An efficient IoT based smart farming system using machine learning algorithms

Rezk, Nermeen Gamal; Hemdan, Ezz El‐Din; Attia, Abdel-Fattah; El‐Sayed, Ayman; El-Rashidy, Mohamed A.

doi:10.1007/s11042-020-09740-6

Cited by 95 publications

(36 citation statements)

References 26 publications

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“…As is shown in Table 1, the work by Rezk et al [10] has most of the features considered in the comparative analysis, which means that it is the work that is most similar to our work. Nonetheless, unlike our proposal, which aims to predict volume of production of crops, in their work a classification model is proposed to classify crop productivity and drought.…”

Section: State Of the Artmentioning

confidence: 70%

“…A smart farming system using Internet of Things (IoT) technologies and machinelearning techniques for data mining is presented in [10]. It is based on an architecture comprising the following four layers: (a) a layer consisting of IoT sensors and actuators deployed in the farming field, (b) an edge-computing layer that provides integration between an IoT wireless sensor network and the cloud using IoT gateways, (c) a cloudcomputing layer that is intended to store and analyze data over cloud servers and (d) an end user mobile-and web-based application layer.…”

Section: State Of the Artmentioning

confidence: 99%

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Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain

et al. 2022

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Internet of Things (IoT) technologies can greatly benefit from machine-learning techniques and artificial neural networks for data mining and vice versa. In the agricultural field, this convergence could result in the development of smart farming systems suitable for use as decision support systems by peasant farmers. This work presents the design of a smart farming system for crop production, which is based on low-cost IoT sensors and popular data storage services and data analytics services on the cloud. Moreover, a new data-mining method exploiting climate data along with crop-production data is proposed for the prediction of production volume from heterogeneous data sources. This method was initially validated using traditional machine-learning techniques and open historical data of the northeast region of the state of Puebla, Mexico, which were collected from data sources from the National Water Commission and the Agri-food Information Service of the Mexican Government.

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Section: State Of the Artmentioning

confidence: 70%

Section: State Of the Artmentioning

confidence: 99%

Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain

et al. 2022

View full text Add to dashboard Cite

show abstract

“…As is shown in Table 1, the work by Rezk et al [10] has most of the features considered in the comparative analysis, which means that it is the work that is most similar to our work. Nonetheless, unlike our proposal, which aims to predict volume of production of crops, in that work a classification model is proposed to classify crop productivity and drought.…”

Section: State Of the Artmentioning

confidence: 70%

“…A smart farming system using Internet of Things (IoT) technologies and machine learning techniques for data mining is presented in [10]. It is based on an architecture comprising the following four layers: (a) a layer consisting of IoT sensors and actuators deployed in the farming field, (b) an edge computing layer that provides integration between an IoT wireless sensor network and the cloud using IoT gateways, (c) a cloud computing layer that is intended to store and analyze data over cloud servers and (d) an end user mobile and Web-based application layer.…”

Section: State Of the Artmentioning

confidence: 99%

“…Additionally, we focused on predicting the volume of corn grain production in the northeast region of the Mexican state of Puebla; corn grain is one of the most widely cultivated crops in the state of Puebla, Mexico, along with coffee beans and black beans. Conversely, Rezk et al [10] focused on classifying productivity and drought of four different crops that are widely cultivated in the state of Maharashtra, India, namely, bajra, soybean, jowar and sugarcan. Furthermore, unlike most of the works analyzed, our work proposes a data mining process that integrates two heterogeneous sources of crop production data and climate data.…”

Section: State Of the Artmentioning

confidence: 99%

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Internet of Things-Driven Data Mining for Smart Crop Production Prediction in the Peasant Farming Domain

Colombo-Mendoza¹,

Paredes-Valverde²,

Salas-Zárate³

et al. 2022

Preprint

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Internet of Things (IoT) technologies can greatly benefit from machine learning techniques and Artificial Neural Networks for data mining and vice versa. In the agricultural field, this convergence could result in the development of smart farming systems suitable for use as decision support systems by peasant farmers. This work presents the design of a smart farming system for crop production, which is based on low-cost IoT sensors and popular data storage services and data analytics services on the Cloud. Moreover, a new data mining method exploiting climate data along with crop production data is proposed for the prediction of production volume from heterogeneous data sources. This method was initially validated using traditional machine learning techniques and open historical data of the northeast region of the state of Puebla, Mexico, which were collected from data sources from the National Water Commission and the Agri-food Information Service of the Mexican Government.

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