Research graph
References from Real-world applications of smart technologies in protected farming. Local targets link to admitted publications; unresolved targets remain external evidence.
An intelligent monitoring model for greenhouse microclimate based on RBF neural network for optimal setpoint detection
10.1016/j.jprocont.2023.103037 · 2023 · External reference
A comprehensive review on deep learning assisted computer vision techniques for smart greenhouse agriculture
10.1109/access.2024.3349418 · 2024 · External reference
Development of an end-effector for robotic harvesting of hydroponic lettuce
10.13031/ja.16269 · 2025 · External reference
A field‐tested robotic harvesting system for iceberg lettuce
10.1002/rob.21888 · 2020 · External reference
10.32473/edis-fe1041-2018
10.32473/edis-fe1041-2018 · External reference
Albumentations: fast and flexible image augmentations
10.3390/info11020125 · 2020 · External reference
Unresolved reference
2010 · External reference
Unresolved reference
External reference
Smart control models used for nutrient management in hydroponic crops: a systematic review
10.1109/access.2025.3526171 · 2025 · External reference
Integrating neural network for pest detection in controlled environment vertical farm
10.17485/ijst/v15i17.353 · 2022 · External reference
Agricultural robotics: unmanned robotic service units in agricultural tasks
10.1109/mie.2013.2252957 · 2013 · External reference
Integrating reinforcement learning and large language models for crop production process management optimization and control through a new knowledge-based deep learning paradigm
10.1016/j.compag.2025.110028 · 2025 · External reference
NeRF-based 3D reconstruction pipeline for acquisition and analysis of tomato crop morphology
10.3389/fpls.2024.1439086 · 2024 · External reference
Xception: deep learning with depthwise separable convolutions
2017 · External reference
Unresolved reference
2018 · External reference
Decision support systems and models for aiding irrigation and nutrient management of vegetable crops
10.1016/j.agwat.2020.106209 · 2020 · External reference
A meta-analysis: food production and vegetable crop yields of hydroponics
10.1016/j.scienta.2023.112339 · 2023 · External reference
Evaluation of end effectors for robotic harvesting of mango fruit
10.3390/su15086769 · 2023 · External reference
Exploiting pre-trained convolutional neural networks for the detection of nutrient deficiencies in hydroponic basil
10.3390/s23125407 · 2023 · External reference
Energy optimization and plant comfort management in smart greenhouses using the artificial bee colony algorithm
10.1038/s41598-024-84141-5 · 2025 · External reference
Vulnerability of California specialty crops to projected mid-century temperature changes
10.1007/s10584-017-2011-3 · 2018 · External reference
Vulnerability of specialty crops to short-term climatic variability and adaptation strategies in the Midwestern USA
10.1007/s10584-017-2066-1 · 2018 · External reference
Unresolved reference
2021 · External reference
Comparison of land, water, and energy requirements of lettuce grown using hydroponic vs. conventional agricultural methods
10.3390/ijerph120606879 · 2015 · External reference
An artificial intelligence-powered environmental control system for resilient and efficient greenhouse farming
10.3390/su162410958 · 2024 · External reference
Direct and indirect measurements of LAI in millet and fallow vegetation in HAPEX-Sahel
10.1016/s0168-1923(98)00092-6 · 1999 · External reference
Greenhouse environment modeling and simulation for microclimate control
10.1016/j.compag.2019.04.013 · 2019 · External reference
Review of LiDAR sensor data acquisition and compression for automotive applications
2018 · External reference
Systematic approach to validate and implement digital phenotyping tool for soybean: a case study with PlantEye
10.1002/ppj2.20025 · 2021 · External reference
Incorporating artificial intelligence technology in smart greenhouses: current State of the Art
10.3390/app13010014 · 2022 · External reference
V-Net: fully convolutional neural networks for volumetric medical image segmentation
2016 · External reference
An Economic and Environmental Comparison of Conventional and Controlled Environment Agriculture (CEA) Supply Chains for Leaf Lettuce to US Cities
2020 · External reference
Deep learning in controlled environment agriculture: a review of recent advancements, challenges and prospects
10.3390/s22207965 · 2022 · External reference
Neural network model for greenhouse microclimate predictions
10.3390/agriculture12060780 · 2022 · External reference
Digital Twins in agriculture: challenges and opportunities for environmental sustainability
