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DNA-Logic Trimodal Biofuel Cell Platform for AI-Assisted Dual Pathogen Detection.

Tao Wen¹, Chenchen Jin¹, Haiyan Chen²

¹Key Laboratory of Optic-Electric Chemo/Biosensing and Molecular Recognition (47874Guangxi Minzu University), Education Department of Guangxi Zhuang Autonomous Region; Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission; Guangxi Key Laboratory of Chemistry and Engineering of Forest Products; School of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning 530006, China.

|March 9, 2026

Summary

This summary is machine-generated.

A novel DNA-logic biosensor rapidly detects two sugarcane pathogens simultaneously using electrochemical, colorimetric, and photothermal signals. This self-powered, smartphone-operated device offers field-deployable disease screening for smart agriculture.

Keywords:

DNA logic algorithm dual-target detection machine learning multimodal biosensing plant pathogen diagnostics

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Area of Science:

Biotechnology
Biosensing
Agricultural Diagnostics

Background:

Conventional sugarcane pathogen detection methods like qPCR are slow, lab-dependent, and impractical for field use.
Early and accurate diagnosis of sugarcane diseases such as smut (Sporisorium scitamineum) and pokkah boeng (Fusarium sacchari) is crucial for crop management.
There is a need for rapid, on-site, and multiplexed diagnostic tools for plant pathogens.

Purpose of the Study:

To develop a DNA-logic-gated, trimodal, self-powered biosensing platform for simultaneous detection of S. scitamineum and F. sacchari.
To overcome the limitations of conventional diagnostic techniques for field deployment.
To create a user-friendly, smartphone-interfaced system for early disease warning in smart agriculture.

Main Methods:

Integration of electrochemical, colorimetric, and photothermal signal readouts in a single portable device.
Utilization of target-specific DNA logic circuits to orchestrate sensing mechanisms.
Employment of an enzymatic biofuel cell for self-powering and a smartphone interface for operation.
Application of a machine learning-assisted random forest regression model for signal processing.

Main Results:

Achieved high sensitivity with detection limits of 3.7 × 10⁻¹⁶ M for smut and 2.3 × 10⁻¹⁶ M for pokkah boeng.
Demonstrated excellent accuracy for both pathogens, with R² values of 0.982 for smut and 1.000 for pokkah boeng.
Validated the platform's performance using field samples, showing high consistency with qPCR assays.

Conclusions:

The developed trimodal biosensing platform offers rapid, simultaneous, and on-site detection of major sugarcane pathogens.
The system is self-powered, user-friendly, and suitable for field deployment, representing a significant advancement for smart agriculture.
This universal strategy enables multiplex pathogen screening, facilitating early disease warning and improved crop management.