Influence analysis of sprinkler irrigation effectiveness using ANFIS

Liang, Zhongwei; Liu, Xiaochu; Wen, Guilin; Yuan, Xue‐Feng

doi:10.25165/j.ijabe.20191205.5123

Cited by 4 publications

(3 citation statements)

References 40 publications

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“…In addition, a study by Navarro-Hellín et al [109] proposed a closed-loop irrigation control scheme, using ANFIS and PLSR as the reasoning and decision engine of the decision support system. A similar approach using ANFIS has been implemented to improve the performance of an irrigation sprinkler, leading to the realization of better infiltration equilibrium, soil moisture uniformity, and high water redistribution efficiency when tested experimentally [110].…”

Section: Adaptive Neuro-fuzzy Inference System (Anfis)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: Adaptive Neuro-fuzzy Inference System (Anfis)mentioning

confidence: 99%

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

et al. 2022

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“…To quantify the applicability and efficiency of the RSAE-ANFIS approach with alternative ones, such frequently used approaches as genetic optimization, simulated annealing-genetic algorithm (SA-GA), Taguchi parameter estimation, artificial neural network-simulated annealing (ANN-SA) prediction, and genetically optimized neural network (GONN) have been employed in the experimental conditions prearranged by Tables 1 and 2 [34][35][36][37][38]. Figure 9a-d showed the value comparisons of θ, e a , e t and C u between the predicted and actual measured results in tests A-K.…”

Section: Assessments Of Adaptive Prediction Qualitymentioning

confidence: 99%

Adaptive Prediction of Water Droplet Infiltration Effectiveness of Sprinkler Irrigation Using Regularized Sparse Autoencoder–Adaptive Network-Based Fuzzy Inference System (RSAE–ANFIS)

Liang

Liu

Zou

et al. 2021

Water

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As the high productive efficiency of sprinkler irrigation is largely based on balanced soil moisture distribution, it is essential to study the exact effectiveness of water droplet infiltration, which provides a theoretical basis for rationally scheduling the circulation efficiency of groundwater in agricultural irrigation performance. This research carried out adaptive prediction of the droplet infiltration effectiveness of sprinkler irrigation by using a novel approach of a regularized sparse autoencoder–adaptive network-based fuzzy inference system (RSAE–ANFIS), for the purpose of quantifying actual water droplet infiltration and effectiveness results of precision irrigation in various environmental conditions. The intelligent prediction experiment we implemented could be phased as: the demonstration of governing equations of droplet infiltration for sprinkler irrigation modeling; the measurement and computation of probability densities in water droplet infiltration; innovative establishment and working analysis of RSAE–ANFIS; and the adaptive prediction of infiltration effectiveness indexes, such as average soil moisture depth increment (θ, mm), irrigation infiltration efficiency (ea, %), irrigation turn duration efficiency (et, mm/min), and the uniformity coefficient of soil moisture infiltration (Cu, %), which were implemented to provide a comprehensive illustration for the effective scheduling of sprinkler irrigation. Result comparisons indicated that when jetting pressure (Pw) was 255.2 kPa, the impinge angle (Wa) was 42.5°, the water flow rate (Fa) was 0.67 kg/min, and continuous irrigation time (Tc) was 32.4 min (error tolerance = ±5%, the same as follows), thereby an optimum and stable effectiveness quality of sprinkler irrigation could be achieved, whereas average soil moisture depth increment (θ) was 57.6 mm, irrigation infiltration efficiency (ea) was 62.5%, irrigation turn duration efficiency (et) was 34.5 mm/min, and the uniformity coefficient of soil moisture infiltration (Cu) was 53.6%, accordingly. It could be concluded that the proposed approach of the regularized sparse autoencoder–adaptive network-based fuzzy inference system has outstanding predictive capability and possesses much better working superiority for infiltration effectiveness in accuracy and efficiency; meanwhile, a high agreement between the adaptive predicted and actual measured values of infiltration effectiveness could be obtained. This novel intelligent prediction system has been promoted constructively to improve the quality uniformity of sprinkler irrigation and, consequently, to facilitate the productive management of sprinkler irrigated agriculture.

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“…Evaluate the project decision-making, the organization planning, and the operation management [105,106] .…”

Section: Analytic Hierarchy Evaluationmentioning

confidence: 99%

Review of conceptual and systematic progress of precision irrigation

Liang

Liu

Xiao

et al. 2021

International Journal of Agricultural and Biological Engineering

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Precision irrigation, defined as accurate and appropriate agricultural techniques characterized by optimal management and best collaboration of various irrigation factors, attracts great attention and obtains wide employments in different irrigation conditions or cultivation processes. Moreover, it becomes well-established in major areas of agricultural researches and across the broad spectrum of agricultural techniques especially in specific sectors of scientific frontiers, including soil quality, irrigation scheduling, water resource distribution, crop productivity, tillage management, climate adaptation, and environment monitoring, etc. This paper reviews the research developments and integrated applications of precision irrigation in typical domains of mechanism and performance, covering key aspects such as process optimization, schedule modelling, and effectiveness evaluation, indicating that advanced irrigation optimization methods support higher productivity of crop field and better environmental conditions of soil; Current schedule modelling techniques provide a set of instructive demonstrations and heuristic descriptions for the working principles of precision irrigation and the quantitative assessments of irrigation productivity; The novel investigation on effectiveness evaluation is extremely significant to obtain higher infiltration efficiency, simultaneously to achieve the optimized irrigation qualities for water balance condition, soil water redistribution, and soil moisture uniformity so that the effectiveness quality of irrigation infiltration could be improved remarkably. It is concluded that precision irrigation owns an outstanding collaborating capability and possesses much better working advancement in typical calibration indexes of cultivation accuracy and infiltration efficiency, meanwhile, a high agreement between the predicted and actual irrigation effectiveness could be expected. This novel irrigation review concentrating on the conceptual and systematic progress should be promoted constructively to improve the quality uniformity for precision irrigation and its constructive influences in different applications, and to facilitate the integrated management of agricultural production by higher irrigation efficiency consequently.

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Influence analysis of sprinkler irrigation effectiveness using ANFIS

Cited by 4 publications

References 40 publications

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

Adaptive Prediction of Water Droplet Infiltration Effectiveness of Sprinkler Irrigation Using Regularized Sparse Autoencoder–Adaptive Network-Based Fuzzy Inference System (RSAE–ANFIS)

Review of conceptual and systematic progress of precision irrigation

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