物联网与人工智能技术在智慧农业中的应用研究

doi:10.11924/j.issn.1000-6850.casb2025-0348

摘要/Abstract

摘要：

探讨物联网与人工智能技术如何推动现代农业的智能化转型，以期为智慧农业发展提供理论依据。本研究采用文献综述法，系统梳理大数据时代背景下，物联网、大数据和人工智能等关键技术在现代智慧农业中的具体应用现状。结果表明，物联网技术实现了农业环境的实时监测，大数据技术为农业生产决策提供了数据支持，而人工智能则在智能育种、产量预测、病虫害识别等领域展现出巨大潜力。物联网、大数据与人工智能的深度融合是提升生产智能化水平的关键。本文剖析了当前在数据、成本、标准与人才方面面临的挑战，并展望了跨模态数据融合、轻量化AI、迁移学习、区块链安全及人机协同等未来方向，以期为相关研究提供借鉴，推动该领域的理论创新与实践落地。

关键词: 物联网, 大数据, 人工智能, 深度学习, 云计算, 智慧农业

Abstract:

This study aims to explore how the Internet of Things (IoT) and artificial intelligence (AI) technologies can drive the intelligent transformation of modern agriculture, with the intention of providing a theoretical basis for the development of smart agriculture. This study employs the literature review method to systematically sort out the current application status of key technologies, including IoT, big data, and AI, in modern smart agriculture against the backdrop of the big data era. The findings indicate that IoT technology enables real-time monitoring of agricultural environments, big data technology provides data support for agricultural production decision-making, and AI demonstrates immense potential in areas such as intelligent breeding, yield prediction, and pest and disease identification. The deep integration of IoT, big data, and AI is the key for improving the level of intelligent production. At the same time, this paper analyzes the current challenges in data, cost, standards and talents, and looks forward to the future directions of cross-modal data fusion, lightweight AI, transfer learning, blockchain security and human-machine collaboration, in order to provide reference for related research and promote theoretical innovation and practice in this field.

Key words: Internet of Things, big data, artificial intelligence, deep learning, cloud computing, smart agriculture

俞静, 许士芳, 韩小双. 物联网与人工智能技术在智慧农业中的应用研究[J]. 中国农学通报, 2026, 42(1): 211-218.

YU Jing, XU Shifang, HAN Xiaoshuang. Research on Integrated Application of Internet of Things and AI Technologies in Smart Agriculture[J]. Chinese Agricultural Science Bulletin, 2026, 42(1): 211-218.

