# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
## 4 Discussion  
In this experiment, eight groups of corn samples were scanned and analyzed using X-ray tomography (CT). The samples were examined at incubation times of 0, 5, 10, and 15 days (0 d, 5 d, 10 d, 15 d). The geometrical models of the embryo, endosperm, and seed grain of the eight corn samples were reconstructed at each of the four incubation stages. The reconstructed models and two-dimensional grayscale images of the embryo, endosperm, husk, and seed kernel of corn sample 8 were analyzed using qualitative analysis. The models of the embryo at incubation times from 0 to 15 days revealed a gradual increase in damage to the corn embryo during the pre-molding stage. The internal infection of the corn kernel originated from the embryo and spread gradually to the endosperm. This study also examined the geometric changes in the embryo, endosperm, seed coat, internal pores, and cracks in the corn; however, the findings were inconclusive. This finding aligns with a previous study indicating that maize embryos contain a significant amount of mold and are rich in nutrients. Additionally, it suggests that maize mold originates from the embryo covering. Utilizing quantitative analysis methods to investigate mold infestation in corn following alterations in its corn kernel embryo, endosperm, and kernel volume parameters, it was determined that, over time, eight groups of corn kernel samples exhibited growth followed by a decrease in the parameters of the embryo, endosperm, seed coat, and kernel volume. The observed non-linear pattern of change was attributed to  
Figure 11: Classification chart of mold degree of moldy corn kernels.  
the corn mold's hygroscopic nature during the process and the metabolic activity of mold. Mold metabolism in maize led to the decomposition of the corn's chemical composition, consumption of free oxygen atoms inside the corn kernel, decomposition of water and CO\({}_{2}\), expansion of the corn's biological macromolecules, and reproduction of spores. Utilizing CT scanning to non-destructively acquire sliced images of corn kernels, in conjunction with the deep learning ResNet50 model for identifying and detecting moldy corn, resulted in an identification accuracy exceeding 93%. This approach enabled the use of X-ray tomography (CT) to rapidly detect corn mold by analyzing a set of sliced kernel images and classifying the degree of mold contamination in the corn. Consequently, it allowed for the accurate prediction of whether the sampled corn was moldy or not. The study of corn kernels using CT scanning combined with the ResNet50 deep learning model has the following expectations:  
(1) Research on characterizing and rapidly detecting other defects of corn kernels, such as insect damage and germination, needs to be expanded in the next step.  
(2) In this study, the accuracy of mold detection reached 93%. How we can enhance the algorithm model to further improve the accuracy of mold detection in corn kernels now needs to be determined.  
(3) The feasibility of utilizing this method to detect defects in wheat, soybeans, broad beans, and other crops needs to be explored and analyzed.  
(4) The process of mold infestation of corn kernels and different types of mold damage to corn characteristics still need to be further explored.