Electrochemical lateral flow assay with ELISA-level performance for detecting plant diseases in East Africa

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.

Plant phenotyping relevance

主軸は分子診断ですが、植物葉画像から病害状態を分類するDeepELLAも開発され、植物病害表現型の直接推定を含むため対象に含める。

Code and data availability

The supplied blocks describe the ELLA electrochemical lateral flow assay, field trial data, and the DeepELLA image-classification model, but contain no public dataset, image, code, or model deposit with an authors' URL. References to SI Appendix tables/figures are not public assets, and no data or code availability/deo

No evidence-backed public reproduction asset is currently recorded.