图/表 4

图1. 物联网与人工智能技术在智慧农业中的应用

图2. 植物病虫害诊断流程

图3. 植物高通量表型平台的应用

图4. 智慧灌溉与采收

参考文献 51

[1]	KHANNA A, KAUR, S. Evolution of Internet of Things (IoT) and its significant impact in the field of precision agriculture[J]. Computers and electronics in agriculture, 2019,157:218-231.
[2]	张宇, 成海燕. 我国智慧农业高质量发展的困境及对策[J]. 智慧农业导刊, 2025, 5(4):6-9.
[3]	LI D, NANSEKI T, CHOMEI Y, et al. A review of smart agriculture and production practices in Japanese large-scale rice farming[J]. Journal of the science of food and agriculture, 2023, 103(4):1609-1620. doi: 10.1002/jsfa.v103.4 URL
[4]	GU J, YIN G, HUANG P, et al. An improved back propagation neural network prediction model for subsurface drip irrigation system[J]. Computers and electrical engineering, 2017,60:58-65.
[5]	OUZEMOU J E, EL HARTI A, LHISSOU R, et al. Crop type mapping from pansharpened Landsat 8 NDVI data: A case of a highly fragmented and intensive agricultural system[J]. Remote sensing applications: society and environment, 2018,11:94-103.
[6]	JHA K, DOSHI A, PATEL P, et al. A comprehensive review on automation in agriculture using artificial intelligence[J]. Artificial intelligence in agriculture, 2019,2:1-12.
[7]	DOS SANTOS U J L, PESSIN G, DA C C A, et al. AgriPrediction: A proactive internet of things model to anticipate problems and improve production in agricultural crops[J]. Computers and electronics in agriculture, 2019,161:202-213.
[8]	MUNDERMANN L, ERASMUS Y, LANE B, et al. Quantitative modeling of Arabidopsis development[J]. Plant physiology, 2005, 139(2):960-968. doi: 10.1104/pp.105.060483 URL
[9]	李良涛, 毛振强, 牛灵安, 等. 基于作物生长模型的冬小麦灌溉方案研究[J]. 灌溉排水学报, 2010, 29(2):22-27.
[10]	刘家稷, 杨挺, 汪文勇. 使用双区块链的防伪溯源系统[J]. 信息安全学报, 2018, 3(3):17-29.
[11]	HENDRICKS D E. Portable electronic detector system used with inverted-cone sex pheromone traps to determine periodicity and moth captures[J]. Environmental entomology, 1985, 14(3):199-204. doi: 10.1093/ee/14.3.199 URL
[12]	BAUER A, BOSTROM A G, BALL J, et al. Combining computer vision and deep learning to enable ultra-scale aerial phenotye and precision agriculture: A case study of lettuce production[J]. Horticulture research, 2019,6.
[13]	POUND M P, ATKINSON J A, WELLS D M, et al. Deep learning for multi-task plant phenotye[A].// Proceedings of the IEEE international conference on computer vision workshops[C]. 2017.
[14]	刘志, 翟瑞芳, 彭万伟, 等. 融合注意力机制的Cascade R-CNN田间害虫检测方法[J]. 华中农业大学学报, 2023, 42(3):133-142.
[15]	周维, 牛永真, 王亚炜, 等. 基于改进的YOLOv4-GhostNet水稻病虫害识别方法[J]. 江苏农业学报, 2022, 38(3):685-695.
[16]	王忠培, 张萌, 董伟, 等. 基于迁移学习的多模型水稻病害识别方法研究[J]. 安徽农业科学, 2021, 49(20):236-242.
[17]	ADLI H K, REMLI M A, SALIHIN W K N S, et al. Recent advancements and challenges of AIoT application in smart agriculture: A review[J]. Sensors, 2023, 23(7):3752. doi: 10.3390/s23073752 URL
[18]	ADLI H K, HUSIN K A K, HANAFIAH N H M, et al. Forecasting and analysis of solar power output from integrated solar energy and IoT system[A].// 2021 5th international conference on informatics and computational sciences[C]. IEEE,2021:222-226.
[19]	MASSAOUDI M, ABU-RUB H, REFAAT S S, et al. Deep learning in smart grid technology: A review of recent advancements and future prospects[J]. IEEE Access, 2021,9:54558-54578.
[20]	HERATH H, MITTAL M. Adoption of artificial intelligence in smart cities: A comprehensive review[J]. International journal of information management data insights, 2022, 2(1):100076. doi: 10.1016/j.jjimei.2022.100076 URL
[21]	BADUGE S K, THILAKARATHNA S, PERERA J S, et al. Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications[J]. Automation in construction, 2022,141:104440.
[22]	AHAMAD S, SHAH D, UDHAYAKUMAR R, et al. A comprehensive analysis of smart home applying natural language processing using internet of technologies[A].// 2022 2nd international conference on advance computing and innovative technologies in engineering[C]. IEEE,2022:135-138.
[23]	OLUGBADE S, OJO S, IMOIZE A L, et al. A review of artificial intelligence and machine learning for incident detectors in road transport systems[J]. Mathematical and computational applications, 2022, 27(5):77. doi: 10.3390/mca27050077 URL
[24]	CIECIERSKI-HOLMES T, SINGH R, AXT M, et al. Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: A systematic scoping review[J]. NPJ digital medicine, 2022, 5(1):162. doi: 10.1038/s41746-022-00700-y
