Agricultural robots: using anticipatory approaches to embed appropriate hygienic design of hardware

Purpose With the global challenge of recruiting labour in agriculture, research and industry attention has turned to the development of autonomous harvesting machines (AHMs) to replace or collaborate with, humans. This research explores the concept of appropriate hygienic design of AHMs to reduce hardware-related food safety risk. Design/methodology/approach This iterative structured evidence review utilises the novel approach of applying the AREA principles of responsible innovation (anticipate, reflect engage and act) together with the application of hazard analysis critical control point (HACCP) as a reflexive framework to critically assess and critique appropriate hardware-related hygienic design in a particular use case, AHMs for fruit and vegetable harvesting. Findings Selective autonomous harvesting encompasses the AHMs navigating to, detecting and selecting harvest-ready fruits or vegetables, detaching the produce, checking its quality and any visible food safety concerns and then placing the assessed produce into the appropriate container. Good hygienic design is an essential process to ensure hardware-related food safety risk is minimised. The five-step framework developed in this work can support hardware designers to ensure AHMs are appropriately designed, developed and deployed. Originality/value This research contributes through the development of a decision-support tool for appropriate AHM hardware design, development and deployment suitable for industry practitioners, academics and policymakers.

Authors

Institutions

Publication Details

Journal
British Food Journal
Published
2025-11-24
DOI
https://doi.org/10.1108/bfj-12-2024-1328
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Agricultural robots: using anticipatory approaches to embed appropriate hygienic design of hardware

Leonardo Guevara, Zhuoling Huang, Louise Manning, Jack H. Grant
British Food Journal
Smart Agriculture and AI
article

Agricultural robots: using anticipatory approaches to embed appropriate hygienic design of hardware

Leonardo Guevara, Zhuoling Huang, Louise Manning, Jack H. Grant
article en

Abstract

Purpose With the global challenge of recruiting labour in agriculture, research and industry attention has turned to the development of autonomous harvesting machines (AHMs) to replace or collaborate with, humans. This research explores the concept of appropriate hygienic design of AHMs to reduce hardware-related food safety risk. Design/methodology/approach This iterative structured evidence review utilises the novel approach of applying the AREA principles of responsible innovation (anticipate, reflect engage and act) together with the application of hazard analysis critical control point (HACCP) as a reflexive framework to critically assess and critique appropriate hardware-related hygienic design in a particular use case, AHMs for fruit and vegetable harvesting. Findings Selective autonomous harvesting encompasses the AHMs navigating to, detecting and selecting harvest-ready fruits or vegetables, detaching the produce, checking its quality and any visible food safety concerns and then placing the assessed produce into the appropriate container. Good hygienic design is an essential process to ensure hardware-related food safety risk is minimised. The five-step framework developed in this work can support hardware designers to ensure AHMs are appropriately designed, developed and deployed. Originality/value This research contributes through the development of a decision-support tool for appropriate AHM hardware design, development and deployment suitable for industry practitioners, academics and policymakers.

British Food JournalVol. 128(4)
Lincoln University - Pennsylvania (US)
Openalex Percentile: Top 74%
Smart Agriculture and AI
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Agricultural robots: using anticipatory approaches to embed appropriate hygienic design of hardware — Leonardo Guevara, Zhuoling Huang, et al. · British Food Journal (2025) | TGRS Research Map | TGRS