Machine learning assisted water management strategy on a self-sustaining seawater desalination and vegetable cultivation platform

Jiao, Long; Luo, Xiao; Zha, Lingyan; Bao, Hua; Zhang, Jingjin; Gu, Xiaokun

doi:10.1016/j.compag.2023.108569

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…An interesting application of ML concerns vegetable cultivation in the absence of freshwater and soil by using a self-sustaining platform. ML was used to predict water consumption and rationalize its use [75]. The analysis of vegetation indices, essential to crop management, is another example of ML application in precision agriculture [76][77][78].…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

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Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data.

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Section: Advantages Disadvantagesmentioning

confidence: 99%

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

Centorame,

Gasperini,

Ilari

et al. 2024

Agronomy

View full text Add to dashboard Cite

show abstract

A Sustainable Holistic Approach of Hydroponic Farming for Reclaiming, and Rehabilitating Wastewater: A Review

Nazir,

Roy,

Saha

et al. 2024

Water Air Soil Pollut

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Energy-saving analysis of desalination equipment based on a machine-learning sequence modeling

Zhang,

Jiang,

et al. 2024

Energy Harvesting and Systems

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To control water quality and seawater desalination dosage, modeling the coagulation process of saltwater is crucial. With a focus on the features of seawater coagulation with a long lag, a machine-learning sequence-based modeling approach is suggested. The link between influent and effluent turbidities, flow rates, flocculant and coagulant dosages, and other parameters is modeled using structured units such as a gate recurrent unit encoder and a linear network decoder. The model’s validity is confirmed by numerical experiments based on real operating data, which also offer a solid foundation for managing flocculant and coagulant assistance reduction.

show abstract

Machine learning assisted water management strategy on a self-sustaining seawater desalination and vegetable cultivation platform

Cited by 3 publications

References 39 publications

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

A Sustainable Holistic Approach of Hydroponic Farming for Reclaiming, and Rehabilitating Wastewater: A Review

Energy-saving analysis of desalination equipment based on a machine-learning sequence modeling

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