A Master’s thesis entitled “Integrating Some Imaging Techniques and Artificial Intelligence Applications for the Early Detection of Environmental Stress in Wheat and Oat Plants” was discussed at the College of Science, Al-Qadisiyah University.

The thesis, by researcher Shams Adel Aziz Al-Lami and supervised by Professor Dr. Maha Ali Abdul-Amir, aimed to evaluate and compare the efficiency of visible spectral imaging and thermal imaging techniques in the early detection of water stress, heavy metal (cadmium) stress, and biological stress (induced by Pseudomonas aeruginosa bacteria) in wheat and oat plants. The results of the study showed that thermal imaging has high sensitivity in the early detection of environmental stresses (water, heavy metal, and biological) in both wheat and oat plants, recording a significant increase in red color intensity before the appearance of visible symptoms. The most important recommendation is to use thermal imaging in field monitoring as a primary tool for the early detection of plant stresses, given its high sensitivity in detecting physiological changes before the appearance of visible symptoms.