This paper aims to investigate how different forms of artificial intelligence (AI) technologies can be used to support smallholder farmers in climate change adaptation efforts while also benefiting the locavore movement and food sovereignty initiatives.
A technical review of currently existing and adopted AI solutions to determine the performance, adoption framework and conformity to locavores in various geographical regions. The application examples from Asia, Africa and the Middle East included in the study vary in terms of technological application and integration model.
The review revealed knowledge gaps in AI applications for climate adaptation and locavore food systems from which new categories of applications with proven usefulness can be derived. Climate knowledge systems incorporating indigenous knowledge, resource management technologies that sustain indigenous crops, market linkage technologies eradicating exploitative middlemen and biodiversity-preserving technologies that support agricultural diversity. These technologies are friendly to locavore principles as long as they are applied through culturally relevant and locally appropriate methods.
The frequent developments in the agricultural use of AI could result in including only some recent implementations in the study. Certainly, there is probably publication bias towards successful implementations, and there remains very little information on any technology due to its relatively recent implementation.
Appropriate measures should be taken to adopt AI in agriculture, which includes a bottom-up approach to its deployment, tackling issues of the digital divide, data ownership and sovereignty and respecting the indigenous knowledge system. Technologies have been developed hand in hand with farmers so that interfaces used in those applications reflect the illiterate levels and are also useable in areas where the Internet is rare.
The subject of the current paper is the areas of AI technologies, climate adaptation and the locavore movements, with a focus on small-scale farmers. Thus, the paper focuses on innovation and enactment issues that can help explain how AI can enhance rather than eliminate indigenous farming practices.
