Automation and digitization of agriculture using artificial intelligence and internet of things

被引:146
作者
Subeesh, A. [1 ]
Mehta, C. R. [1 ]
机构
[1] ICAR Cent Inst Agr Engn CIAE, Bhopal, Madhya Pradesh, India
来源
ARTIFICIAL INTELLIGENCE IN AGRICULTURE | 2021年 / 5卷
关键词
Agriculture automation; Artificial intelligence; Deep learning; Internet of things; Smart farm machinery; WEED DETECTION; MACHINE VISION; MOISTURE ESTIMATION; FARM MECHANIZATION; FEATURE-SELECTION; RANDOM FOREST; CLASSIFICATION; SUPPORT; CHALLENGES; RECOGNITION;
D O I
10.1016/j.aiia.2021.11.004
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
The growing population and effect of climate change have put a huge responsibility on the agriculture sector to increase food-grain production and productivity. In most of the countries where the expansion of cropland is merely impossible, agriculture automation has become the only option and is the need of the hour. Internet of things and Artificial intelligence have already started capitalizing across all the industries including agriculture. Advancement in these digital technologies has made revolutionary changes in agriculture by providing smart systems that can monitor, control, and visualize various farm operations in real-time and with comparable intelligence of human experts. The potential applications of IoT and AI in the development of smart farm machinery, irrigation systems, weed and pest control, fertilizer application, greenhouse cultivation, storage structures, drones for plant protection, crop health monitoring, etc. are discussed in the paper. The main objective of the paper is to provide an overview of recent research in the area of digital technology-driven agriculture and identification of the most prominent applications in the field of agriculture engineering using artificial intelligence and internet of things. The research work done in the areas during the last 10 years has been reviewed from the scientific databases including PubMed, Web of Science, and Scopus. It has been observed that the digitization of agriculture using AI and IoT has matured from their nascent conceptual stage and reached the execution phase. The technical details of artificial intelligence, IoT, and challenges related to the adoption of these digital technologies are also discussed. This will help in understanding how digital technologies can be integrated into agriculture practices and pave the way for the implementation of AI and IoT-based solutions in the farms. & COPY; 2021 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:278 / 291
页数:14
相关论文
共 105 条
[1]  
Adam G, 2019, Arxiv, DOI arXiv:1904.00438
[2]  
Ahmad I., 2011, P 5 INT C UBIQUITOUS, DOI [10.1145/1968613, DOI 10.1145/1968613]
[3]   Plant discrimination by Support Vector Machine classifier based on spectral reflectance [J].
Akbarzadeh, Saman ;
Paap, Arie ;
Ahderom, Selam ;
Apopei, Beniamin ;
Alameh, Kamal .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 148 :250-258
[4]   An IoT-based greenhouse monitoring system with Micaz motes [J].
Akkas, Mustafa Alper ;
Sokullu, Radosveta .
8TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS (EUSPN 2017) / 7TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH-2017) / AFFILIATED WORKSHOPS, 2017, 113 :603-608
[5]  
Al-Ali A.R., 2019, J. Electr. Sci. Technol., V17, DOI [10.1016/j.jnlest.2020.100017, DOI 10.1016/J.JNLEST.2020.100017]
[6]  
Alam Mansoor, 2020, REAL TIME MACHINE LE, DOI [10.1109/ICEEE49618.2020, DOI 10.1109/ICEEE49618.2020]
[7]  
Aravind Krishnaswamy Rangarajan, 2020, Deep Learning for Analytics, P173, DOI DOI 10.1016/B978-0-12-819764-6.00010-7
[8]  
Arivazhagan S., 2013, Agricultural Engineering International: CIGR Journal, V15, P211
[9]   Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks [J].
Ashwinkumar, S. ;
Rajagopal, S. ;
Manimaran, V ;
Jegajothi, B. .
MATERIALS TODAY-PROCEEDINGS, 2022, 51 :480-487
[10]   Smart poultry management: Smart sensors, big data, and the internet of things [J].
Astill, Jake ;
Dara, Rozita A. ;
Fraser, Evan D. G. ;
Roberts, Bruce ;
Sharif, Shayan .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 170