An IoT Based Smart Farming System Using Machine Learning

Dahane, Amine; Benameur, Rabaie; Kechar, Bouabdellah; Benyamina, Abou El Hassan

doi:10.1109/isncc49221.2020.9297341

Cited by 45 publications

(18 citation statements)

References 12 publications

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“…Dahane and the joint authors (2020) in [24] established and experimented with a smart farming platform with artificial intelligence (SFP-AI) technology to provide forecasting potential. In [25], Gaikwad and others (2021) implemented a real-time monitoring farming system using IoT (RMFS-IoT) in high-resolution soil humidity, temperature, humidity, and temperature data at the agribusiness level.…”

Section: Relevant Research Work On Smart Farming and Information Mana...mentioning

confidence: 99%

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Pal¹,

VijayKumar²,

Akila³

et al. 2023

Computers, Materials &Amp; Continua

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Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies, including big data, the cloud, and the Internet of Things (IoT). Many researchers try to integrate IoT-based smart farming on cloud platforms effectively. They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes. Since IoT-cloud systems involve massive structured and unstructured data, data optimization comes into the picture. Hence, this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization (ICISF-DDO), which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime. Here, a conceptual framework of the proposed scheme and statistical design model has been well defined. The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization. The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%, reliability ratio of 98.63%, a coverage ratio of 99.67%, least sensor error rate of 8.96%, and efficient energy consumption ratio of 4.84%.

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Section: Relevant Research Work On Smart Farming and Information Mana...mentioning

confidence: 99%

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Pal¹,

VijayKumar²,

Akila³

et al. 2023

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…ANN and the Penman-Monteith equation have been utilized to predict daily evapotranspiration [128]. A smart irrigation system has been proposed using Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) based models in an Edge-Fog-Cloud setting [129]. Spatial water distribution has been predicted with ANN in [130] for a neuro-drip irrigation system.…”

Section: Smart Irrigationmentioning

confidence: 99%

Everything You wanted to Know about Smart Agriculture

Mitra¹,

Vangipuram²,

Bapatla³

et al. 2022

Preprint

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The world population is anticipated to increase by close to 2 billion by 2050 causing a rapid escalation of food demand. A recent projection shows that the world is lagging behind accomplishing the "Zero Hunger" goal, in spite of some advancements. Socio-economic and well being fallout will affect the food security. Vulnerable groups of people will suffer malnutrition. To cater to the needs of the increasing population, the agricultural industry needs to be modernized, become smart, and automated. Traditional agriculture can be remade to efficient, sustainable, eco-friendly smart agriculture by adopting existing technologies. In this survey paper the authors present the applications, technological trends, available datasets, networking options, and challenges in smart agriculture. How Agro Cyber Physical Systems are built upon the Internet-of-Agro-Things is discussed through various application fields. Agriculture 4.0 is also discussed as a whole. We focus on the technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) which support the automation, along with the Distributed Ledger Technology (DLT) which provides data integrity and security. After an in-depth study of different architectures, we also present a smart agriculture framework which relies on the location of data processing. We have divided open research problems of smart agriculture as future research work in two groups -from a technological perspective and from a networking perspective. AI, ML, the blockchain as a DLT, and Physical Unclonable Functions (PUF) based hardware security fall under the technology group, whereas any network related attacks, fake data injection and similar threats fall under the network research problem group.

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“…IoT is a revolutionary technology that reflects the future of communication and computing. This article is about implementing IoT-based smart farming energy in smart farm agriculture [7]. Crop management, resource management, lower-cost agriculture, enhanced quality and quantity, crop monitoring and field monitoring are all made possible by the IoT [12].…”

Section: Smart Farming In Iotmentioning

confidence: 99%

“…In addition, the integration of smart grid technologies that are new enables the smart farm to meet maximum standards regarding performance and environmental energy. With the progress of communication technologies, the automatic control of the indoor energy mate has found wide applications [7]. Although the initial objectives of growing in a smart farm environment were to provide a protected environment for the crop, the management of indoor energy has become a tool to guide crop production in the last five years [8].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Based Model for Energy Usage Peak Prediction in Smart Farms

Venkatesan

Lim

et al. 2022

Electronics

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Context: Energy utilization is one of the most closely related factors affecting many areas of the smart farm, plant growth, crop production, device automation, and energy supply to the same degree. Recently, 4th industrial revolution technologies such as IoT, artificial intelligence, and big data have been widely used in smart farm environments to efficiently use energy and control smart farms’ conditions. In particular, machine learning technologies with big data analysis are actively used as one of the most potent prediction methods supporting energy use in the smart farm. Purpose: This study proposes a machine learning-based prediction model for peak energy use by analyzing energy-related data collected from various environmental and growth devices in a smart paprika farm of the Jeonnam Agricultural Research and Extension Service in South Korea between 2019 and 2021. Scientific method: To find out the most optimized prediction model, comparative evaluation tests are performed using representative ML algorithms such as artificial neural network, support vector regression, random forest, K-nearest neighbors, extreme gradient boosting and gradient boosting machine, and time series algorithm ARIMA with binary classification for a different number of input features. Validate: This article can provide an effective and viable way for smart farm managers or greenhouse farmers who can better manage the problem of agricultural energy economically and environmentally. Therefore, we hope that the recommended ML method will help improve the smart farm’s energy use or their energy policies in various fields related to agricultural energy. Conclusion: The seven performance metrics including R-squared, root mean squared error, and mean absolute error, are associated with these two algorithms. It is concluded that the RF-based model is more successful than in the pre-others diction accuracy of 92%. Therefore, the proposed model may be contributed to the development of various applications for environment energy usage in a smart farm, such as a notification service for energy usage peak time or an energy usage control for each device.

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An IoT Based Smart Farming System Using Machine Learning

Cited by 45 publications

References 12 publications

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Everything You wanted to Know about Smart Agriculture

A Machine Learning Based Model for Energy Usage Peak Prediction in Smart Farms

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