# Characterization and Detection Classification of Moldy Corn Kernels Based on X-CT and Deep Learning
## 1 Introduction  
Corn is one of the primary grain crops in China. The large embryonic part, rich in nutrients, with a high bacterial load and strong respiration, makes it highly susceptible to mold infection. Mold infections significantly contribute to the deterioration of grain quality in both farmers' storage facilities and state grain storage warehouses. The consumption of moldy maize feed by livestock can lead to mold infections in animals, with aflatoxin B1 being a significant factor in causing liver cancer in humans. Every year, China experiences a grain loss of about 70 billion catties, with farmers' grain storage accounting for 8% ofthis loss. Mildew is identified as the primary cause of this loss. Therefore, it is crucial to understand the process of maize mold infection, investigate the changes in the internal characteristics of maize kernels after molding, and accurately detect maize molds to ensure the safety of grain storage and food. The non-destructive detection of changes in the internal characteristics of moldy maize kernels is challenging with conventional mold detection techniques. The detection of moldy maize can be achieved through sensory inspection, but this approach is ineffective and can only detect visible mold spots on the surface. Colony counting, enzyme-linked immunoassay [1], and thin-layer chromatography [2] involve laborious and expensive detection methods. Near-infrared spectroscopy [3] and terahertz [4] are non-destructive testing methods but have limited detection sensitivity, complex modeling challenges, and high technical requirements for test preparation. Electronic temperature measurement [5] and CO2 detection [6,7] show hysteresis in mold detection. Currently, conventional image processing methods are utilized to detect the internal morphological characteristics of maize kernel samples. However, this approach requires the chemical slicing of maize, which is inefficient and environmentally polluting. Furthermore, it cannot provide isotropic information about the three-dimensional structure of a whole maize kernel [8]. In order to intuitively monitor the changes in the morphology of the embryo, endosperm, and grain at each stage of corn kernel development and quickly detect moldy corn kernels in the early stages, a non-destructive testing method that preserves the morphology of corn kernels is needed. X-ray tomography is a new imaging technique in which X-rays are used to scan a sample in a multi-directional spatial stereoscopic manner, and the image data obtained from the scan are reconstructed into a three-dimensional model. This technique is characterized by non-contact, non-destructive scanning and high resolution, and is widely used for the non-destructive testing of materials and the structural investigation of microscopic objects. [PERSON] et al. [9] used this technique to reconstruct the three-dimensional characteristics of wheat grain after germination in three dimensions. The researchers calibrated the volume parameters of the embryo and endosperm of wheat grain, examined the volume changes of the embryo and endosperm before and after germination, and identified that the endosperm gradually decreases while the embryo grows after wheat germination. [PERSON] et al. [10] scanned wheat grains and used this technique to reconstruct a three-dimensional model and export the point cloud data. They integrated it with Geomagic Design for post-processing point cloud data and created a discrete element model, which was subsequently validated through experimental verification. This technology can be used for scanning wheat grain images and in processing methods for discrete element simulation. [PERSON] et al. [11] used X-ray tomography technology to evaluate the effects of moisture-heat coupling aging on recombinant bamboo. The study led to changes in the CT value of the engineered bamboo, demonstrating the effective structural strength characteristics of the engineered bamboo and the loss of moisture-heat coupling aging. [PERSON] et al.