
Application and existing problems of artificial intelligence technology in the agricultural field
- 1 Beijing No.12 high school Beijing, 100070, China
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Abstract
In recent years, the application of artificial intelligence technology in the field of agriculture has been rapidly developed. This paper summarizes the application of artificial intelligence in agriculture and divides it into two main directions: monitoring system and expert system. This paper analyzes the soil monitoring, pest monitoring, and plant growth detection of the monitoring system, the simple decision chain of the expert system, and the complex expert system combined with artificial intelligence technology. Utilizing sensor networks, image processing, and machine learning techniques, artificial intelligence enables real-time monitoring of soil parameters, automatic identification of pest and disease, analysis of plant growth status, and provision of tailored management recommendations. By employing rule-based expert systems, artificial intelligence assists farmers in making informed decisions. These applications have significantly advanced resource management optimization, pest control, precise growth monitoring, and intelligent decision-making in agriculture. At the end of the article, this paper summarizes the full text and looks forward to the future trend.
Keywords
agriculture, artificial intelligence, machine learning, expert system, monitoring system
[1]. Wang Hui, et al. "Research on methods of data collection and integration of agricultural resources in Xinjiang Production and Construction Corps." Journal of Agricultural Big Data 3.2 (2021): 31-41.
[2]. Gonzalez-de-Santos P, Fernández R, Sepúlveda D, Navas E, Emmi L, Armada M. Field Robots for Intelligent Farms—Inhering Features from Industry. Agronomy. 2020; 10(11):1638.
[3]. Lu Xiaosong, WANG Guoqing, LI Xu Zhi, DU Junyang, Sun Li. Application of site environment big data acquisition and machine learning methods in pollution intelligent identification. Journal of Ecology and Rural Environment, 2022, 38(9): 1101-1111.
[4]. Xie Chengjun, Liu Zhendong, Zhang Wei, et al. Mobile intelligent recognition system for pests and diseases based on big data for plant protection: Following Knowledge. Plant Doctor, 2020 (2):53-59.
[5]. Zheng Yongzhi, Wu Huilin, Zhu Dingju, et al. Question and answer system based on knowledge map of lychee and Longan pests. Computer and Digital Engineering, (2021)
[6]. Claudio O Stöckle, Marcello Donatelli, Roger Nelson. CropSyst, a cropping systems simulation model, European Journal of Agronomy (2003):3-4
[7]. Li Bohu, et al. Preliminary research on modeling and simulation techniques for novel artificial intelligence systems. Chinese Journal of System Simulation 30.2 (2018): 349.
[8]. Feng Ding. Neural network expert system. Science Press, 2006.
[9]. Alkaissi, Hussam, and Samy I. McFarlane. Artificial hallucinations in ChatGPT: implications in scientific writing. Cureus 15.2 (2023).
[10]. Beierle, Christoph, and Ingo J. Timm. Intentional forgetting: An emerging field in AI and beyond. KI-Künstliche Intelligenz 33 (2019): 5-8.
[11]. Yang Guoqiang, Wang Shuangxi, and Du Wei. Research progress of agricultural expert system in China. Journal of Shanxi Agricultural University 24.3 (2004): 303-305.
[12]. Yuan Chunxin, et al. Agricultural expert system and its application in rice cultivation. Southwest Agricultural Journal 16.4 (2003): 130-133.
[13]. Świechowski, Maciej, et al. Monte Carlo tree search: A review of recent modifications and applications. Artificial Intelligence Review 56.3 (2023): 2497-2562.
[14]. Antonin Pépin, Maria Vittoria Guidoboni, Philippe Jeanneret, Hayo M.G. van der Werf, Using an expert system to assess biodiversity in life cycle assessment of vegetable crops, Ecological Indicators, 148, (2023). 1470-160X
Cite this article
Niu,M. (2024). Application and existing problems of artificial intelligence technology in the agricultural field. Applied and Computational Engineering,35,32-40.
Data availability
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Volume title: Proceedings of the 2023 International Conference on Machine Learning and Automation
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