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Prasad Deshpande, Wri Dutta, Gaurav Jha
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The effect of soil moisture anomalies on maize yield in Germany
10.5194/nhess-18-889-2018 · doi-reference
The MONICA model: testing predictability for crop growth, soil moisture and nitrogen dynamics
10.1016/j.ecolmodel.2011.02.018 · doi-reference
The anthropology of food and eating
10.1146/annurev.anthro.32.032702.131011 · 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 · doi-reference
Responsible AI in farming: a multi-criteria framework for sustainable technology design
10.3390/app14010437 · doi-reference
Autoformer-based model for predicting and assessing wheat quality changes of pesticide residues during storage
10.3390/foods12091833 · doi-reference
GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography
10.3390/ijgi11070385 · doi-reference
10.1016/j.compag.2024.109032
10.1016/j.compag.2024.109032 · doi-reference
Geospatial artificial intelligence (GeoAI) and satellite imagery fusion for soil physical property predicting
10.3390/su151914125 · 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 · doi-reference
Food security: the challenge of feeding 9 billion people
10.1126/science.1185383 · 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 · doi-reference
Principled artificial intelligence: mapping consensus in ethical and rights-based approaches to principles for AI
10.2139/ssrn.3518482 · doi-reference
Recommendations for ethical and responsible use of artificial intelligence in digital agriculture
10.3389/frai.2022.884192 · doi-reference
An analysis of power dynamics within innovation platforms for natural resource management
10.1080/2157930x.2014.921274 · 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 · doi-reference
Cucumber disease recognition with small samples using image-text-label-based multi-modal language model
10.1016/j.compag.2023.107993 · doi-reference
Plant modelling framework: software for building and running crop models on the APSIM platform
10.1016/j.envsoft.2014.09.005 · doi-reference
Looking through a responsible innovation lens at uneven engagements with digital farming
10.1016/j.njas.2019.03.001 · doi-reference
Using a co-innovation approach to support innovation and learning: cross-cutting observations from different settings and emergent issues
10.1177/0030727017707403 · doi-reference
Who drives the digital revolution in agriculture? A review of supply‐side trends, players and challenges
10.1002/aepp.13145 · doi-reference
Wheat yield potential in controlled-environment vertical farms
10.1073/pnas.2002655117 · 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 · doi-reference
An institutional diagnostics of agricultural innovation; public-private partnerships and smallholder production in Uganda
10.1016/j.njas.2017.10.006 · doi-reference
Managing the data resource: a contingency perspective
10.2307/249204 · doi-reference