Abstract
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Rohit Anand, Dattatray G. Bhalekar, Karishma Kumari, Roaf Ahmad Parray
Abstract
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A digital twin for smart farming
2019
Spectral data driven machine learning classification models for real-time leaf spot disease detection in brinjal crops
10.1016/j.eja.2024.127384 · 2024
A multimodal approach for enhanced disease management in cauliflower crops: integration of spectral sensors, machine learning models and targeted spraying technology
2024
Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks
10.1016/j.cj.2021.10.010 · 2022
Leveraging IoT and digital twins to monitor crop growth and health in agriculture
2024
Agrochemical spray technology adoption and safety awareness assessment in crop protection in vineyard cultivation
2024
Ultrasonic sensor-based automatic control volume sprayer for pesticides and growth regulators application in vineyards
10.1016/j.atech.2023.100232 · 2023
Unresolved referenced work
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10.31220/agrirxiv.2022.00165
10.31220/agrirxiv.2022.00165
Digital twin technology challenges and applications: a comprehensive review
Provenance
crossref
Confidence 100%
openalex
Confidence 95%
datacite
Confidence 0%
10.3390/rs14061335 · 2022
Multi-vegetation indices based handheld device for precise nitrogen assessment and prescription in direct seeded rice
10.1016/j.compag.2024.109886 · 2025
Digital twin system of pest management driven by data and model fusion
10.3390/agriculture14071099 · 2024
Optimized convolutional neural networks for real-time detection and severity assessment of early blight in tomato (Solanum lycopersicum L.)
10.1016/j.fgb.2025.103984 · 2025
Digital twins in agriculture: orchestration and applications
10.1021/acs.jafc.4c01934 · 2024
Water and sustainable agriculture
2011
Computer vision in smart agriculture and precision farming: techniques and applications
2024
Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems
2016
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IoT based smart water quality monitoring: recent techniques, trends and challenges for domestic applications
10.3390/w13131729 · 2021
Energy planning of pigsty using digital twin
2019
Application scenarios of digital twins for smart crop farming through cloud-fog-edge infrastructure
10.3390/fi16030100 · 2024
Business models for industrial smart services–the example of a digital twin for a product-service-system for potato harvesting
10.1016/j.procir.2019.04.114 · 2019
Spectral sensor‐based device for real‐time detection and severity estimation of groundnut bud necrosis virus in tomato
10.1002/rob.22391 · 2025
The multi-agent approach for developing a cyber-physical system for managing precise farms with digital twins of plants
10.35470/2226-4116-2019-8-4-257-261 · 2019
Automatic monitoring of lettuce fresh weight by multi-modal fusion based deep learning
10.3389/fpls.2022.980581 · 2022
Planning agricultural core road networks based on a digital twin of the cultivated landscape
2019
From bytes to farm: transferability of industrial digital twins in agricultural systems
10.1007/s42853-025-00256-1 · 2025
APSIM: a novel software system for model development, model testing and simulation in agricultural systems research
10.1016/0308-521x(94)00055-v · 1996
Digital twin for the future of orchard production systems
2020
Enhancing crop health through digital twin for disease monitoring and nutrient balance
10.35784/iapgos.5626 · 2024
A systematic review of IoT solutions for smart farming
10.3390/s20154231 · 2020
A digital twin approach for soil moisture measurement with physically based rendering simulations and machine learning
10.3390/electronics14020395 · 2025
Enhancing smart agriculture by implementing digital twins: a comprehensive review
10.3390/s23167128 · 2023
Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic
10.1016/j.scs.2021.103520 · 2022
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A revisit of internet of things technologies for monitoring and control strategies in smart agriculture
10.3390/agronomy12010127 · 2022
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The digital twin paradigm applied to soil quality assessment: a systematic literature review
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Development of models and methods for creating a digital twin of plant within the cyber-physical system for precision farming management
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A comprehensive review of digital twins technology in agriculture
10.3390/agriculture15090903 · doi-reference
Digital twins for additive manufacturing: a state-of-the-art review
10.3390/app10238350 · doi-reference
A digital advisor twin for crop nitrogen management
10.3390/agriculture12020302 · doi-reference
Digital twins in smart farming
10.1016/j.agsy.2020.103046 · doi-reference
RiceGrow: a rice growth and productivity model
10.1016/j.njas.2009.12.003 · doi-reference
Integrating digital twins and artificial intelligence multi-modal transformers into water resource management: overview and advanced predictive framework
10.3390/ai5040098 · doi-reference
Development of models and methods for creating a digital twin of plant within the cyber-physical system for precision farming management
10.1088/1742-6596/1703/1/012022 · doi-reference
The digital twin paradigm applied to soil quality assessment: a systematic literature review
10.3390/s23021007 · doi-reference
A revisit of internet of things technologies for monitoring and control strategies in smart agriculture
10.3390/agronomy12010127 · doi-reference
Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic
10.1016/j.scs.2021.103520 · doi-reference
Enhancing smart agriculture by implementing digital twins: a comprehensive review
10.3390/s23167128 · doi-reference
A digital twin approach for soil moisture measurement with physically based rendering simulations and machine learning
10.3390/electronics14020395 · doi-reference
A systematic review of IoT solutions for smart farming
10.3390/s20154231 · doi-reference
Enhancing crop health through digital twin for disease monitoring and nutrient balance
10.35784/iapgos.5626 · doi-reference
APSIM: a novel software system for model development, model testing and simulation in agricultural systems research
10.1016/0308-521x(94)00055-v · doi-reference
From bytes to farm: transferability of industrial digital twins in agricultural systems
10.1007/s42853-025-00256-1 · doi-reference
Automatic monitoring of lettuce fresh weight by multi-modal fusion based deep learning
10.3389/fpls.2022.980581 · doi-reference
The multi-agent approach for developing a cyber-physical system for managing precise farms with digital twins of plants
10.35470/2226-4116-2019-8-4-257-261 · doi-reference
Spectral sensor‐based device for real‐time detection and severity estimation of groundnut bud necrosis virus in tomato
10.1002/rob.22391 · doi-reference
Business models for industrial smart services–the example of a digital twin for a product-service-system for potato harvesting
10.1016/j.procir.2019.04.114 · doi-reference
Application scenarios of digital twins for smart crop farming through cloud-fog-edge infrastructure
10.3390/fi16030100 · doi-reference
IoT based smart water quality monitoring: recent techniques, trends and challenges for domestic applications
10.3390/w13131729 · doi-reference
Digital twins in agriculture: orchestration and applications
10.1021/acs.jafc.4c01934 · doi-reference
Optimized convolutional neural networks for real-time detection and severity assessment of early blight in tomato (Solanum lycopersicum L.)
10.1016/j.fgb.2025.103984 · doi-reference
Digital twin system of pest management driven by data and model fusion
10.3390/agriculture14071099 · doi-reference
Multi-vegetation indices based handheld device for precise nitrogen assessment and prescription in direct seeded rice
10.1016/j.compag.2024.109886 · doi-reference
Digital twin technology challenges and applications: a comprehensive review
10.3390/rs14061335 · doi-reference
10.31220/agrirxiv.2022.00165
10.31220/agrirxiv.2022.00165 · doi-reference
Ultrasonic sensor-based automatic control volume sprayer for pesticides and growth regulators application in vineyards
10.1016/j.atech.2023.100232 · doi-reference
Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks
10.1016/j.cj.2021.10.010 · doi-reference
Spectral data driven machine learning classification models for real-time leaf spot disease detection in brinjal crops
10.1016/j.eja.2024.127384 · doi-reference