Issues in mining imbalanced data sets – a review paper. In: Proceedings of the Sixteen Midwest Artificial Intelligence and Cognitive Science Conference, MAICS-
2005, Dayton, 16th – 17th April 2005. p.67–73.
https://www.researchgate.net/publication/228386653
_Issues_in_Mining_Imbalanced_Data_Sets_-
_A_Review_Paper [Accessed 15th January 2023].
[8] Wang C, Yan S, Zhang HJ. Large scale natural
image classification by sparsity exploration.
In: Proceedings of the IEEE International
Conference on Acoustics, Speech, and Signal
Processing. IEEE; 2009. p.3709–3712.
http://dx.doi.org/10.1109/ICASSP.2009.4960432.
[9] Dreyfus SE. Artificial neural networks, back propagation, and the Kelley-Bryson gradient procedure. Journal of Guidance, Control, and Dynamics. 1990;13:
926–928. https://doi.org/10.2514/3.25422.
[10] Polak E. Optimization: Algorithms and Consistent
Approximations. New York: Springer. 1997.
[11] Turing AM. Computing Machinery and Intelligence.
In: Epstein R, Roberts G, Beber G. (eds) Parsing the Turing Test. Dordrecht: Springer; 2009.
https://doi.org/10.1007/978-1-4020-6710-5_3.
[12] Back T. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. New York: Oxford University Press; 1996.
[13] Ministry of Agriculture and Rural Development, Vietnam. National technical regulation on surveillance
method of rice pests. QCVN 01-38: 2010/BNNPTNT.
Ministry of Agriculture & Rural Development; 2010.
[14] Nguyen Manh Chinh. Plant Doctor’s Handbook.
[Cẩm nang bác sĩ cây trồng]. Hanoi, Vietnam: Agriculture Publishing House; 2012.
[15] An Giang Plant Protection Joint Stock. Handbook of
rice farmers need to know. [Sổ tay người nông dân
trồng rau cần biết]. An Giang, Vietnam: An Giang
Plant Protection Joint Stock; 2020.
[16] Montana DJ, Davis L. Training feedforward neural
networks using genetic algorithms. In: Proceedings
of the 11th International Joint Conference on Artificial Intelligence, San Mateo. United States: Morgan
Kaufmann Publishers; 1989. p.762–767.
[17] Whitley D, Starkweather T, Bogart C. Genetic algorithms and neural networks: Optimizing connections
and connectivity. Parallel Computing. 1990;14(3):
347–361. https://doi.org/10.1016/0167
-8191(90)90086-O.