An ensemble machine learning approach for determination of the optimum sampling time for evapotranspiration assessment from high-throughput phenotyping data

Kar, Soumyashree; Purbey, Vikram Kumar; Suradhaniwar, Saurabh; Korbu, Lijalem; Kholová, Jana; Durbha, Surya S.; Adinarayana, J.; Vadez, Vincent

doi:10.1016/j.compag.2021.105992

Cited by 24 publications

(10 citation statements)

References 60 publications

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“…Two-layer ensemble model is build with RF, SVR, MLP, LR and KNN models and found higher accuracy in terms of R ranged from (0.66 to 0.99) as compared to empirical models [ 88 ]. Another, ensemble based model is build with ANN, SVM and RF to estimate the ET with geno-types and optimize the ET with time series data, and found the correct results [ 89 ].…”

Section: Literature Of Irrigation Schedulingmentioning

confidence: 99%

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches

Saggi

Jain

2022

Arch Computat Methods Eng

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From last decade, Big data analytics and machine learning is a hotspot research area in the domain of agriculture. Agriculture analytics is a data intensive multidisciplinary problem. Big data analytics becomes a key technology to perform analysis of voluminous data. Irrigation water management is a challenging task for sustainable agriculture. It depends on various parameters related to climate, soil and weather conditions. For accurate estimation of requirement of water for a crop a strong modeling is required. This paper aims to review the application of big data based decision support system framework for sustainable water irrigation management using intelligent learning approaches. We examined how such developments can be leveraged to design and implement the next generation of data, models, analytics and decision support tools for agriculture irrigation water system. Moreover, water irrigation management need to rapidly adapt state-of-the-art using big data technologies and ICT information technologies with the focus of developing application based on analytical modeling approach. This study introduces the area of research, including a irrigation water management in smart agriculture, the crop water model requirement, and the methods of irrigation scheduling, decision support system, and research motivation.

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Section: Literature Of Irrigation Schedulingmentioning

confidence: 99%

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches

Saggi

Jain

2022

Arch Computat Methods Eng

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“…Phenotyping transpiration dynamics in a field-based lysimeter system, with realistic VPD and progressive soil drying captures the aggregate phenotype of interest as well as component phenes (Vadez et al, 2015;Kar et al, 2020Kar et al, , 2021. While field-based lysimeters have significant construction and operating costs, they have demonstrated utility for both trait-based and QTL-based selection (Kholová et al, 2012;Karthika et al, 2019).…”

Section: Direct Selection For Transpiration Restrictionmentioning

confidence: 99%

Optimizing Crop Water Use for Drought and Climate Change Adaptation Requires a Multi-Scale Approach

2022

Self Cite

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Selection criteria that co-optimize water use efficiency and yield are needed to promote plant productivity in increasingly challenging and variable drought scenarios, particularly dryland cereals in the semi-arid tropics. Optimizing water use efficiency and yield fundamentally involves transpiration dynamics, where restriction of maximum transpiration rate helps to avoid early crop failure, while maximizing grain filling. Transpiration restriction can be regulated by multiple mechanisms and involves cross-organ coordination. This coordination involves complex feedbacks and feedforwards over time scales ranging from minutes to weeks, and from spatial scales ranging from cell membrane to crop canopy. Aquaporins have direct effect but various compensation and coordination pathways involve phenology, relative root and shoot growth, shoot architecture, root length distribution profile, as well as other architectural and anatomical aspects of plant form and function. We propose gravimetric phenotyping as an integrative, cross-scale solution to understand the dynamic, interwoven, and context-dependent coordination of transpiration regulation. The most fruitful breeding strategy is likely to be that which maintains focus on the phene of interest, namely, daily and season level transpiration dynamics. This direct selection approach is more precise than yield-based selection but sufficiently integrative to capture attenuating and complementary factors.

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“…There has been a surge in the use of these methods in the domain of agriculture [240][241][242][243][244][245][246][247]. Recent research has been directed towards using bagging [248][249][250][251] and boosting [186].…”

Section: Ensemble Modelsmentioning

confidence: 99%

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

Badgujar¹,

Figueroa²,

Das³

et al. 2022

Preprint

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Soil working tools, implements, and machines are inevitable in mechanized agriculture. The soil-tool/machine interaction is a multivariate, dynamic, and intricate process. The accurate interpretation, description, and modeling of a soil-machine interaction is key to providing a solution to sustainable crop production by reducing energy input, excessive soil pulverization, and compaction. The traditional method provides insight into soil-machine interaction but often provides inadequate solutions and lacks broad applicability. Computational intelligence (CI) is a comprehensive class of approaches that rely on approximate information to solve complex problems. The CI method has been extensively studied and applied in soil tillage and traction domain in recent decades. The study critically reviews the CI techniques implemented in soil-machine interactions, especially in the context of tillage, traction, and compaction. The traditional methods and their limitation are discussed. The fundamental of CI methods and a detailed overview of the most popular methods are provided. The study reviews and summarizes the 50 selected articles on soil-machine interaction studies where CI methods were employed. It discusses the strength and limitations of employed CI methods. It also suggests the emergent CI methods and future applications are discussed. The outlined study would serve as a concise reference and a quick and systematic way to understand the applicable CI methods that allow crucial farm management decision-making.

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An ensemble machine learning approach for determination of the optimum sampling time for evapotranspiration assessment from high-throughput phenotyping data

Cited by 24 publications

References 60 publications

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches

A Survey Towards Decision Support System on Smart Irrigation Scheduling Using Machine Learning approaches

Optimizing Crop Water Use for Drought and Climate Change Adaptation Requires a Multi-Scale Approach

Application of Computational Intelligence Methods in Agricultural Soil-Machine Interaction : A Review

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