Evaluation of an empirical equation to compute the nonlinearity parameter of a root water uptake model

Kumar, Navsal; Poddar, Arunava; Shankar, Vijay

doi:10.1002/ird.2781

Cited by 2 publications

(3 citation statements)

References 35 publications

(56 reference statements)

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“…(1) To conduct a comprehensive analysis of the factors influencing optimal irrigation scheduling and water management practices (2) To gain insight into AI and ML algorithms used for irrigation scheduling predictive models.…”

Section: Objectives Of Review Papermentioning

confidence: 99%

“…[1]) is essential to guaranteeing that water is used as efficiently as possible to maximize crop yield and minimize water waste. Conventional irrigation scheduling techniques frequently depend on empirical methodologies, which might not be as flexible in response to shifting crop needs and environmental conditions (Kumar, N. et al (2024). [2]).…”

Section: Introductionmentioning

confidence: 99%

“…Conventional irrigation scheduling techniques frequently depend on empirical methodologies, which might not be as flexible in response to shifting crop needs and environmental conditions (Kumar, N. et al (2024). [2]). But with to developments in machine learning and artificial intelligence, it may be possible to create predictive models (Baranyi, J. et.al.…”

Section: Introductionmentioning

confidence: 99%

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Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

H.,

Veeramanju

2024

IJMTS

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Purpose: Maintaining agricultural output, protecting water supplies, and lessening environmental effects all depend on effective water management. Through a comprehensive review of the literature and an in-depth analysis of various AI and ML techniques, this paper aims to put light on the cutting-edge approaches used in irrigation scheduling predictive modeling. The goal of the research is to determine the advantages, disadvantages, and future directions of AI and ML-based irrigation management systems by means of a methodical analysis of various algorithms, data sources, and applications. Additionally, the study seeks to demonstrate how data-driven methods can enhance irrigation systems' sustainability, accuracy, and precision. Stakeholders in agriculture, water resource management, and environmental conservation can make well-informed decisions to maximize irrigation scheduling techniques by having a thorough understanding of the theoretical underpinnings and practical applications of predictive models. The study also attempts to tackle issues like scalability, model interpretability, and lack of data when implementing AI and ML solutions for practical irrigation management. In final form, this review's conclusions advance our understanding of how to use AI and ML to improve agricultural systems' resilience and water use efficiency, supporting adaptive and sustainable water management strategies in the face of rising water scarcity concerns and climate change. Design/Methodology/Approach: In order to gather information for this review study, several research articles from reliable sources were analyzed and compared. Objective: To provide the current research gaps in prediction models for the best irrigation scheduling and water management, and suggest using AI and ML techniques to fill in these gaps. Results/ Findings: In response to the growing challenges of water scarcity and climate change, the paper's findings highlight the transformative potential of AI and ML techniques in optimizing irrigation scheduling, enhancing agricultural resilience, increasing water use efficiency, and supporting adaptive and sustainable water management strategies. Originality/Value: This paper's uniqueness and significance come from its thorough analysis of AI and ML approaches in predictive modeling for ideal water management and irrigation scheduling. It also provides insights into new methods and their possible effects on resource optimization and agricultural sustainability. Type of Paper: Literature Review.

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Section: Objectives Of Review Papermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

H.,

Veeramanju

2024

IJMTS

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Subsurface flow model for estimating 2D wetting pattern in drip irrigation

Reddy,

Singh

2024

CLEAN Soil Air Water

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Numerical model using 2D Richard’s equation in cylindrical polar coordinate system was developed to assess the soil wetting patterns in a drip irrigation system. A fully implicit finite‐difference scheme was used to solve equations with suitable initial and boundary conditions. The developed model was validated with an experimental result available in the literature, which shows a high level of concordance with low values of RMSE and R2 greater than 0.93 for all discharge rates. Furthermore, the versatility and applicability of the model were demonstrated for complex subsurface conditions considering homogeneous and heterogeneous soil profiles. Water ponding on the surface was observed in clay loam soil, contrasting with deep percolation in sandy loam soil. This underscores the significance of adjusting both discharge rate and irrigation frequency in accordance with prevailing soil conditions.

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Evaluation of an empirical equation to compute the nonlinearity parameter of a root water uptake model

Cited by 2 publications

References 35 publications

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Predictive Models for Optimal Irrigation Scheduling and Water Management: A Review of AI and ML Approaches

Subsurface flow model for estimating 2D wetting pattern in drip irrigation

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