[25]	SINHA R S, WEI Y, HWANG S H. A survey on LPWA technology: LoRa and NB-IoT[J]. ICT express, 2017, 3(1):14-21. doi: 10.1016/j.icte.2017.03.004 URL
[26]	NATIVI S, MAZZETTI P, SANTORO M, et al. Big data challenges in building the global earth observation system of systems[J]. Environmental modelling & software, 2015,68:1-26.
[27]	WANG T, CHEN B, ZHANG Z, et al. Applications of machine vision in agricultural robot navigation: A review[J]. Computers and electronics in agriculture, 2022,198:107085.
[28]	SRBINOVSKA M, GAVROVSKI C, DIMCEV V, et al. Environmental parameters monitoring in precision agriculture using wireless sensor networks[J]. Journal of cleaner production, 2015,88:297-307.
[29]	刘文静, 刘章娥, 赖勇, 等. 物联网技术在智慧农业中的应用及发展[J]. 中外食品工业, 2025(17):76-78.
[30]	BOTTA A, DE DONATO W, PERSICO V, et al. Integration of cloud computing and internet of things: A survey[J]. Future generation computer systems, 2016,56:684-700.
[31]	PEDREGOSA F, VAROQUAUX G, GRAMFORT A, et al. Scikit-learn: machine learning in python[J]. Journal of machine learning research, 2011,12:2825-2830.
[32]	朱艾青, 田文婕, 李梅芳. 我国人工智能技术在农业生产领域中的应用研究综述及展望——以番茄生产中的应用为例[J/OL]. 东北农业科学,1-9[2025-11-25].
[33]	彭天蓝. 基于区块链技术的生鲜农产品信息追溯系统优势与发展趋势[J]. 物流科技, 2025, 48(15):43-46.
[34]	王菲, 吕可. 浅析区块链和农业物联网在养殖业中的应用——以众安科技“步步鸡”为例[J]. 农村经济与科技, 2020, 31(1):71-72.
[35]	FERENTINOS K P, KATSOULAS N, TZOUNIS A, et al. Wireless sensor networks for greenhouse climate and plant condition assessment[J]. Biosystems engineering, 2017,153:70-81.
[36]	SHAKOOR N, LEE S, MOCKLER T C. High throughput phenotye to accelerate crop breeding and monitoring of diseases in the field[J]. Current opinion in plant biology, 2017,38:184-192.
[37]	向琳娜, 杨鸥, 张艳, 等. 农业信息化技术在辣椒病虫害防治中的应用[J]. 长江蔬菜, 2024(19):50-52.
[38]	PIXIA D, XIANGDONG W. Recognition of greenhouse cucumber disease based on image processing technology[J]. Open journal of applied sciences, 2013, 3(1):27-31.
[39]	AHMED R Z, BIRADAR R C, CHAUDHARI S S. Cluster-based data aggregation for pest identification in coffee plantations using wireless sensor networks[J]. Computers & electrical engineering, 2016,56:591-607.
[40]	BENDRE M R, THOOL R C, THOOL V R. Big data in precision agriculture: Weather forecasting for future farming[A].// 2015 1st international conference on next generation computing technologies[C]. IEEE,2015:744-750.
[41]	AHOUANDJINOU A S R M, KIKI P M A F, ASSOGBA K. Smart environment monitoring system by using sensors ultrasonic detection of farm pests[A].// 2017 2nd International Conference on Bio-engineering for Smart Technologies[C]. IEEE, 2017.
[42]	FOUGHALI K, FATHALLAH K, FRIHIDA A. Using cloud IOT for disease prevention in precision agriculture[J]. Procedia computer science, 2018,130:575-582.
[43]	ZHOU J, TARDIEU F, PRIDMORE T, et al. Plant phenomics: History, present status and challenges[J]. Journal of Nanjing agricultural university, 2018, 41(4):580-588.
[44]	ANISI M H, ABDUL-SALAAM G, ABDULLAH A H. A survey of wireless sensor network approaches and their energy consumption for monitoring farm fields in precision agriculture[J]. Precision agriculture, 2015, 16(2):216-238. doi: 10.1007/s11119-014-9371-8 URL
[45]	SHAFIEKHANI A, KADAM S, FRITSCHI F B, et al. Vinobot and vinoculer: Two robotic platforms for high-throughput field phenotye[J]. Sensors, 2017, 17(1):214. doi: 10.3390/s17010214 URL
[46]	VAN E H, WOODARD J. Innovation in agriculture and food systems in the digital age[J]. The global innovation index, 2017,4:97-104.
[47]	TANTALAKI N, SOURAVLAS S, ROUMELIOTIS M. Data-driven decision making in precision agriculture: The rise of big data in agricultural systems[J]. Journal of agricultural & food information, 2019, 20(4):344-380.
[48]	ELIJAH O, RAHMAN T A, ORIKUMHI I, et al. An overview of Internet of Things (IoT) and data analytics in agriculture: Benefits and challenges[J]. IEEE internet of things journal, 2018, 5(5):3758-3773. doi: 10.1109/JIoT.6488907 URL
[49]	刘雪梅, 刘英涛, 党信凯. 人工智能技术与设施农业智能化管理的融合在智能灌溉系统中的实践与应用[J]. 农业工程技术, 2024, 44(23):34-35.
[50]	师连鑫, 李海雄, 张博龙, 等. 基于物联网与云计算技术的陕北智慧节水无人灌溉系统[J]. 农业工程, 2025, 15(2):128-133.
[51]	夏张, 赵敏, 王诗成. 水肥一体化技术在密植型玉米上的应用研究[J]. 安徽农学通报, 2025, 31(5):6-9. doi: 10.16377/j.cnki.issn1007-7731.2025.05.002