ORIGINAL RESEARCH article

Front. Plant Sci.

Sec. Sustainable and Intelligent Phytoprotection

Volume 16 - 2025 | doi: 10.3389/fpls.2025.1631928

This article is part of the Research TopicAdvances in Remote Sensing Techniques for Forest Monitoring and AnalysisView all 11 articles

MLVI-CNN: A Hyperspectral Stress Detection Framework Using Machine Learning-Optimized Indices and Deep Learning for Precision Agriculture

Provisionally accepted
  • Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

The final, formatted version of the article will be published soon.

Early and accurate detection of crop stress is vital for sustainable agriculture and food security. Traditional vegetation indices such as NDVI and NDWI often fail to detect early-stage water and structural stress due to their limited spectral sensitivity. This study introduces two novel hyperspectral indices Machine Learning-Based Vegetation Index (MLVI) and Hyperspectral Vegetation Stress Index (H_VSI) which leverage critical spectral bands in the Near-Infrared (NIR), Shortwave Infrared 1 (SWIR1), and Shortwave Infrared 2 (SWIR2) regions. These indices are optimized using Recursive Feature Elimination (RFE) and serve as inputs to a Convolutional Neural Network (CNN) model for stress classification. The proposed CNN model achieved a classification accuracy of 83.40%, effectively distinguishing six levels of crop stress severity. Compared to conventional indices, MLVI and H_VSI enable detection of stress 10-15 days earlier and exhibit a strong correlation with ground-truth stress markers (r = 0.98). This framework is suitable for deployment with UAVs, satellite platforms, and precision agriculture systems.

Keywords: hyperspectral imaging, vegetation index, machine learning, Crop stress, Early detection, remote sensing

Received: 20 May 2025; Accepted: 07 Jul 2025.

Copyright: © 2025 SERALATHAN and Edward A. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Shirly Edward A, Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

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