Functional Framework for IoT-Based Agricultural System

被引：0
作者：
Sharma M.K. [1 ]
Shekhawat R.S. [1 ]
Mehta R. [1 ]
机构：
[1] Manipal University Jaipur, Jaipur
来源：
Studies in Big Data | 2021年 / 99卷
关键词：
Agro-products; Food chain; IoT; Machine learning; Sensors; Wireless;
D O I：
10.1007/978-981-16-6210-2_1
中图分类号：
学科分类号：
摘要：
In the present age of agriculture, governments are trying to increase yield production along with the improved financial health of the farmers. In continuation of the same, a visible technological advancement has taken place in agriculture. Internet of things (IoT) is one of the most revolutionary technologies which have been the game-changer in smart cities, transportation and its monitoring, new-age vehicles, healthcare, industry, security and agriculture, etc. IoT technology is mainly based on four factors: (1) high performance with reduced power requirement. (2) Sensors used to collect real-time data which stored on cloud-based storage for further analysis. (3) Big-data tools play very crucial role in analysis of captured data which is in different forms. (4) High-speed Internet connection plays very important role in IoT architectures. IoT has been the game-changer in the agriculture sector and is being used for various purposes (i.e. yield analysis, monitoring of water, soil, crop, etc., analyse the requirement of fertilizers and other gradients, pesticides requirement and many more). This chapter is presenting an IoT-based architecture modelling for the monitoring of various aspects related to agriculture (i.e. water requirements, soil health analysis, fertilizers and pesticides requirement) and a GPS-based monitoring system is also introduced. However, data will be collected from different sensor(s) and store on the cloud for further analysis. The proposed IoT-based framework consists of a coherent architectural viewpoint of the functional system. The proposed functional framework is useful in organic farming, monitoring of water, fertilizers, pesticides, crop yield and growth, etc. The framework provides timely valuable analysis-based help to enhance the agriculture production with quality of product and improve financial paradigm of farmers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022.
引用
收藏
页码：1 / 27
页数：26
相关论文
共 31 条
  • [1] Devi D., Et al., IoT-deep learning based prediction of amount of pesticides and diseases in fruits, Proceeding in International Conference on Smart Electronics and Communication (ICOSEC), pp. 848-853, (2020)
  • [2] Achilles D., Et al., Internet of things (IoT) and agricultural unmanned aerial vehicles (UAVs) in smart farming: A comprehensive review, Internet Things, pp. 2542-6605, (2020)
  • [3] Sreekantha D.K., Et al., Agricultural crop monitoring using IOT-a study, Proceedings of 11Th International Conference on Intelligent Systems and Control (ISCO), pp. 134-139, (2017)
  • [4] Liheng Z., Et al., Application of IoT in the supply chain of the fresh agricultural products, Proceedings of the International Conference on Communication, Information Management and Network Security (CIMNS 2016). Atlantis Press, pp. 201-204, (2016)
  • [5] Ricardo S., Et al., An intelligent edge-IoT platform for monitoring livestock and crops in a dairy farming scenario, Ad Hoc Netw, 98, 1-23, (2020)
  • [6] Villa Henriksen A., Et al., Internet of things in arable farming: Implementation, applications, challenges and potential, Biosyst. Eng., 191, pp. 60-84, (2020)
  • [7] Satyanarayana G.V., Et al., Wireless sensor based remote monitoring system for agriculture using ZigBee and GPS, Proceedings of Conference on Advances in Communication and Control Systems, pp. 110-115, (2013)
  • [8] Pooja M.M., Et al., Smart agriculture, Int. J. Internet Things, 6, 2, pp. 83-87, (2017)
  • [9] van Thomas K., Et al., Crop yield prediction using machine learning: A systematic literature review, Comput. Electron. Agricult., 177, 1-18, (2020)
  • [10] Ji S., Et al., 3D convolutional neural networks for human action recognition, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1, pp. 221-231, (2012)
1234