Smart Irrigation IoT Solution using Transfer Learning for Neural Networks

Risheh, Ali; Jalili, A.; Nazerfard, Ehsan

doi:10.1109/iccke50421.2020.9303612

Cited by 10 publications

(10 citation statements)

References 20 publications

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“…On the other hand, the most common method to establish M2C communication in SF is to connect the microcontroller to the local wireless network connected to the internet. Alternatively, some papers use Hypertext Transfer Protocol (HTTP) [89,162], Message Queue Telemetry Transport (MQTT) [97,163,164], or Constrained Application Protocol (CoAP) [165], TCP/IP protocols used to establish M2C communication, known as IoT protocols.…”

Section: ) M2c Communication Protocolsmentioning

confidence: 99%

“…Model-based algorithms take actions in accordance with a reference model, which can be based on scientific equations [101,292,293], or on previous data (i.e. machine learning models) [163,239,268,[294][295][296]. The fuzzy-based algorithm is preferred over other control algorithms as it enables multiple control variables without using more complicated mathematical or database models.…”

Section: B Autonomous Systemsmentioning

confidence: 99%

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Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

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Section: ) M2c Communication Protocolsmentioning

confidence: 99%

Section: B Autonomous Systemsmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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“…The system recommended performed admirably on both its own gathered data set and the accessible crop data collection. Risheh et al (2020) using artificial neural networks and an IoT architecture, created a dependable system for greenhouse irrigation and demonstrate the superior performance of neural networks compared to the current alternative method of support vector regression using a dataset gathered by conducting tests on various soils also used transfer learning technique to reduce the processing power and speed up the training. Mehra et al (2020) proposed an intelligent IoT-based hydroponic system using Deep Neural Networks.…”

Section: Challenges In Adopting Precision Agriculture In Indiamentioning

confidence: 99%

Automatic Irrigation Model Powered by Smart Rain Prediction Device in India

Geeta¹,

Barnali²

2023

jae

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This paper presents a simple rain prediction device-based automatic irrigation management algorithm using a combination of weather parameters and soil moisture measurements for the water balance required for a crop at each condition during its growing phase that will reduce farmer intervention for irrigation and avoid unnecessary irrigation by predicting the rainfall before starting the motor for irrigating the field. This device is powered by various technologies like deep learning to classify clouds responsible for rain, machine learning models to predict rainfall based on atmospheric parameters and the Internet of Things (IoT) using different sensors to collect data from the field. This algorithm is very appropriate for farmers who are in remote locations and are not able to use the internet and WIFI due to its unavailability. The device will be attached to the motor, will take the data from sensors and will do the rain prediction at device level only and will switch ON/OFF the motor based on the soil moisture value and rain prediction without any human intervention.

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“…This was made possible through the integration of GA, ANN, and Bayesian networks, with model performance assessed in terms of the coefficient of determination (R 2 ) and standard prediction error of 96% and 8.7%, respectively. Further investigation of the applicability of ANN for smart irrigation with IoT integration was implemented by Risheh et al [103], using a transfer learning approach to address the limitations of ANN, such as the high number of dataset requirements and the need for high training of the network, resulting in high processing complexity.…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

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Smart Irrigation IoT Solution using Transfer Learning for Neural Networks

Cited by 10 publications

References 20 publications

Technologies Driving the Shift to Smart Farming: A Review

Technologies Driving the Shift to Smart Farming: A Review

Automatic Irrigation Model Powered by Smart Rain Prediction Device in India

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

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