Research graph
References from Trustworthy artificial intelligence for digital agriculture. Local targets link to admitted publications; unresolved targets remain external evidence.
An institutional diagnostics of agricultural innovation; public-private partnerships and smallholder production in Uganda
10.1016/j.njas.2017.10.006 · 2018 · External reference
Who is responsible for ‘responsible AI’?: navigating challenges to build trust in AI agriculture and food system technology
10.1007/s11119-023-10063-3 · 2024 · External reference
Wheat yield potential in controlled-environment vertical farms
10.1073/pnas.2002655117 · 2020 · External reference
Who drives the digital revolution in agriculture? A review of supply‐side trends, players and challenges
10.1002/aepp.13145 · 2021 · External reference
Unresolved reference
2016 · External reference
Using a co-innovation approach to support innovation and learning: cross-cutting observations from different settings and emergent issues
10.1177/0030727017707403 · 2017 · External reference
Looking through a responsible innovation lens at uneven engagements with digital farming
10.1016/j.njas.2019.03.001 · 2019 · External reference
Plant modelling framework: software for building and running crop models on the APSIM platform
10.1016/j.envsoft.2014.09.005 · 2014 · External reference
Cucumber disease recognition with small samples using image-text-label-based multi-modal language model
10.1016/j.compag.2023.107993 · 2023 · External reference
Simulating the effects of agricultural production practices on water conservation and crop yields using an improved SWAT model in the Texas High Plains, USA
10.1016/j.agwat.2020.106574 · 2021 · External reference
An analysis of power dynamics within innovation platforms for natural resource management
10.1080/2157930x.2014.921274 · 2014 · External reference
Recommendations for ethical and responsible use of artificial intelligence in digital agriculture
10.3389/frai.2022.884192 · 2022 · External reference
Bayesian neural networks for satellite fog detection: quantifying epistemic and aleatoric uncertainties
2024 · External reference
What Does Explainable AI Really Mean? A New Conceptualization of Perspectives.
2017 · External reference
Unresolved reference
External reference
Principled artificial intelligence: mapping consensus in ethical and rights-based approaches to principles for AI
10.2139/ssrn.3518482 · 2020 · External reference
Implementation of a GEOAI model to assess the impact of agricultural land on the spatial distribution of PM2.5 concentration
10.1016/j.chemosphere.2024.141438 · 2024 · External reference
Food security: the challenge of feeding 9 billion people
10.1126/science.1185383 · 2010 · External reference
Managing the data resource: a contingency perspective
10.2307/249204 · 1988 · External reference
Introduction to the handbook on strategic public management
2023 · External reference
Big models in agriculture: key technologies, application and future directions
2024 · External reference
Performance of a wheat yield prediction model and factors influencing the performance: a review and meta-analysis
10.1016/j.agsy.2021.103278 · 2021 · External reference
Geospatial artificial intelligence (GeoAI) and satellite imagery fusion for soil physical property predicting
10.3390/su151914125 · 2023 · External reference
The DSSAT cropping system model
2003 · External reference
10.1016/j.compag.2024.109032
10.1016/j.compag.2024.109032 · External reference
Unresolved reference
External reference
GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography
10.3390/ijgi11070385 · 2022 · External reference
Autoformer-based model for predicting and assessing wheat quality changes of pesticide residues during storage
10.3390/foods12091833 · 2023 · External reference
Unresolved reference
2023 · External reference
A unified approach to interpreting model predictions
2017 · External reference
Responsible AI in farming: a multi-criteria framework for sustainable technology design
10.3390/app14010437 · 2024 · External reference
Unresolved reference
2024 · External reference
The U.S. culture collection network responding to the requirements of the Nagoya protocol on access and benefit sharing
10.1128/mbio.00982-17 · 2017 · External reference
Survey reveals concerns and adoption trends around AI’s rising influence [WWW Document
2024 · External reference
The anthropology of food and eating
10.1146/annurev.anthro.32.032702.131011 · 2002 · External reference
The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics
10.1016/j.ecolmodel.2011.02.018 · 2011 · External reference
Unresolved reference
1997 · External reference
Unresolved reference
2022 · External reference
The effect of soil moisture anomalies on maize yield in Germany
10.5194/nhess-18-889-2018 · 2018 · External reference
“Why Should I Trust You?”: explaining the predictions of any classifier
2016 · External reference
Cereal growth, development and yield
1998 · External reference
Hyperfidelis: a software toolkit to empower precision agriculture with GeoAI
10.3390/rs16091584 · 2024 · External reference
Unresolved reference
2024 · External reference
The role of stakeholders in the context of responsible innovation: a meta-synthesis
10.3390/su11061766 · 2019 · External reference
Unresolved reference
2019 · External reference
Advances in geocomputation and geospatial artificial intelligence (GeoAI) for mapping
2023 · External reference
Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
10.1038/s41598-022-06249-w · 2022 · External reference
AquaCrop—the FAO crop model to simulate yield response to water: I. Concepts and underlying principles
10.2134/agronj2008.0139s · 2009 · External reference
How can LLMs transform the robotic design process?
