Early Detection of Crop Diseases Using Hyperspectral Imaging and Deep Learning

Registration ID: IJNRD_322154

Published ID: IJNRD2603338

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Mrs. Bharti Pardhi, Aishkumar Gautam, Krupasagar Gupta, Aditya Gupta, Aman Chore

Detecting crop diseases early is vital for boosting agricultural output, minimizing financial losses, and ensuring global food security. Traditional detection methods, which primarily depend on visual inspection and RGB imaging, frequently miss early-stage infections and can result in delayed treatment decisions. Hyperspectral imaging has recently emerged as a sophisticated sensing technology that captures detailed spectral data, revealing subtle physiological changes in plants before visible symptoms manifest. Meanwhile, deep learning techniques have demonstrated significant promise in managing high-dimensional data and autonomously identifying intricate spectral-spatial patterns to achieve precise disease classification. This paper offers a comprehensive review of research from 2015 to 2024 on leveraging hyperspectral imaging and deep learning for the early detection of crop diseases. This study evaluates and compares various approaches, ranging from traditional machine learning models and convolutional neural networks to emerging attention-based architectures, in terms of their effectiveness, scalability, and practical applicability. The study also addresses significant challenges, including high computational demands, the scarcity of labeled datasets, and obstacles to real-world field deployment. Furthermore, the study highlights critical research gaps and future opportunities, with a focus on developing efficient models, data-driven learning strategies, and cost-effective solutions for precision agriculture. The review highlights the increasing significance of combining hyperspectral sensing with intelligent algorithms to facilitate timely, accurate, and automated crop health monitoring. This research focuses on hyperspectral imaging, deep learning, and machine learning for plant disease detection within the realm of precision farming and agricultural technology

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Mrs. Bharti Pardhi, Aishkumar Gautam, Krupasagar Gupta, Aditya Gupta, & Aman Chore (March-2026). Early Detection of Crop Diseases Using Hyperspectral Imaging and Deep Learning . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(3), d288-d293. https://ijnrd.org/papers/IJNRD2603338.pdf

Volume 11 Issue 3, March-2026

Pages : d288-d293

Paper Reg. ID: IJNRD_322154

Published Paper Id: IJNRD2603338

Research Area: Other area not in list

Author Type: Indian Author

Country: Nagpur, Maharashtra, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2603338.pdf

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Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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