Work

From Prototype to Sustainable Food Production: Translational Barriers and Design Priorities for Agricultural Biosensing Systems

Year: 2026

Type: article

Abstract: ABSTRACT Agricultural biosensors could advance sustainable food production by enabling timely detection of crop stress, pathogens, nutrient imbalances, and environmental contaminants. Despite decades of research, however, robust commercial biosensing platforms remain uncommon because analytical performance under controlled conditions does not necessarily translate into dependable field operation or measurable sustainability benefits. Sensors affected by fouling, calibration drift, unstable biorecognition elements, ambiguous biological signals, or frequent replacement may increase cost and material consumption without improving management decisions or reducing agricultural inputs. This perspective article examines the technical, biological, operational, and socioeconomic barriers that limit the transition of agricultural biosensors from laboratory prototypes to resource‐efficient decision‐support systems. It considers performance standards, recognition‐element durability, plant–device biocompatibility, validation practices, user education, and uncertainty communication across open fields, controlled‐environment agriculture, plant wearables, and in vivo platforms. A sustainability‐oriented framework is proposed that connects analytical validity and operational reliability with decision relevance, maintainability, lifecycle considerations, and responsible resource use. Future progress will require biological co‐design, realistic multiscale validation, multimodal data integration, modular and repairable devices, actionable user interfaces, and transparent communication of uncertainty. Agricultural biosensors should ultimately be evaluated not only by their ability to detect target analytes but also by whether they enable reliable interventions that reduce resource consumption, environmental burdens, production risks, and avoidable waste.

Source: Journal of Sustainable Agriculture and Environment

DOI: 10.1002/sae2.70221

Authors: Frank Efe Erukainure, David B. Langston, Zachary Easton, Blake N. Johnson, Abhilash K. Chandel

Institutions: Tidewater Community College, Virginia Gastroenterology Institute, Virginia Tech

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDGs: Zero hunger, Responsible consumption and production

Open Access status: gold

Estimated APC paid: $2,640