Forecasting daily evapotranspiration using artificial neural networks for sustainable irrigation scheduling

Farooque, Aitazaz A.; Afzaal, Hassan; Abbas, Farhat; Bos, Melanie; Maqsood, Junaid; Wang, Xiuquan; Hussain, Nazar

doi:10.1007/s00271-021-00751-1

Cited by 25 publications

(15 citation statements)

References 47 publications

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“…Forecasts of evapotranspiration (ETo) can help with irrigation scheduling and water resource management. For forecast ETo, three cutting-edge deep learning algorithms were tested: long short-term memory (LSTM), convolutional LSTM (ConvLSTM), and one-dimensional CNN (1D-CNN) [49]. Table 1 represent different smart scheduling irrigation systems from the type of crop and scale that it made on also the benefits of each one.…”

Section: A Artificial Intelligence Irrigation Scheduling Systemmentioning

confidence: 99%

Smart Irrigation Systems: Overview

et al. 2024

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Countries are collaborating to make agriculture more efficient by combining new technologies to improve its procedure. Improving irrigation efficiency in agriculture is thus critical for the survival of sustainable agricultural production. Smart irrigation methods can enhance irrigation efficiency, specially with the introduction of wireless communication systems, monitoring devices, and enhanced control techniques for efficient irrigation scheduling. The study compared on a wide range of study subjects to investigate scientific approaches for smart irrigation. As a result, this project included a wide range of topics related to irrigation methods, decision-making, and technology used. Information was gathered from a variety of scientific papers. So, our research relied on several published documents, the majority of which were published during the last four years, and authors from all over the world. In the meantime, various irrigation initiatives were given special attention. Following that, the evaluation focuses on the key components of smart irrigation, such as real-time irrigation scheduling, IoT, the importance of an internet connection, smart sensing, and energy harvesting.INDEX TERMS Smart irrigation, Soil monitoring, Smart agriculture, IoT, Energy harvesting.• Pressurized distribution: The pressurized systems'main components are a trickle, sprinkler, and array of the same systems, where water is carried and spread along the land surface within networks of pressurized pipes. Besides, many individual systems configurations are presented by novel features, such as centre-pivot sprinkler systems. • Gravity-flow distribution: Systems based on gravityflow carry and distribute the water at the field level through the overland, free-surface flow regime. These mentioned surface irrigation methods are split based on the operational specifications and configuration. • Drainage flow distribution: An irrigation system using drainage control-sub-irrigation is not commonly used.

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Section: A Artificial Intelligence Irrigation Scheduling Systemmentioning

confidence: 99%

Smart Irrigation Systems: Overview

et al. 2024

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“…water stress ML/DL pred. [17] various air temperature, solar radiation, relative humidity, and wind speed water stress DL pred.…”

Section: Referencementioning

confidence: 99%

Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

et al. 2023

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“…Machine learning approaches have more recently been applied in estimation of evapotranspiration, and hence plant water requirements, based on weather data (Sidhu et al, 2020;Farooque et al, 2021) estimate evapotranspiration values, which are applied in precision scheduling of irrigation to achieve targeted optimal combinations of yield and irrigation amount selected by the farmer (Linker et al, 2018). Estimation of evapotranspiration using remote sensing data is employed in Barker et al (Barker et al, 2018) for variable rate irrigation control.…”

Section: Atmosphere-based Approachesmentioning

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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Forecasting daily evapotranspiration using artificial neural networks for sustainable irrigation scheduling

Cited by 25 publications

References 47 publications

Smart Irrigation Systems: Overview

Smart Irrigation Systems: Overview

Classification and Forecasting of Water Stress in Tomato Plants Using Bioristor Data

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

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