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Adoption of autonomous robots in the soft fruit sector: grower perspectives in the UK

Rose, D. C. and Bhattacharya, M. (2023) Adoption of autonomous robots in the soft fruit sector: grower perspectives in the UK. Smart Agricultural Technology, 3. 100118. ISSN 2772-3755

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To link to this item DOI: 10.1016/j.atech.2022.100118

Abstract/Summary

The effects of changing geopolitics, demographic change, and COVID-19 have caused significant disruption to labour in the agricultural sector around the world. In the UK, the challenges to free movement of labour and safe working conditions caused by COVID-19 have exacerbated the labour shortage caused by Brexit. In these circumstances, the use of autonomous robots in those sectors hardest hit by labour shortages, such as soft fruit, is being considered as a potential solution. Autonomous robots for use in the high value crop sector, including soft fruit, are at varying stages of technology readiness with robots for disease treatment, packhouse, and logistic support already commercially used and robots for picking approaching a demonstration phase. However, the pathway to implementation is not determined by technology readiness alone, but rather by the intention and ability of growers to adopt. To date, there has been limited investigation of the views of soft fruit growers towards the introduction of autonomous robots in the sector. We used a mixed methods approach, utilising a grower survey and qualitative interviews conducted in the UK, to explore the factors affecting adoption of autonomous robots on soft fruit farms. In general, the survey shows that growers are optimistic about the prospects of autonomous robots on soft fruit farms, although not necessarily in the short-term and there are several factors affecting uptake, particularly cost and infrastructure, as well as issues such as data ownership, cybersecurity, skills, and trust. We reflect on our findings in the context of existing research on technology adoption by growers and make a series of industry and policy recommendations which have global relevance.

Item Type:Article
Refereed:Yes
Divisions:Life Sciences > School of Agriculture, Policy and Development > Department of International Development
ID Code:107485

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