Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

Glória, André; Cardoso, J. M. R.; Sebastião, Pedro

doi:10.3390/s21093079

Cited by 34 publications

(8 citation statements)

References 21 publications

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“…To analyze the collected data from an experimental setup for sustainable irrigation by Glória et al [30], a mobile application named "smart farm" has enhanced the possibility of farmers performing a task without being present on the farm. The application has the functionality of displaying the collected data in real-time, connected with the developed API using MQTT to remotely switch on/off actuators such as pumps on the farm.…”

Section: Mobile Applications For Smart Irrigation Managementmentioning

confidence: 99%

“…Some previous review works have investigated the current trend in the area of smart monitoring and control of irrigation [6,8,[21][22][23]. Numerous papers have explored the role of machine learning in enabling smart irrigation [14,[24][25][26][27][28][29][30]; these are summarized in Table 1. The majority of these existing works have focused on the application of supervised and unsupervised learning for smart irrigation systems.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Mobile Applications For Smart Irrigation Managementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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“…Consequently, high quality of life in a green economy will only be possible by effectively orchestrating a great wealth of fused information, which will be created from an immense number of smart sensor networks. Smart sensors are already in demand in diverse areas of utilization, including modern energy systems, energy-smart buildings, water resource management in urban and farming applications, healthcare operations, as well as in environmental air-quality monitoring and processed food improvement [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Testing Thermostatic Bath End-Scale Stability for Calibration Performance with a Multiple-Sensor Ensemble Using ARIMA, Temporal Stochastics and a Quantum Walker Algorithm

Besseris

2023

Sensors

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Thermostatic bath calibration performance is usually checked for uniformity and stability to serve a wide range of industrial applications. Particularly challenging is the assessment at the limiting specification ends where the sensor system may be less effective in achieving consistency. An ensemble of eight sensors is used to test temperature measurement stability at various topological locations in a thermostatic bath (antifreeze) fluid at −20 °C. Eight streaks of temperature data were collected, and the resulting time-series were processed for normality, stationarity, and independence and identical distribution by employing regular statistical inference methods. Moreover, they were evaluated for autoregressive patterns and other underlying trends using classical Auto-Regressive Integrated Moving Average (ARIMA) modeling. In contrast, a continuous-time quantum walker algorithm was implemented, using an available R-package, in order to test the behavior of the fitted coefficients on the probabilistic node transitions of the temperature time series dataset. Tracking the network sequence for persistence and hierarchical mode strength was the objective. The quantum walker approach favoring a network probabilistic framework was posited as a faster way to arrive at simultaneous instability quantifications for all the examined time-series. The quantum walker algorithm may furnish expedient modal information in comparison to the classical ARIMA modeling and in conjunction with several popular stochastic analyzers of time-series stationarity, normality, and data sequence independence of temperature end-of-scale calibration datasets, which are investigated for temporal consistency.

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“…Also, the algorithms depended on data acquisition with online weather data collection. In [11], researchers implemented a sustainable irrigation system based on a random forest (RF) algorithm. They evaluated the performance of the algorithm via a confusion matrix, and achieved an accuracy of 84.6%.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms to Classify Water Levels for Smart Irrigation Systems

Ali

2022

Journal of Engineering Research

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Agriculture is the main source of food. With the passing of time, there are dangers in order to preserve on the freshwater in agriculture sector. Thus, one of solutions to save the freshwater is enhancing the wastewater. Machine learning (ML) algorithms are used in several applications, such as smart irrigation, to reduce freshwater loss via building highperformance ML algorithms. This paper proposes four algorithms: support vector machine (SVM), decision tree (DT), SVM with Adaboost, and DT with Adaboost to classify water levels of sprinklers for smart irrigation. Here, five levels of water are classified-Max, High, Medium, Low, and Stop. The proposed algorithms are tested to obtain which algorithm achieves better performance and higher accuracy. Five steps sequentially are implemented on the used dataset via Pandas and Scikit-learn frameworks. The steps are preprocessing data, feature selection, feature scaling, training, and classification; to analyze the performance of the algorithms. The results showed that the DT algorithm with Adaboost is the best algorithm compared to the rest of the algorithms. The DT algorithm achieves an accuracy score of 0.912 with a shorter testing time of 2.2 seconds and mean square error (MSE) of 0.08.

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Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data

Cited by 34 publications

References 21 publications

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Testing Thermostatic Bath End-Scale Stability for Calibration Performance with a Multiple-Sensor Ensemble Using ARIMA, Temporal Stochastics and a Quantum Walker Algorithm

Machine Learning Algorithms to Classify Water Levels for Smart Irrigation Systems

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