The College of Science, Al-Qadisiyah University discussed a master dissertation on the integration of imaging techniques and artificial intelligence applications for the early detection of environmental stress in wheat and oat plants by the postgraduate student, Mr. Shams Adul Aziz.

The dissertation aimed at evaluating and comparing the efficiency of visible spectral imaging and thermal imaging techniques in the early detection of water stress, heavy metal stress (such as cadmium) and biological stress (induced by Pseudomonas aeruginosa bacteria) in wheat and oat plants.

The dissertation reviewed 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 dissertation recommended using 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.