Trends in applying artificial intelligence in agricultural cultivation and some orientations for Vietnam
DOI:
https://doi.org/10.56764/hpu2.jos.2026.5.01.14-26Abstract
This study examines the pivotal role of Artificial Intelligence (AI) in transforming agricultural biotechnology, especially amid the pressing challenges of climate change and the escalating global demand for food. AI has demonstrated considerable potential in optimizing agricultural processes through big data analytics, predictive modeling, and the development of novel crop varieties with enhanced resilience, improved resource efficiency, and reduced environmental impact. In precision agriculture, AI enables optimized use of water, fertilizers, and pesticides, and provides accurate forecasts for planting and harvesting schedules. Such capabilities substantially enhance productivity and mitigate production risks for farmers. AI applications for detecting pests and diseases have opened new ways to monitor and manage crops, thereby improving production quality and efficiency. AI also plays a key role in adapting agriculture to climate change, from smart irrigation management to adjusting farming practices based on weather conditions. However, for AI to reach its full potential in Vietnam, attention needs to be paid to factors such as digital infrastructure, training and awareness raising, cost and accessibility, integration with traditional methods, data security and adaptability to local conditions. These factors not only ensure effective AI deployment but also help bring sustainable benefits to Vietnam’s agricultural sector in the digital age.
References
[1] G. Codeluppi et al., “LoRaFarM: A LoRaWAN-based smart farming modular IoT architecture,” Sensors, vol. 20, p. 2028, Apr. 2020, doi: 10.3390/s20072028.
[2] S. Kim et al., “IoT-based strawberry disease prediction system for smart farming,” Sensors, vol. 18, p. 4051, Nov. 2018, doi: 10.3390/s18114051.
[3] M. A. Ahmed et al., “LoRa based IoT platform for remote monitoring of large-scale agriculture farms in Chile,” Sensors, vol. 22, p. 2824, Jan. 2022, doi: 10.3390/s22082824.
[4] N. Jaliyagoda et al., “Internet of things (IoT) for smart agriculture: Assembling and assessment of a low-cost IoT system for polytunnels,” PLoS ONE, vol. 18, p. e0278440, May. 2023, doi: 10.1371/journal.pone.0278440.
[5] L. Droukas et al., “A survey of robotic harvesting systems and enabling technologies,” Journal of Intelligent & Robotic Systems, vol. 107, p. 21, Jan. 2023, doi: 10.1007/s10846-022-01793-z.
[6] T. Wang et al., “Applications of UAS in crop biomass monitoring: A review,” Frontiers in Plant Science, vol. 12, p. 616689, Apr. 2021, doi: 10.3389/fpls.2021.616689.
[7] B. B. Sapkota et al., “Evaluating cross-applicability of weed detection models across different crops in similar production environments,” Frontiers in Plant Science, vol. 13, p. 837726, 2022, doi: 10.3389/fpls.2022.837726.
[8] M. Darbyshire et al., “Towards practical object detection for weed spraying in precision agriculture,” Frontiers in Plant Science, vol. 14, p. 1183277, Nov. 2023, doi: 10.3389/fpls.2023.1183277.
[9] P. Wang et al., “Weed25: A deep learning dataset for weed identification,” Frontiers in Plant Science, vol. 13, p. 1053329, Nov. 2022, doi: 10.3389/fpls.2022.1053329.
[10] B. Yang et al., “Applications of deep-learning approaches in horticultural research: A review,” Horticulture Research, vol. 8, p. 123, Jun. 2021, doi: 10.1038/s41438-021-00560-9.
11] M. Zhang et al., “Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery,” Biosystems Engineering, vol. 189, pp. 24–35, 2020, doi: 10.1016/j.biosystemseng.2019.11.001.
[12] Y. Liu et al., “Evaluating how lodging affects maize yield estimation based on UAV observations,” Frontiers in Plant Science, vol. 13, p. 979103, 2022, doi: 10.3389/fpls.2022.979103.
