Improved Classification of Nitrogen Stress Severity in Plants Under Combined Stress Conditions Using Spatio-Temporal Deep Learning Framework

תקציר מקורי באנגליתarXiv:2509.06625v3 Announce Type: replace-cross Abstract: Plants in their natural habitats endure an array of interacting stresses, both biotic and abiotic, that rarely occur in isolation. Nutrient stress-particularly nitrogen deficiency-becomes even more critical when compounded with drought and weed competition, making it increasingly difficult to distinguish and address its effects. Early detection of nitrogen stress is therefore crucial for protecting plant health and implementing effective management strategies. This study proposes a novel deep learning framework to accurately classify nitrogen stress severity in a combined stress environment. Our model uses a unique blend of four imaging modalities-RGB, multispectral, and two infrared wavelengths-to capture a wide range of physiologi