10.1016/j.cosust.2022.101252 · 2023 · External reference
IoT-equipped and AI-enabled next generation smart agriculture: a critical review, current challenges and future trends
10.1109/access.2022.3152544 · 2022 · External reference
10.2139/ssrn.5142250
10.2139/ssrn.5142250 · External reference
RGB cams vs RGB-D sensors: low cost motion capture technologies performances and limitations
10.1016/j.jmsy.2014.07.011 · 2014 · External reference
A system for the monitoring and predicting of data in precision agriculture in a rose greenhouse based on wireless sensor networks
10.1016/j.procs.2017.11.042 · 2017 · External reference
Application of an image and environmental sensor network for automated greenhouse insect pest monitoring
10.1016/j.aspen.2019.11.006 · 2020 · External reference
Peduncle detection of sweet pepper for autonomous crop harvesting—combined color and 3-D information
10.1109/lra.2017.2651952 · 2017 · External reference
MobileNetV2: inverted residuals and linear bottlenecks
2018 · External reference
Edge-AI based plant leaf disease identification and prevention for smart agriculture
2024 · External reference
Greenhouse farming and employment: evidence from Ecuador
2023 · External reference
Automation and digitization of agriculture using artificial intelligence and internet of things
2021 · External reference
Robotic applications in the automation of agricultural production under greenhouse: a review
2017 · External reference
Information acquisition of cucumber fruit in greenhouse environment based on nearing infrared image
2009 · External reference
Combination of multivariate standard addition technique and deep kernel learning model for determining multi-ion in hydroponic nutrient solution
10.3390/s20185314 · 2020 · External reference
Large language models and agricultural extension services
10.1038/s43016-023-00867-x · 2023 · External reference
Digital Twin: generalization, characterization and implementation
10.1016/j.dss.2021.113524 · 2021 · External reference
Online recognition and yield estimation of tomato in plant factory based on YOLOv3
10.1038/s41598-022-12732-1 · 2022 · External reference
Design of a control system for a mini-automatic transplanting machine of plug seedling
10.1016/j.compag.2020.105226 · 2020 · External reference
Design and experiment of low damage flexible harvesting device for hydroponic lettuce
2022 · External reference
Research on flexible end-effectors with humanoid grasp function for small spherical fruit picking
10.3390/agriculture13010123 · 2023 · External reference
Density Weighted Connectivity of Grass Pixels in image frames for biomass estimation
10.1016/j.eswa.2018.01.055 · 2018 · External reference
A novel strategy for pest disease detection of Brassica chinensis based on UAV imagery and deep learning
10.1080/01431161.2022.2155082 · 2022 · External reference
Multiple disease detection method for greenhouse-cultivated strawberry based on multiscale feature fusion Faster R_CNN
10.1016/j.compag.2022.107176 · 2022 · External reference
Systematic approach to validate and implement digital phenotyping tool for soybean: a case study with PlantEye
10.1002/ppj2.20025 · ExternalCitation · doi-reference
A field‐tested robotic harvesting system for iceberg lettuce
10.1002/rob.21888 · ExternalCitation · doi-reference
Vulnerability of California specialty crops to projected mid-century temperature changes
10.1007/s10584-017-2011-3 · ExternalCitation · doi-reference
Vulnerability of specialty crops to short-term climatic variability and adaptation strategies in the Midwestern USA
10.1007/s10584-017-2066-1 · ExternalCitation · doi-reference
Decision support systems and models for aiding irrigation and nutrient management of vegetable crops
10.1016/j.agwat.2020.106209 · ExternalCitation · doi-reference
Application of an image and environmental sensor network for automated greenhouse insect pest monitoring
10.1016/j.aspen.2019.11.006 · ExternalCitation · doi-reference
Greenhouse environment modeling and simulation for microclimate control
10.1016/j.compag.2019.04.013 · ExternalCitation · doi-reference
Design of a control system for a mini-automatic transplanting machine of plug seedling
10.1016/j.compag.2020.105226 · ExternalCitation · doi-reference
Multiple disease detection method for greenhouse-cultivated strawberry based on multiscale feature fusion Faster R_CNN
10.1016/j.compag.2022.107176 · ExternalCitation · doi-reference
Integrating reinforcement learning and large language models for crop production process management optimization and control through a new knowledge-based deep learning paradigm
10.1016/j.compag.2025.110028 · ExternalCitation · doi-reference
Digital Twins in agriculture: challenges and opportunities for environmental sustainability
