Smartphone-powered electrochemical lateral flow assay delivers ELISA-level plant disease detection in East Africa
The study reports the development of ELLA, a battery-free, smartphone-powered electrochemical lateral flow assay designed for field deployment in resource-limited settings. Investigators found that validation trials in Tanzania achieved 95% agreement with ELISA and 89% agreement with RT-qPCR for detecting cassava brown streak disease, while outperforming ELISA in limit of detection and costing under US$1 per test. The platform integrates disposable cassettes with ferrocene-labeled nanoparticles and cloud-linked analytics, including an AI image classification model for scalable surveillance. This work demonstrates a validated pathway for decentralized, low-cost immunoassays that bypass centralized laboratory infrastructure in low- and middle-income regions.
The original study
Electrochemical lateral flow assay with ELISA-level performance for detecting plant diseases in East Africa.
- Authors
- Flauzino JMR, Sanli A, Shirima RR, Cai Y, Hu T, Li L, et al.
- Journal
- Proceedings of the National Academy of Sciences of the United States of America
- Type
- Journal Article
- PMID
- 42475572
Original abstract
Cassava brown streak disease (CBSD) threatens food security for millions in East Africa, yet its control remains limited by the absence of field-deployable molecular diagnostics. Here, we introduce ELLA (Electrochemical Lateral flow assay with Linked Analytics), a battery-free, smartphone-powered electrochemical lateral flow assay that delivers enzyme-linked immunosorbent assay (ELISA)-grade protein detection directly in the field. ELLA integrates near-field communication, a single-chip potentiostat, metal-pin electrodes, and ferrocene-labeled nanoparticles into a fully disposable cassette, enabling quantitative immunoassays without optical instrumentation or centralized laboratory infrastructure. Validated across laboratory studies and extensive field trials in Tanzania, ELLA achieved 95% agreement with ELISA and 89% agreement with RT-qPCR, outperforming ELISA's limit of detection while maintaining a material cost below US$1. By coupling molecular test results with cloud-linked analytics, we further trained DeepELLA, a smartphone-based image classification model that enables scalable surveillance from field-acquired leaf images. Together, these advances unify electrochemical sensing, digital connectivity, and AI-assisted interpretation, enabling portable, ELISA-level diagnostics for plant, environmental, and health monitoring in resource-limited regions.