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Research Progress in Agricultural Pest and Disease Diagnosis Technology based on UAV Remote Sensing

Author(s)

Zipan Zhang

Affiliation(s)

Yantai University, Trier Institute of Technology for Sustainable Development, Yantai, Shandong, China

Abstract

Crops are an important economic product in my country and an indispensable part of our daily life. Different crops such as wheat, cotton, and soybeans play their own important values. As the environment continues to change, various pests that endanger the healthy growth of crops emerge in an endless stream. The traditional technical means used by field workers to control pests and diseases on large-scale crops are inefficient and time-consuming. In recent years, with the in-depth research and development of science and technology, drone remote sensing technology has been widely used in the fields, greatly facilitating the work of workers and making traditional agriculture further develop towards smart agriculture. This paper summarizes the research progress of agricultural pest and disease identification technology based on drone remote sensing, mainly focusing on drone remote sensing technical means, algorithm models, and data sharing. Drone remote sensing identification of agricultural pests and diseases usually use a variety of technical means such as hyperspectral cameras, multispectral cameras, thermal infrared imaging, visible light imaging, and lidar. Then, the practical application of machine learning models and deep learning models in identifying pests and diseases is described in detail, and the combination of artificial intelligence and Internet of Things-enhanced systems in drone AI remote sensing agricultural data sharing is explored in the end. By analyzing existing studies, this paper summarizes the advantages and disadvantages of applying UAV remote sensing to agricultural pest and disease identification, and proposes innovative ideas, aiming to provide reference for the subsequent development of this field.

Keywords

UAV Remote Sensing; Agricultural Pest Identification; Algorithm Model; Data Sharing

References

[1] Li Xuguang. Research on the application of UAV remote sensing technology in agricultural pest monitoring[J]. Hebei Agricultural Machinery, 2024(2):42-44. [2] Gu Zexin, Wang Baijuan, Su Wenping, et al. Application of hyperspectral UAV remote sensing image recognition technology in tea garden pest control [J]. Economist, 2020, (12): 61-62. [3] Zhao Shengli, Mujahid Hussain, Wang Guobin, et al. Research progress of crop growth monitoring based on UAV remote sensing[J]. Jiangsu Agricultural Science, 2024, 52(8):8-15. [4] Peng Xiaomeng, Li Simeng, Zhao Lixiang. Promotion and application of agricultural drone plant protection technology in corn disease and insect pest control[J]. Agricultural Science and Technology, 2025(4):148-150. [5] Li Zhaodong. Exploration on the application of remote sensing images and UAV technology in forest pest control[J]. Modern Agricultural Machinery, 2023(2):124-126. [6] Wang Zhiye. Research on wheat growth parameter estimation based on UAV multispectral remote sensing and machine learning[D]. Henan University of Finance and Economics, 2024. [7] Cui Meina. Dynamic monitoring of cotton mite pests based on UAV remote sensing[D]. Shihezi University, 2019. [8] Cao Yingli, Zhang Hongze, Guo Fuxu, et al. Research progress of crop disease monitoring based on UAV remote sensing[J]. Journal of Shenyang Agricultural University, 2024, 55(05): 616-628. [9] Li Wei. Application of UAV remote sensing technology in forestry resource survey and pest control[J]. China Agricultural Abstracts-Agricultural Engineering, 2019, 31(05):45-46+60. [10] Liu Yuhang. Application of UAV remote sensing technology in agricultural pest monitoring[J]. Hebei Agricultural Machinery, 2025, (04): 21-23. [11] Cui Kai. Exploration of the application of UAV remote sensing technology in crop disease and insect pest monitoring[J]. Agricultural Science and Technology Innovation, 2025, (07): 26-28.