Machine Learning-Assisted Biosensing in Agriculture, Food, and Environmental Applications
A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Environmental Biosensors and Biosensing".
Deadline for manuscript submissions: 31 July 2026 | Viewed by 17
Special Issue Editors
Interests: biosensors; microfluidics; machine learning; gas sensors; optical sensing
Special Issue Information
Dear Colleagues,
Rapid, sensitive, specific, and cost-effective detection is paramount throughout the agri-food-environment chain—from monitoring soil health, crop diseases, pesticide residues, and environmental hazards to ensuring food safety, quality control, and traceability. Traditional analytical methods often fall short in meeting these demands due to limitations in speed, cost, portability, or complexity. Biosensors offer promising alternatives, but challenges remain in enhancing their sensitivity, specificity, multiplexing capability, robustness in complex matrices, and data interpretation.
This Special Issue focuses on how machine learning (ML) and artificial intelligence (AI) are revolutionizing biosensor development and deployment. We seek contributions demonstrating ML integration in the following areas: optimizing biosensor design (biorecognition elements, transducers, architectures); processing and enhancing complex, noisy signals (improving SNR, enabling multiplexing); automating large-scale data analysis (predictive analytics, classification, anomaly detection); facilitating sensor fusion and calibration; and developing smart, field-deployable systems for real-time decision support.
We invite original research papers and comprehensive reviews addressing both fundamental advances and practical applications. Topics include, but are not limited to, the following: ML-enhanced electrochemical, optical, acoustic wave, conductive/impedance, and semiconductor biosensors; and applications in precision agriculture, livestock health, food safety, food quality/freshness monitoring, and environmental pollutant detection.
Dr. Hao Zhang
Dr. Junfeng Wu
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- deep learning
- biosensors
- nanomaterials
- semiconductors
- optics
- electrochemicals
- agriculture
- food
- environment
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