ABSTRACT
In view of the limitation of scale on the spatial structure of ground objects and the problem that traditional watershed segmentation tends to produce crown over-segmentation, we proposed a segmentation method of Camellia oleifera crown based on the optimized watershed with multi-scale markers, with the C. oleifera base in Mingyue Village of Changsha County as the research object. Firstly, the high-resolution unmanned aerial vehicle (UAV) was used to collect images. The image features were analyzed to construct the classification system of C. oleifera, and the distribution area of C. oleifera was extracted. After being extracted by multi-scale region iterative growth, the crown markers were applied to the multi-threshold scale watershed transformation. Combined with Johnson index, the optimal scale of crown marker growth and watershed threshold was used to realize the accurate identification of individual trees. The results showed that the relative error between the method of optimized watershed with multi-scale markers and the visual interpretation of the reference value of tree-crown was 9.4% for the separation of individual trees. The overall identification accuracy of each tree was 89.4%, which was 34.8% higher than that of the traditional watershed segmentation method. The optimal iterative growth scale obtained by Johnson index was 20, while the thre-shold scale of watershed segmentation was 85. Compared with the results of different scale combinations, the crown extraction accuracy under the optimal scale was the highest (R2=0.75). The method of optimized watershed with multi-scale markers could accurately separate C. oleifera crown. Applying this method to UAV image crown segmentation could effectively improve the efficiency of economic forest investigation.
Subject(s)
Camellia , Remote Sensing Technology , Forests , TreesABSTRACT
BACKGROUND: Oil-tea Camellia is a very important edible oil plant widely distributed in southern China. Tea oil extracted from the oil-tea Camellia seeds is beneficial to health and is considered as a health edible oil. We attempt to identify genes related to fatty acid biosynthesis in an oil-tea Camellia seed kernel, generated a comprehensive transcriptome analysis of the seed kernel at different developmental stages, and explore optimal picking time of fruit. Material and Methods. A gas chromatography-mass spectrometer was used to detect the content of various fatty acids in samples. Transcriptome analysis was performed to detect gene dynamics and corresponding functions. RESULTS: Multiple phenotypic data were counted in detail, including the oil content, oleic acid content, linoleic acid content, linolenic acid content, fruit weight, fruit height, fruit diameter, single seed weight, seed length, and seed width in different developmental stages, which indicate that a majority of indicators increased with the development of oil-tea Camellia. The transcriptomics was conducted to perform a comprehensive and system-level view on dynamic gene expression networks for different developmental stages. Short Time-series Expression Miner (STEM) analysis of XL106 (the 6 time points) and XL210 (8 time points) was performed to screen related fatty acid (FA) gene set, from which 1041 candidate genes related to FA were selected in XL106 and 202 related genes were screened in XL210 based on GO and KEGG enrichment. Then, candidate genes and trait dataset were combined to conduct correlation analysis, and 10 genes were found to be strongly connected with several key traits. CONCLUSIONS: The multiple phenotypic data revealed the dynamic law of changes during the picking stage. Transcriptomic analysis identified a large number of potential key regulatory factors that can control the oil content of dried kernels, oleic acid, linoleic acid, linolenic acid, fresh seed rate, and kernel-to-seed ratio, thereby providing a new insight into the molecular networks underlying the picking stage of oil-tea Camellia, which provides a theoretical basis for the optimal fruit picking point.
ABSTRACT
Tea oil derived from seeds of Camellia oleifera Abel. is high-quality edible oil in China. This study isolated full-length cDNAs of Rubisco subunits rbcL and rbcS from C. oleifera. The rbcL has 1,522 bp with a 1,425 bp coding region, encoding 475 amino acids; and the rbcS has 615 bp containing a 528 bp coding region, encoding 176 amino acids. The expression level of the two genes, designated as Co-rbcL and Co-rbcS, was determined in three C. oleifera cultivars: Hengchong 89, Xianglin 1, and Xianglin 14 whose annual oil yields were 546.9, 591.4, and 657.7 kg ha(-1), respectively. The Co-rbcL expression in 'Xianglin 14' was significantly higher than 'Xianglin 1', and 'Xianglin 1' was greater than 'Hengchong 89'. The expression levels of Co-rbcS in 'Xianglin 1' and 'Xianglin 14' were similar but were significantly greater than in 'Hengchong 89'. The net photosynthetic rate of 'Xianglin 14' was significantly higher than 'Xianglin 1', and 'Xianglin 1' was higher than 'Hengchong 89'. Pearson's correlation analysis showed that seed yields and oil yields were highly correlated with the expression level of Co-rbcL at P < 0.001 level; and the expression of Co-rbcS was correlated with oil yield at P < 0.01 level. Net photosynthetic rate was also correlated with oil yields and seed yields at P < 0.001 and P < 0.01 levels, respectively. Our results suggest that Co-rbcS and Co-rbcL in particular could potentially be molecular markers for early selection of high oil yield cultivars. In combination with the measurement of net photosynthetic rates, the early identification of potential high oil production cultivars would significantly shorten plant breeding time and increase breeding efficiency.
ABSTRACT
Genetic interactions or epistasis have been thought to play a pivotal role in shaping the formation, development and evolution of life. Previous work focused on lower-order interactions between a pair of genes, but it is obviously inadequate to explain a complex network of genetic interactions and pathways. We review and assess a statistical model for characterizing high-order epistasis among more than two genes or quantitative trait loci (QTLs) that control a complex trait. The model includes a series of start-of-the-art standard procedures for estimating and testing the nature and magnitude of QTL interactions. Results from simulation studies and real data analysis warrant the statistical properties of the model and its usefulness in practice. High-order epistatic mapping will provide a routine procedure for charting a detailed picture of the genetic regulation mechanisms underlying the phenotypic variation of complex traits.
