Abstract
Background: Future predictions of the world population and the demand for agricultural products per capita suggest that we have to increase food production at least two-fold by 2050 despite negative forces such as climate change with more frequent extreme weather conditions, degradation of land, increasing land scarcity, shortness of water, desertification, fooding and the loss of biodiversity for ecosystem services. One pathway how to achieve this challenge is sustainable intensification, which formulates a coarse goal rather than a detailed guideline. A heterogeneous compound of strategies is necessary and a central aspect involves examining the potential of AI technology to boost production efficiency while mitigating negative environmental consequences.Methodology: To comprehensively examine this topic, two systematic literature searches were conducted. The first aimed to gather information on sustainable agriculture and the environmental costs of conventional practices. The second focused on identifying explicit AI applications and their impact.Results: Numerous examples demonstrated how sustainable AI development drives agriculture towards a more sustainable future. The main contribution of this study is the Data-Model-Purpose matrix (DMP matrix) and the derived Bayesian matrix for a comprehensive analysis of several AI applications in agriculture and their relations to data sources and algorithms.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
