AUTHOR=S Poornima , Shirly Edward A. 

TITLE=MLVI-CNN: a hyperspectral stress detection framework using machine learning-optimized indices and deep learning for precision agriculture

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1631928

DOI=10.3389/fpls.2025.1631928

ISSN=1664-462X

ABSTRACT=IntroductionEarly 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.MethodThis 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.ResultsThe 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).DiscussionThis framework is suitable for deployment with UAVs, satellite platforms, and precision agriculture systems.