Application Of Imaging And Artificial Intelligence In Seed Research

This article discusses the use of imaging and artificial intelligence to monitor seed quality in an objective, effective, and non-destructive manner. Different imaging techniques, such as multispectral and hyperspectral imaging, digital imaging, laser-induced light backscattering imaging, fluorescence imaging, Raman imaging, X-ray computed tomography, magnetic resonance, microwave imaging, or thermal imaging, can be used to extract information about the external or internal structures of seeds. Image features can provide valuable data about seed characteristics that may be invisible to the naked eye. Machine learning algorithms can be used to develop models that can be effective at identifying varieties and species of seeds, breeding programs, assessing the effects of cultivation conditions on the seed quality, seed grading and sorting, assessing the effects of storage and processing on seed quality, and detecting seed abnormality, defects, or diseases.