Author(s): Deepa Srivastava

Abstract: The integration of artificial intelligence (AI) into plant pathogen detection is transforming agricultural practices by enabling more efficient, accurate, and scalable disease management solutions. Traditional diagnostic methods, while effective, often require significant time, expertise and laboratory resources, limiting their application in large-scale and real-time scenarios. AI-powered innovations, including machine learning (ML), deep learning (DL) and computer vision, are revolutionizing the detection and diagnosis of plant diseases. These technologies analyze vast datasets from sources such as high-resolution imaging, genomic sequences, environmental sensors, and remote sensing platforms to identify pathogens with unprecedented precision. AI-driven tools, such as mobile-based diagnostic apps, autonomous drones, and predictive modeling systems, empower farmers and agricultural stakeholders with real-time insights into disease outbreaks and progression. Additionally, AI enhances the interpretation of metagenomic data, facilitating the identification of novel and unculturable pathogens. This paper explores the transformative potential of AI in plant pathogen detection, highlighting its contributions to sustainable agriculture, early disease management, and food security. It also addresses challenges such as data availability, model reliability, and ethical considerations, paving the way for future advancements in AI-driven plant pathology.

DOI: 10.22271/27893065.2025.v5.i2a.145

Pages: 60-66 | Views: 540 | Downloads: 292

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