Towards a Smart Irrigation Scheduling System Through Massive Data and Predictive Models

Mezouari, Asmae El; Najib, Mehdi; Fazziki, Abdelaziz El

doi:10.1007/978-3-030-36664-3_42

Cited by 3 publications

(2 citation statements)

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“…Through the integration of prediction concepts in irrigation system management, dynamic changes in environmental parameters can be anticipated through training and adaptation using predictive models. This was investigated in a study where extreme gradient boosting and autoregressive moving-average models were trained, using data stored in a dedicated IoT-enabled database, for the prediction of weather and environmental parameters, in order to guide the farmer's decision as to when to commence or stop irrigation [54].…”

Section: Random Forest (Rf)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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Section: Random Forest (Rf)mentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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“…The vast quantities of data generated from networked sensors generate a need for expanded processing and storage capabilities. In this respect, cloud computing approaches provide a viable solution, finding application in monitoring of real-time irrigation status (López-Riquelme et al, 2017;Vaishali et al, 2017) and modeling of plant water requirements for soil-based (Raikar et al, 2018;Mezouari et al, 2020), plant-based (Roopaei et al, 2017), and atmosphere-based (Bendre et al, 2015) precision irrigation control approaches. Data analysis techniques applied on Big Data applications are also proving beneficial in management of precision irrigation control systems (Zhang et al, 2017).…”

Section: Big Datamentioning

confidence: 99%

How much is enough in watering plants? State-of-the-art in irrigation control: Advances, challenges, and opportunities with respect to precision irrigation

Owino

Söffker²

2022

Front. Control Eng.

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With a rapidly expanding global population placing an ever growing demand on freshwater resources, an increased focus on irrigation techniques tailored to the specific needs of plant appears as one solution to minimize overall freshwater consumption. Precision irrigation methods seek to realize an acceptable compromise between yield and irrigation water consumption through control of the timing and quantity of water supplied to plants. The goal is to maintain the water content of the soil, achieve specific water use efficiency with regard to yield or maintain the physiological response of the plant to water stress within predetermined limits. Reliance on soil moisture measurements to establish irrigation water demand inadequately addresses heterogenous distribution of water in soil. Growing research interest is observed detailing the determination of plant water status directly from physiological responses. This paper reviews irrigation control approaches based on different plant water status assessment techniques. A distinct focus is made on application scale of the discussed control approaches, an aspect that has not been considered intensively enough in previous discussions of irrigation control approaches. A discussion of the observed strengths and shortcomings and technological advances supporting the various methods used to quantify plant water status extends the review. Emerging trends that are likely to have an impact on plant water status determination and optimal timing and quantification of irrigation water requirements are integrated to show latest results. A peek into the future of precision irrigation foresees greater reliance on plant-based signals, both in characterization of the control variable, namely the plant water status, and in generation of controller outputs in terms of quantity and timing.

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Computerized Irrigation Scheduling

Koné,

Grati,

Bouaziz

et al. 2023

2023 20th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA)

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Towards a Smart Irrigation Scheduling System Through Massive Data and Predictive Models

Cited by 3 publications

References 11 publications

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

How much is enough in watering plants? State-of-the-art in irrigation control: Advances, challenges, and opportunities with respect to precision irrigation

Computerized Irrigation Scheduling

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