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Canopy coverage-based crop growth monitoring is highly dependent on the performance of crop segmentation algorithms. Under field conditions, crop segmentation for unmanned aerial vehicle (UAV) imagery should be sophisticated considering geometric distortion of images by wind and illumination variations. Under Korean cultivation conditions, a plastic mulch used to restrict weeds and prevent cold weather damage increases the complexity of the image background. In particular, on-site monitoring of onion and garlic growth has been limited by their morphology because they have long narrow leaves. The ultimate goal of this study was to quantify the growth parameters of onion and garlic at multiple growth stages using red, green, and blue (RGB) imagery obtained with UAVs. Canopy coverage and plant height were used as predictor variables to develop mathematical models to estimate the fresh weights of onion and garlic. The use of a CIE L*a*b* color space and mean shift (MS) algorithm enhanced the extraction of the canopy coverage of onion and garlic from complex backgrounds, including plastic mulch, soil, and shadows under varying illumination conditions. Multiple linear regression models consisting of the a* band-based vegetation fraction (VF) and structure from motion (SfM)-based plant height (PH) fitted the fresh weight data of onion and garlic well with high coefficients of determination (R2) ranging from 0.82 to 0.92. The validation results showed an almost 1:1 slope with highly linear relationships (R2 > 0.82) between the onion and garlic fresh weights obtained with the UAV RGB imagery and actual fresh weights, confirming that the UAV-RGB imagery based on the use of the a*band and PH can be used to quantify the spatial and temporal variability of onion and garlic growth parameters during the growing season.

The advent and recent advances of single-cell RNA sequencing (scRNA-seq) have yielded transformative insights into our understanding of cellular diversity in the central nervous system (CNS) with unprecedented detail. However, due to current experimental and computational limitations on defining transcriptomic cell types (T-types) and the multiple phenotypic features of cell types in the CNS, an integrative and multimodal approach should be required for the comprehensive classification of cell types. To this end, performing multimodal analysis of scRNA-seq in hypothalamus would be very beneficial in that hypothalamus, controlling homeostatic and innate survival behaviors which known to be highly conserved across a wide range of species and encoded in hard-wired brain circuits, is likely to display the more straightforward relationship between transcriptomic identity, axonal projections, and behavioral activation, respectively. In my dissertation, I have been focused on the cell type characterizations of a hypothalamic node controlling innate social behavior in mice, the ventrolateral subdivision of the ventromedial hypothalamus (VMHvl). VMHvl only contains ~4,000 neurons per hemisphere in mice but due to its behavioral, anatomical, and molecular heterogeneity, which T-types in VMHvl are related to connectivity and behavioral function is largely unknown. In Chapter II, I described my main thesis work to perform scRNA-seq in VMHvl using two independent platforms: SMART-seq2 (~4,500 neurons sequenced) and 10x (~78,000 neurons sequenced). Specifically, 17 joint VMHvl T-types including several sexually dimorphic clusters were identified by canonical correlation analysis (CCA) in Seurat, and the majority of them were validated by multiplexed single-molecule FISH (seqFISH). Correspondence between transcriptomic identity, and axonal projections or behavioral activation, respectively, was also investigated. Immediate early gene analysis identified T-types ex