Volume 15, Issue 8 · IJARST · ISSN 2319-1783 / 2320-1126

Design and Development of an Artificial Intelligence-Assisted Portable Molecular Diagnostic Platform for Rapid Detection of Plant Diseases and Precision Agriculture

Design and Development of an Artificial Intelligence-Assisted Portable Molecular Diagnostic Platform for Rapid Detection of Plant Diseases and Precision Agriculture is an open-access, peer-reviewed research paper by Sonia Nain, published in Volume 15, Issue 8 of the International Journal of Advanced Research in Science and Technology (IJARST), a UGC-approved journal (Print ISSN 2319-1783, Online ISSN 2320-1126).

Author

Sonia Nain

Abstract

The rapid emergence of plant diseases, climate variability, and increasing global food demand have created an urgent need for intelligent, rapid, and field-deployable diagnostic technologies to support sustainable agriculture. Conventional plant disease detection methods, including visual inspection and laboratory-based molecular analysis, are often time-consuming, expensive, infrastructure-dependent, and unsuitable for real-time field applications. Although Polymerase Chain Reaction (PCR) and Quantitative Real-Time PCR (qPCR) provide highly sensitive and accurate pathogen detection, their dependence on centralized laboratories limits their practical application in precision agriculture. This study presents an Artificial Intelligence-Assisted Portable Molecular Diagnostic Platform for rapid and on-site detection of plant diseases. The proposed platform integrates automated DNA extraction, portable qPCR, a modular microfluidic biosensor cartridge, fluorescence-based detection, embedded electronics, wireless communication, cloud computing, and Artificial Intelligence within a compact portable architecture. The system combines molecular diagnostic data with biosensor signals and environmental information to support automated pathogen identification, disease classification, severity assessment, and intelligent agricultural decision-making. The proposed Artificial Intelligence framework incorporates Machine Learning and Deep Learning algorithms, including Random Forest, XGBoost, Support Vector Machine, Convolutional Neural Network, and Transformer-based approaches. Explainable Artificial Intelligence techniques are incorporated to improve the interpretability of diagnostic predictions. Experimental validation involves comparison of biosensor outputs with qPCR measurements using sensitivity, specificity, accuracy, detection limit, repeatability, and other statistical performance indicators. The results presented in the research demonstrate that the integrated platform provides rapid, reliable, and field-deployable molecular diagnosis while maintaining strong agreement with laboratory-based qPCR analysis. The integration of biosensors, Artificial Intelligence, wireless communication, and cloud-based analytics provides a comprehensive technological framework for precision agriculture, sustainable crop protection, and real-time disease surveillance.

DOI: https://doi.org/10.62226/ijarst20262783

 

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DOI

10.62226/ijarst20262783

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Sonia Nain — “Design and Development of an Artificial Intelligence-Assisted Portable Molecular Diagnostic Platform for Rapid Detection of Plant Diseases and Precision Agriculture.” International Journal of Advanced Research in Science and Technology (IJARST), Volume 15, Issue 8. DOI: 10.62226/ijarst20262783.

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