New Methodology for Corn Stress Detection Using Remote Sensing and Vegetation Indices

Since corn is the second most widespread crop globally and its production has an impact on all industries, from animal husbandry to sweeteners, modern agriculture meets task of preserving yield quality detecting stress. Application remote sensing techniques enabled more efficient monitoring due ability cover large areas perform non-destructive non-invasive measurements. By using vegetation indices, it possible effectively measure status surface detect stress field. This study describes methodology for detection red-green-blue (RGB) imagery indices. Using Excess Green index calculated histogram healthy crop, been detected. The obtained results showed higher than 89% accuracy both experimental plots, confirming that proposed can be used images acquired only with RGB sensor. method does not depend sensor image acquisition detection, so in various different setups.