[13] T. Yang et al., “An approach for plant leaf image segmentation based on YOLOv8 and the improved DeepLabV3,” Plants, vol. 12, p. 3438, 2023, doi: 10.3390/plants12193438.
[14] S. Yang et al., “Maize-YOLO: A new high-precision and real-time method for maize pest detection,” Insects, vol. 14, p. 278, Mar. 2023, doi: 10.3390/insects14030278.
[15] E. C. Tetila et al., “Detection and classification of soybean pests using deep learning with UAV images,” Computers and Electronics in Agriculture, vol. 179, p. 105836, Dec. 2020, doi: 10.1016/j.compag.2020.105836.
[16] X. Deng et al., “Detection of citrus Huanglongbing based on multi-input neural network model of UAV hyperspectral remote sensing,” Remote Sensing, vol. 12, p. 2678, , Aug. 2020, doi: 10.3390/rs12172678.
[17] B. Stutsel et al., “Detecting plant stress using thermal and optical imagery from an unoccupied aerial vehicle,” Frontiers in Plant Science, vol. 12, p. 734944, Oct. 2021, doi: 10.3389/fpls.2021.734944.
[18] M. Gomez Selvaraj et al., “Detection of banana plants and their major diseases through aerial images and machine learning methods: A case study in DR Congo and Republic of Benin,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 169, pp. 110–124, Nov. 2020, doi: 10.1016/j.isprsjprs.2020.08.025.
[19] M. S. Z. Abid et al., “Bangladeshi crops leaf disease detection using YOLOv8,” Heliyon, vol. 10, p. e36694, Sep. 2024, doi: 10.1016/j.heliyon.2024.e36694.
[20] D. Gao et al., “A framework for agricultural pest and disease monitoring based on Internet-of-things and unmanned aerial vehicles,” Sensors, vol. 20, p. 1487, Mar. 2020, doi: 10.3390/s20051487.
[21] K. Johansen et al., “Mapping the condition of macadamia tree crops using multi-spectral UAV and WorldView-3 imagery,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 165, pp. 28–40, Jul. 2020, doi: 10.1016/j.isprsjprs.2020.04.017.
[22] A. Bouguettaya et al., “A survey on deep learning-based identification of plant and crop diseases from UAV-based aerial images,” Cluster Computing, vol. 26, pp. 1297–1317, 2023, doi: 10.1007/s10586-022-03627-x.
[23] M. Kerkech et al., “Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach,” Computers and Electronics in Agriculture, vol. 174, p. 105446, Jul. 2020, doi: 10.1016/j.compag.2020.105446.
[24] J. D. Liedtke et al., “High-throughput phenotyping of dynamic canopy traits associated with stay-green in grain sorghum,” Plant Phenomics, vol. Sep. 2020, p. 4635153, 2020, doi: 10.34133/2020/4635153.
[25] W. Jia et al., “Daily reference evapotranspiration prediction for irrigation scheduling decisions based on the hybrid PSO-LSTM model,” PLoS ONE, vol. 18, p. e0281478, Apr. 2023, doi: 10.1371/journal.pone.0281478.
[26] K. E. Alordzinu et al., “Ground-based hyperspectral remote sensing for estimating water stress in tomato growth in sandy loam and silty loam soils,” Sensors, vol. 21, p. 5705, 2021, doi: 10.3390/s21175705.
[27] W. Li et al., “Automatic localization and count of agricultural crop pests based on an improved deep learning pipeline,” Scientific Reports, vol. 9, p. 7024, 2019, doi: 10.1038/s41598-019-43171-0.
[28] S.-J. Hong et al., “Automatic pest counting from pheromone trap images using deep learning object detectors for Matsucoccus thunbergianae monitoring,” Insects, vol. 12, p. 342, 2021, doi: 10.3390/insects12040342.
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Copyright (c) 2026 Xuan-Thanh Nguyen, Xuan-Phong Ong , Huy-Gioi Dong, Son-Thinh Pham, Hoang-Thien Pham Van, Truong-Xuan Le, Duc-Ha Chu
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