Introduction: 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.Method: 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.Results: 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).Discussion: This framework is suitable for deployment with UAVs, satellite platforms, and precision agriculture systems.

Diabetes mellitus has developed into a significant problem during the past decades. Identifying and controlling diabetes can be done by proper treatment and routine like a healthy diet, a weight reduction program, insulin shots treatments, vocal medicines to reduce blood glucose level. Self-glucose checking or clinical testing helps control future complications like blindness, heart disease, kidney damage, or amputations of the limbs due to the disease. Therefore, timely checkups can increase the chances of getting treatment before it is too late. Present glucose evaluating systems for raising diabetic patients are mostly intrusive, hurting, laborious, & cost a sum for frequent testers. The non-invasive glucose testing machinery helps in overcoming these boundaries. Due to this advantage, this subject has gained interest among researchers and many product manufacturing Industries. This evaluation targets to offer current information on available and upcoming trends for non-invasive and nominally invasive techniques. This review also focuses on using the different electromagnetic spectrum bands in detecting the blood glucose level.