Visually nonidentifiable pathological symptoms at an early stage are a major limitation in agricultural plantations. Thickness reduction in palisade parenchyma (PP) and spongy parenchyma (SP) layers is one of the most common symptoms that occur at the early stage of leaf diseases, particularly in apple and persimmon. To visualize variations in PP and SP thickness, we used optical coherence tomography (OCT)-based imaging and analyzed the acquired datasets to determine the threshold parameters for pre-identifying and estimating persimmon and apple leaf abnormalities using an intensity-based depth profiling algorithm. The algorithm identified morphological differences between healthy, apparently-healthy, and infected leaves by applying a threshold in depth profiling to classify them. The qualitative and quantitative results revealed changes and abnormalities in leaf morphology in addition to disease incubation in both apple and persimmon leaves. These can be used to examine how initial symptoms are influenced by disease growth. Thus, these datasets confirm the significance of OCT in identifying disease symptoms nondestructively and providing a benchmark dataset to the agriculture community for future reference.

Whole-directional scanning methodology is required to observe distinctive features of an entire physical structure with a three dimensional (3D) visualization. However, the implementation of whole-directional scanning is challenging for conventional optical coherence tomography (OCT), which scans a limited portion of the sample by utilizing unidirectional and bidirectional scanning methods. Therefore, in this paper an integrated quad-scanner (QS) strategy-based OCT method was implemented to obtain the whole-directional volumetry of a sample by employing four scanning arms installed around the sample. The simultaneous and sequential image acquisition capabilities are the conceptual key points of the proposed QS-OCT method, and were implemented using four precisely aligned scanning arms and applied in a complementary way according to the experimental criteria. To assess the feasibility of obtaining whole-directional morphological structures, a roll of Scotch tape, an ex vivo mouse heart, and kidney specimens were imaged and independently obtained tissue images at different directions were delicately merged to compose the 3D volume data set. The results revealed the potential merits of QS-OCT-based whole-directional imaging, which can be a favorable inspection method for various discoveries that require the dynamic coordinates of the whole physical structure.

Dental crowns are used to restore decayed or chipped teeth, where their surfaces play a key role in this restoration process, as they affect the fitting and stable bonding of the prostheses. The surface texture of crowns can interfere with this restoration process, therefore the measurement of their inner surface roughness is very important but difficult to achieve using conventional imaging methods. In this study, the inner surfaces of dental crowns were three-dimensionally (3D) visualized using swept-source optical coherence tomography (SS-OCT) system. Nine crowns were fabricated with a commercial 3D printer using three different hatching methods (one-way, cross, and 30° angle counterclockwise) and three different build direction angles (0°, 45°, and 90°). In addition, an image processing algorithm was developed, which uses morphological filtering, boundary detection, and a high-pass frequency filtering technique, to quantitatively evaluate the inner surface roughness of the dental crowns cross-sections with the depth-of-focus set to match two different regions. The averaged smoothness of fabricated crown was effectively produced using the cross-hatching and the build direction angle of 90° by the respective process. Thus, the results confirm the potential use of this methodology to determine the best parameters to use in 3D fabrication for improving the effectiveness and stability of dental prostheses. INDEX TERMS 3D printing, dental crown, image processing, optical coherence tomography, surface roughness.

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Rapid climate change has increased the incidence of various pests and diseases, and these threaten global food security. In particular, BLB (bacterial leaf blight) is caused by Xoo (Xanthomonas oryzae pv. oryzae) and its main characteristic is that the rice suddenly dries and withers. Recently, omics have been effectively used in agriculture. In particular, it is a key technology that can accurately diagnose diseases in the field. Until now, QTL (quantitative trait loci) mapping has been analyzed using only subjective phenotypic data by experts. However, in this study, diseases were accurately diagnosed using OCT (optical coherence tomography), and QTL mapping was performed using leaf thickness and leaf angles after Xoo inoculation. After Xoo inoculation of a 120 Cheongcheong/Nagdong double haploid (CNDH) population, QTL mapping was performed using the changing leaf angle, and OsWRKY34q1 was detected in RM811-RM14323 of chromosome 1. OsWRKY34q1 always had a higher expression level in the BLB-resistant population than in the susceptible population after Xoo inoculation. OsWRKY34q1 belongs to the WRKY family of genes. OsWRKY34q1 could be effectively used to develop BLB-resistant rice varieties in response to the current era of unpredictable climate change.