Deep learning and its role in smart agriculture

被引:13
|
作者
Magomadov, V. S. [1 ]
机构
[1] Chechen State Univ, Fac Informat Technol, 32 Sheripov St, Grozny 364024, Russia
关键词
D O I
10.1088/1742-6596/1399/4/044109
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Deep learning is a data analysis and image-processing method, which has recently gained a lot of attention as a tool, which has great potential and promising results. There are many different fields that deep learning has been applied to and it is also being applied to the field of agriculture. The purpose of this paper is to explore deep learning in terms of agriculture and food production. The performance of deep learning in agriculture is the focus of this paper comparing it to other existing artificial intelligence models, which have been used in agriculture. In addition, several types of deep learning models are covered and their differences are explained. The paper explains why some deep learning models are better equipped to be used in the field of agriculture than other models.
引用
收藏
页数:5
相关论文
共 50 条
  • [31] Tomato disease detection with lightweight recurrent and convolutional deep learning models for sustainable and smart agriculture
    Le, An Thanh
    Shakiba, Masoud
    Ardekani, Iman
    FRONTIERS IN SUSTAINABILITY, 2024, 5
  • [32] dCrop: A Deep-Learning based Framework for Accurate Prediction of Diseases of Crops in Smart Agriculture
    Pallagani, Vishal
    Khandelwal, Vedant
    Chandra, Bharath
    Udutalapally, Venkanna
    Das, Debanjan
    Mohanty, Saraju P.
    2019 IEEE INTERNATIONAL SYMPOSIUM ON SMART ELECTRONIC SYSTEMS (ISES 2019), 2019, : 29 - 33
  • [33] Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming
    Shaikh, Tawseef Ayoub
    Rasool, Tabasum
    Lone, Faisal Rasheed
    COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 198
  • [34] Smart Agriculture Using Iot and Machine Learning
    David, Shiela
    BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS, 2020, 13 (06): : 326 - 329
  • [35] Machine Learning for Smart Agriculture: A Comprehensive Survey
    Mahmood M.R.
    Matin M.A.
    Goudos S.K.
    Karagiannidis G.
    IEEE Transactions on Artificial Intelligence, 2024, 5 (06): : 2568 - 2588
  • [36] Deep Learning Smart Microscope
    Jalali, Bahram
    Mahjoubfar, Ata
    Chen, Claire Lifan
    2018 CONFERENCE ON LASERS AND ELECTRO-OPTICS (CLEO), 2018,
  • [37] Editorial: Role of Microbes in Climate Smart Agriculture
    Das, Suvendu
    Ho, Adrian
    Kim, Pil Joo
    FRONTIERS IN MICROBIOLOGY, 2019, 10
  • [38] GMLP-IDS: A Novel Deep Learning-Based Intrusion Detection System for Smart Agriculture
    Berguiga, Abdelwahed
    Harchay, Ahlem
    Massaoudi, Ayman
    Ben Ayed, Mossaad
    Belmabrouk, Hafedh
    CMC-COMPUTERS MATERIALS & CONTINUA, 2023, 77 (01): : 379 - 402
  • [39] S2AM: a sustainable smart agriculture model for crop protection based on deep learning
    Sharma, Abhilasha
    Sharma, Parul
    JOURNAL OF PLANT DISEASES AND PROTECTION, 2024, 131 (06) : 2181 - 2205
  • [40] Multi-Timeframe Forecasting Using Deep Learning Models for Solar Energy Efficiency in Smart Agriculture
    Venkatesan, Saravanakumar
    Cho, Yongyun
    ENERGIES, 2024, 17 (17)