10.1038/s42256-023-00669-7 · 2023 · External reference
CropSyst, a cropping systems simulation model
2003 · External reference
Revealing power dynamics and staging conflicts in agricultural system transitions: case studies of innovation platforms in New Zealand
10.1016/j.jrurstud.2020.04.022 · 2020 · External reference
Large language models and agricultural extension services
10.1038/s43016-023-00867-x · 2023 · External reference
On approaches and applications of the Wageningen crop models
2003 · External reference
Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation
10.1029/2007wr006720 · 2008 · External reference
Report from the conference, ‘identifying obstacles to applying big data in agriculture
10.1007/s11119-020-09738-y · 2021 · External reference
Leaf only SAM: A segment anything pipeline for zero-shot automated leaf segmentation
10.1016/j.atech.2024.100515 · 2024 · External reference
The EPIC crop growth model
10.13031/2013.31032 · 1989 · External reference
An innovative segment anything model for precision poultry monitoring
10.1016/j.compag.2024.109045 · 2024 · External reference
Who drives the digital revolution in agriculture? A review of supply‐side trends, players and challenges
10.1002/aepp.13145 · ExternalCitation · doi-reference
Report from the conference, ‘identifying obstacles to applying big data in agriculture
10.1007/s11119-020-09738-y · ExternalCitation · doi-reference
Who is responsible for ‘responsible AI’?: navigating challenges to build trust in AI agriculture and food system technology
10.1007/s11119-023-10063-3 · ExternalCitation · doi-reference
Performance of a wheat yield prediction model and factors influencing the performance: a review and meta-analysis
10.1016/j.agsy.2021.103278 · ExternalCitation · doi-reference
Simulating the effects of agricultural production practices on water conservation and crop yields using an improved SWAT model in the Texas High Plains, USA
10.1016/j.agwat.2020.106574 · ExternalCitation · doi-reference
Leaf only SAM: A segment anything pipeline for zero-shot automated leaf segmentation
10.1016/j.atech.2024.100515 · ExternalCitation · doi-reference
Implementation of a GEOAI model to assess the impact of agricultural land on the spatial distribution of PM2.5 concentration
10.1016/j.chemosphere.2024.141438 · ExternalCitation · doi-reference
Cucumber disease recognition with small samples using image-text-label-based multi-modal language model
10.1016/j.compag.2023.107993 · ExternalCitation · doi-reference
10.1016/j.compag.2024.109032
10.1016/j.compag.2024.109032 · ExternalCitation · doi-reference
An innovative segment anything model for precision poultry monitoring
10.1016/j.compag.2024.109045 · ExternalCitation · doi-reference
The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics
10.1016/j.ecolmodel.2011.02.018 · ExternalCitation · doi-reference
Plant modelling framework: software for building and running crop models on the APSIM platform
10.1016/j.envsoft.2014.09.005 · ExternalCitation · doi-reference
Revealing power dynamics and staging conflicts in agricultural system transitions: case studies of innovation platforms in New Zealand
10.1016/j.jrurstud.2020.04.022 · ExternalCitation · doi-reference
An institutional diagnostics of agricultural innovation; public-private partnerships and smallholder production in Uganda
10.1016/j.njas.2017.10.006 · ExternalCitation · doi-reference
Looking through a responsible innovation lens at uneven engagements with digital farming
10.1016/j.njas.2019.03.001 · ExternalCitation · doi-reference
Treatment of input uncertainty in hydrologic modeling: doing hydrology backward with Markov chain Monte Carlo simulation
10.1029/2007wr006720 · ExternalCitation · doi-reference
Winter wheat yield prediction using convolutional neural networks from environmental and phenological data
10.1038/s41598-022-06249-w · ExternalCitation · doi-reference
How can LLMs transform the robotic design process?
10.1038/s42256-023-00669-7 · ExternalCitation · doi-reference
Large language models and agricultural extension services
10.1038/s43016-023-00867-x · ExternalCitation · doi-reference
Wheat yield potential in controlled-environment vertical farms
10.1073/pnas.2002655117 · ExternalCitation · doi-reference
An analysis of power dynamics within innovation platforms for natural resource management
10.1080/2157930x.2014.921274 · ExternalCitation · doi-reference
Food security: the challenge of feeding 9 billion people
10.1126/science.1185383 · ExternalCitation · doi-reference
The U.S. culture collection network responding to the requirements of the Nagoya protocol on access and benefit sharing
10.1128/mbio.00982-17 · ExternalCitation · doi-reference
The anthropology of food and eating
10.1146/annurev.anthro.32.032702.131011 · ExternalCitation · doi-reference
Using a co-innovation approach to support innovation and learning: cross-cutting observations from different settings and emergent issues
10.1177/0030727017707403 · ExternalCitation · doi-reference
The EPIC crop growth model
10.13031/2013.31032 · ExternalCitation · doi-reference
AquaCrop—the FAO crop model to simulate yield response to water: I. Concepts and underlying principles
10.2134/agronj2008.0139s · ExternalCitation · doi-reference
Principled artificial intelligence: mapping consensus in ethical and rights-based approaches to principles for AI
10.2139/ssrn.3518482 · ExternalCitation · doi-reference
Managing the data resource: a contingency perspective
10.2307/249204 · ExternalCitation · doi-reference
Recommendations for ethical and responsible use of artificial intelligence in digital agriculture
10.3389/frai.2022.884192 · ExternalCitation · doi-reference
Responsible AI in farming: a multi-criteria framework for sustainable technology design
10.3390/app14010437 · ExternalCitation · doi-reference
Autoformer-based model for predicting and assessing wheat quality changes of pesticide residues during storage
10.3390/foods12091833 · ExternalCitation · doi-reference
GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography
10.3390/ijgi11070385 · ExternalCitation · doi-reference
Hyperfidelis: a software toolkit to empower precision agriculture with GeoAI
10.3390/rs16091584 · ExternalCitation · doi-reference
The role of stakeholders in the context of responsible innovation: a meta-synthesis
10.3390/su11061766 · ExternalCitation · doi-reference
Geospatial artificial intelligence (GeoAI) and satellite imagery fusion for soil physical property predicting
10.3390/su151914125 · ExternalCitation · doi-reference
The effect of soil moisture anomalies on maize yield in Germany
10.5194/nhess-18-889-2018 · ExternalCitation · doi-reference