10.1016/j.cosust.2022.101252 · ExternalCitation · doi-reference
Digital Twin: generalization, characterization and implementation
10.1016/j.dss.2021.113524 · ExternalCitation · doi-reference
Density Weighted Connectivity of Grass Pixels in image frames for biomass estimation
10.1016/j.eswa.2018.01.055 · ExternalCitation · doi-reference
RGB cams vs RGB-D sensors: low cost motion capture technologies performances and limitations
10.1016/j.jmsy.2014.07.011 · ExternalCitation · doi-reference
An intelligent monitoring model for greenhouse microclimate based on RBF neural network for optimal setpoint detection
10.1016/j.jprocont.2023.103037 · ExternalCitation · doi-reference
A system for the monitoring and predicting of data in precision agriculture in a rose greenhouse based on wireless sensor networks
10.1016/j.procs.2017.11.042 · ExternalCitation · doi-reference
A meta-analysis: food production and vegetable crop yields of hydroponics
10.1016/j.scienta.2023.112339 · ExternalCitation · doi-reference
Direct and indirect measurements of LAI in millet and fallow vegetation in HAPEX-Sahel
10.1016/s0168-1923(98)00092-6 · ExternalCitation · doi-reference
Online recognition and yield estimation of tomato in plant factory based on YOLOv3
10.1038/s41598-022-12732-1 · ExternalCitation · doi-reference
Energy optimization and plant comfort management in smart greenhouses using the artificial bee colony algorithm
10.1038/s41598-024-84141-5 · ExternalCitation · doi-reference
Large language models and agricultural extension services
10.1038/s43016-023-00867-x · ExternalCitation · doi-reference
A novel strategy for pest disease detection of Brassica chinensis based on UAV imagery and deep learning
10.1080/01431161.2022.2155082 · ExternalCitation · doi-reference
IoT-equipped and AI-enabled next generation smart agriculture: a critical review, current challenges and future trends
10.1109/access.2022.3152544 · ExternalCitation · doi-reference
A comprehensive review on deep learning assisted computer vision techniques for smart greenhouse agriculture
10.1109/access.2024.3349418 · ExternalCitation · doi-reference
Smart control models used for nutrient management in hydroponic crops: a systematic review
10.1109/access.2025.3526171 · ExternalCitation · doi-reference
Peduncle detection of sweet pepper for autonomous crop harvesting—combined color and 3-D information
10.1109/lra.2017.2651952 · ExternalCitation · doi-reference
Agricultural robotics: unmanned robotic service units in agricultural tasks
10.1109/mie.2013.2252957 · ExternalCitation · doi-reference
Development of an end-effector for robotic harvesting of hydroponic lettuce
10.13031/ja.16269 · ExternalCitation · doi-reference
Integrating neural network for pest detection in controlled environment vertical farm
10.17485/ijst/v15i17.353 · ExternalCitation · doi-reference
10.2139/ssrn.5142250
10.2139/ssrn.5142250 · ExternalCitation · doi-reference
10.32473/edis-fe1041-2018
10.32473/edis-fe1041-2018 · ExternalCitation · doi-reference
NeRF-based 3D reconstruction pipeline for acquisition and analysis of tomato crop morphology
10.3389/fpls.2024.1439086 · ExternalCitation · doi-reference
Neural network model for greenhouse microclimate predictions
10.3390/agriculture12060780 · ExternalCitation · doi-reference
Research on flexible end-effectors with humanoid grasp function for small spherical fruit picking
10.3390/agriculture13010123 · ExternalCitation · doi-reference
Incorporating artificial intelligence technology in smart greenhouses: current State of the Art
10.3390/app13010014 · ExternalCitation · doi-reference
Comparison of land, water, and energy requirements of lettuce grown using hydroponic vs. conventional agricultural methods
10.3390/ijerph120606879 · ExternalCitation · doi-reference
Albumentations: fast and flexible image augmentations
10.3390/info11020125 · ExternalCitation · doi-reference
Combination of multivariate standard addition technique and deep kernel learning model for determining multi-ion in hydroponic nutrient solution
10.3390/s20185314 · ExternalCitation · doi-reference
Deep learning in controlled environment agriculture: a review of recent advancements, challenges and prospects
10.3390/s22207965 · ExternalCitation · doi-reference
Exploiting pre-trained convolutional neural networks for the detection of nutrient deficiencies in hydroponic basil
10.3390/s23125407 · ExternalCitation · doi-reference
Evaluation of end effectors for robotic harvesting of mango fruit
10.3390/su15086769 · ExternalCitation · doi-reference
An artificial intelligence-powered environmental control system for resilient and efficient greenhouse farming
10.3390/su162410958 · ExternalCitation · doi-reference
