Abstract
Agriculture and farming are the most important and basic industries that are very important to humanity and generate a considerable portion of any nation's GDP. For good agricultural and farming management, technological advancements and support are required. Smart agriculture (or) farming is a set of approaches that uses a variety of current information and communication technology to improve the production and quality of agricultural products with minimum human involvement and at a lower cost. Smart farming is mostly based on IoT technology, since there is a need to continually monitor numerous aspects in the agricultural field, such as water level, light, soil characteristics, plant development, and so on. Machine learning algorithms are used in smart farming to increase production and reduce the risk of crop damage. Data analytics has been shown through extensive study to improve the accuracy and predictability of smart agricultural systems. Data analytics is utilised in agricultural fields to make decisions and recommend acceptable crops for production. This study provides a comprehensive overview of the different methods and structures utilised in smart farming. It also provides a thorough analysis of different designs and recommends appropriate answers to today's smart farming problems.
Publisher
Institute of Advanced Engineering and Science
Subject
Electrical and Electronic Engineering,Control and Optimization,Computer Networks and Communications,Hardware and Architecture,Instrumentation,Information Systems,Control and Systems Engineering,Computer Science (miscellaneous)
Cited by
21 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Crop Recommendation and Yield prediction Using Machine Learning based Approaches;2024 5th International Conference on Recent Trends in Computer Science and Technology (ICRTCST);2024-04-09
2. IoT Based Precise Greenhouse Management System using Machine Learning Algorithm;2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI);2024-03-14
3. Improved Crop Yields and Resource Efficiency in IoT-based Agriculture with Machine Learning;2024 International Conference on Automation and Computation (AUTOCOM);2024-03-14
4. Control Valve Discharge Prediction Using Multiple Linear Regression for Smart Precision Farming;2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS);2024-02-21
5. Analytics and Decision-making Model Using Machine Learning for Internet of Things-based Greenhouse Precision Management in Agriculture;Microorganisms for Sustainability;2024