Functional nanomaterial-based biosensors as complex adaptive systems for early plant disease detection for sustainable agriculture
Issued Date
2026-03-01
Resource Type
ISSN
13698001
Scopus ID
2-s2.0-105021855420
Journal Title
Materials Science in Semiconductor Processing
Volume
203
Rights Holder(s)
SCOPUS
Bibliographic Citation
Materials Science in Semiconductor Processing Vol.203 (2026)
Suggested Citation
Sonu S., Shaklani S., Sable H., Pathania D., singh P., Thakur P., Raizada P., Chaudhary V. Functional nanomaterial-based biosensors as complex adaptive systems for early plant disease detection for sustainable agriculture. Materials Science in Semiconductor Processing Vol.203 (2026). doi:10.1016/j.mssp.2025.110222 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114511
Title
Functional nanomaterial-based biosensors as complex adaptive systems for early plant disease detection for sustainable agriculture
Corresponding Author(s)
Other Contributor(s)
Abstract
Plant diseases must be detected early and accurately because of the growing risk to global food security posed by phytopathogens, climate change and intensive farming methods. Integrating nanotechnology into biosensor development provides transformative potential to address this crisis by facilitating rapid, accurate, and field-deployable detection of phytopathogens. Advanced functional nanomaterials enable the design of high-performance, quicker reaction time, portable, greater sensitivity and point-of-care biosensors that function as intelligent constituents within complex adaptive agricultural systems can act as a better diagnosis tool. These biosensors can interact dynamically with their environment and provide real-time disease surveillance and informed decision-making, contributing to resilient and sustainable farming. This review comprehensively analyses various phytopathogens and functional nanomaterial-based biosensors for plant pathogen monitoring, considering the One Health approach that recognizes the interconnectedness of plant, human, and environmental health. It highlights various biosensing strategies, including electrical, electrochemical, chemiresistive and optical modules, that overcome the limitations of conventional diagnostics in terms of sensitivity, specificity, and scalability. It further explores the integration of biosensing with advanced technologies such as complex physics tools like artificial intelligence, machine learning, network analysis, Internet of Things, and cloud computing. As adaptive nodes in intelligent and complex agricultural ecosystems, nano biosensors represent a promising frontier for early pathogen detection, minimizing crop losses, and enhancing global agricultural resilience.