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International Journal of Engineering in Computer Science

Impact Factor (RJIF): 10.52, P-ISSN: 2663-3582, E-ISSN: 2663-3590
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2025, Vol. 7, Issue 1, Part C

From leaves to lab: Innovative methods in plant disease diagnosis


Author(s): Ajay Chauhan, Ashwin Parihar and Srikant Singh

Abstract: From Leaves to Lab: Innovative Methods in Plant Disease Diagnosis explores the evolution and advancement of techniques used to detect and diagnose plant diseases, with a focus on integrating traditional practices with modern technological innovations. The study highlights the shift from conventional visual inspection and microscopic analysis of leaves to cutting-edge methods involving machine learning, image processing, remote sensing, and biosensors. Emphasis is placed on early detection, accuracy, and scalability of these methods to address global agricultural challenges, enhance crop yield, and reduce economic losses. Case studies involving AI-based leaf image classification, hyperspectral imaging, and lab-on-a-chip technologies demonstrate the potential of interdisciplinary approaches in revolutionizing plant pathology. The paper also discusses challenges in field deployment, data quality, and the need for collaborative frameworks between researchers, farmers, and technologists. By bridging the gap between field observations and laboratory precision, the study underscores the promise of innovative diagnostics in ensuring sustainable and resilient agricultural systems.

DOI: https://www.doi.org/10.33545/26633582.2025.v7.i1c.184

Pages: 219-226 | Views: 1459 | Downloads: 908

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International Journal of Engineering in Computer Science
How to cite this article:
Ajay Chauhan, Ashwin Parihar, Srikant Singh. From leaves to lab: Innovative methods in plant disease diagnosis. Int J Eng Comput Sci 2025;7(1):219-226. DOI: 10.33545/26633582.2025.v7.i1c.184
International Journal of Engineering in Computer Science

International Journal of Engineering in Computer Science

International Journal of Engineering in Computer Science
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