Machine Learning Applications in Iot Based Agriculture and Smart Farming: A Review

Maduranga, Mwp; Abeysekera, Ruvan

doi:10.33564/ijeast.2020.v04i12.004

Cited by 40 publications

(16 citation statements)

References 7 publications

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“…Maduranga and Abeysekera (2020) analyze machine learning applications in IoT-based agriculture and smart farming. Instead of presenting a methodology, the paper surveys agricultural Internet of Things (IoT) machine-learning techniques [18]. The result is a thorough understanding of machine learning applications in agriculture, which can inform future implementations.…”

Section: Literature Surveymentioning

confidence: 99%

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Shwetabh,

Ambhaikar

2024

BIO Web Conf.

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Implementing intelligent monitoring systems for Agricultural Machinery (AM) is hindered by the intricate and costly nature of the Internet of Things (IoT) sensor technologies. The heavy reliance on cloud and fog computing, the availability of network infrastructure, and the need for expert knowledge pose challenges in rural areas that lack network connectivity. Using edge devices, such as smartphones, which possess significant computational capabilities, is a potential solution that has not yet been fully realized in the commercial sphere. Furthermore, the increasing demand from users for economically viable and user-friendly technology serves as a driving force for transitioning away from expensive and intricate sensors towards more cost-effective alternatives. In the IoT era, there is anticipated to be a widespread network connection between a vast array of AM and service centers. Using smartphone applications has increased the potential for edge computation on smartphones to significantly aid in network traffic control. The development of an Artificial Intelligence (AI) - -based data analytic method poses a significant challenge due to the need to optimize for the limited computational capabilities of smartphones. However, the users’ demand for affordable technology renders it resistant to easy penetration. This paper uses IoT and AI to propose a Smart Health Monitoring System for Agricultural Machines with Deep Learning-based Optimization (SHMAM-DLO). This paper aims to propose a Fusion Genetic Algorithm (FGA) methodology and Artificial Neural Network (ANN) for optimization during monitoring the health of AM. The proposed approach enables cost-effective utilization of smartphone end devices by leveraging their built-in microphones instead of relying on expensive IoT sensors.

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Section: Literature Surveymentioning

confidence: 99%

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Shwetabh,

Ambhaikar

2024

BIO Web Conf.

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“…The sending of this framework, which depends on remote sensor network innovation, involves three essential advances: (1) information gathering utilizing sensors put in an agrarian field, (2) information cleaning and stockpiling, and (3) expectation handling using specific man-made intelligence methods. As per [7] the following stage in the development of savvy cultivating and rural practices is IoT-ML based agribusiness. With the utilization of the rural IoT, ML calculations might be applied to information gathered from different ranch contributions to make the framework more intelligent, offer indisputable data, and make forecasts.…”

Section: IImentioning

confidence: 99%

Soil Quality Monitoring and Crop Prediction Using IOT and Machine Learning

2023

IJRASET

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People are pressured for time in today's fast-paced environment, and technology assists them in a variety of ways. Agriculture is the primary industry in India. As a result, in the current environment, increasing output or production is critical. When it comes to determining certain chemical features of soil that have an immediate impact on plant growth, traditional farming faces various obstacles. Another critical factor to consider is the effect of timely water supply on plant output. The soil alkalinity and electrical conductivity are measured with PH electrodes. This study also looked into the real-time detection of soil variables and the location of chemical properties like pH, humidity, temperature, and soil moisture using a Raspberry Pi as a controller. Semi-supervised learning will be carried out in a methodical manner using the Q-learning algorithm. We used a range of samples in the experiment to show how to apply the indicated performance.

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“…Akter and Sofi [ 52 ] proposed integrating data analytics and machine learning into an IoT system to predict apple disease in apple orchards in the Kashmir Valley. Maduranga and Abeysekera [ 53 ] stated that IoT-based smart agriculture has attracted the interest of researchers who have used IoT and machine learning (ML) technologies to conduct groundbreaking research. The IoT’s primary objective in agriculture is to automate all agricultural operations and methods to maximise productivity.…”

Section: Iot and Machine Learningmentioning

confidence: 99%

A Review of Emerging Technologies for IoT-Based Smart Cities

Whaiduzzaman

Barros

Chanda

et al. 2022

Sensors

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Smart cities can be complemented by fusing various components and incorporating recent emerging technologies. IoT communications are crucial to smart city operations, which are designed to support the concept of a “Smart City” by utilising the most cutting-edge communication technologies to enhance city administration and resident services. Smart cities have been outfitted with numerous IoT-based gadgets; the Internet of Things is a modular method to integrate various sensors with all ICT technologies. This paper provides an overview of smart cities’ concepts, characteristics, and applications. We thoroughly investigate smart city applications, challenges, and possibilities with solutions in recent technological trends and perspectives, such as machine learning and blockchain. We discuss cloud and fog IoT ecosystems in the in capacity of IoT devices, architectures, and machine learning approaches. In addition we integrate security and privacy aspects, including blockchain applications, towards more trustworthy and resilient smart cities. We also highlight the concepts, characteristics, and applications of smart cities and provide a conceptual model of the smart city mega-events framework. Finally, we outline the impact of recent emerging technologies’ implications on challenges, applications, and solutions for futuristic smart cities.

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Machine Learning Applications in Iot Based Agriculture and Smart Farming: A Review

Cited by 40 publications

References 7 publications

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Smart Health Monitoring System of Agricultural Machines: Deep Learning-based Optimization with IoT and AI

Soil Quality Monitoring and Crop Prediction Using IOT and Machine Learning

A Review of Emerging Technologies for IoT-Based Smart Cities

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