Citrus is a large genus that includes several major cultivated species, including C. sinensis (sweet orange), Citrus reticulata (tangerine and mandarin), Citrus limon (lemon), Citrus grandis (pummelo) and Citrus paradisi (grapefruit). In 2009, the global citrus acreage was 9 million hectares and citrus production was 122.3 million tons (FAO statistics, see URLs), which is the top ranked among all the fruit crops. Among the 10.9 million tons (valued at $9.3 billion) of citrus products traded in 2009, sweet orange accounted for approximately 60% of citrus production for both fresh fruit and processed juice consumption (FAO statistics, see URLs). Moreover, citrus fruits and juice are the prime human source of vitamin C, an important component of human nutrition.Citrus fruits also have some unique botanical features, such as nucellar embryony (nucellus cells can develop into apomictic embryos that are genetically identical to mother plant). Consequently, somatic embryos grow much more vigorously than the zygotic embryos in seeds such that seedlings are essentially clones of the maternal parent. Such citrus-unique characteristics have hindered the study of citrus genetics and breeding improvement 1,2 . Complete genome sequences would provide valuable genetic resources for improving citrus crops.Citrus is believed to be native to southeast Asia 3-5 , and cultivation of fruit crops occurred at least 4,000 years ago 3,6 . The genetic origin of the sweet orange is not clear, although there are some speculations that sweet orange might be derived from interspecific hybridization of some primitive citrus species 7,8 . Citrus is also in the order Sapindales, a sister order to the Brassicales in the Malvidae, making it valuable for comparative genomics studies with the model plant Arabidopsis.We aimed to sequence the genome of Valencia sweet orange (C. sinensis cv. Valencia), one of the most important sweet orange varieties cultivated worldwide and grown primarily for orange juice production. Normal sweet oranges are diploids, with nine pairs of chromosomes and an estimated genome size of ~367 Mb 9 . To reduce the complexity of the sequenced genome, we obtained a doublehaploid (dihaploid) line derived from the anther culture of Valencia sweet orange 10 . We first generated whole-genome shotgun pairedend-tag sequence reads from the dihaploid genomic DNA and built a de novo assembly as the citrus reference genome; we then produced shotgun sequencing reads from the parental diploid DNA and mapped the sequences to the haploid reference genome to obtain the complete genome information for Valencia sweet orange. In addition, we conducted comprehensive transcriptome sequencing analyses for four representative tissues using shotgun RNA sequencing (RNA-Seq) to capture all transcribed sequences and paired-end-tag RNA sequencing (RNA-PET) to demarcate the 5′ and 3′ ends of all transcripts. On the basis of the DNA and RNA sequencing data, we characterized the orange genome for its gene content, heterozygosity and evolutionary features. ...

Significantly improved crop varieties are urgently needed to feed the rapidly growing human population under changing climates. While genome sequence information and excellent genomic tools are in place for major crop species, the systematic quantification of phenotypic traits or components thereof in a high-throughput fashion remains an enormous challenge. In order to help bridge the genotype to phenotype gap, we developed a comprehensive framework for highthroughput phenotype data analysis in plants, which enables the extraction of an extensive list of phenotypic traits from nondestructive plant imaging over time. As a proof of concept, we investigated the phenotypic components of the drought responses of 18 different barley (Hordeum vulgare) cultivars during vegetative growth. We analyzed dynamic properties of trait expression over growth time based on 54 representative phenotypic features. The data are highly valuable to understand plant development and to further quantify growth and crop performance features. We tested various growth models to predict plant biomass accumulation and identified several relevant parameters that support biological interpretation of plant growth and stress tolerance. These image-based traits and model-derived parameters are promising for subsequent genetic mapping to uncover the genetic basis of complex agronomic traits. Taken together, we anticipate that the analytical framework and analysis results presented here will be useful to advance our views of phenotypic trait components underlying plant development and their responses to environmental cues.

High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays 'Fernandez') plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.

Due to an increase in the consumption of food, feed, fuel and to meet global food security needs for the rapidly growing human population, there is a necessity to breed high yielding crops that can adapt to the future climate changes, particularly in developing countries. To solve these global challenges, novel approaches are required to identify quantitative phenotypes and to explain the genetic basis of agriculturally important traits. These advances will facilitate the screening of germplasm with high performance characteristics in resource-limited environments. Recently, plant phenomics has offered and integrated a suite of new technologies, and we are on a path to improve the description of complex plant phenotypes. High-throughput phenotyping platforms have also been developed that capture phenotype data from plants in a non-destructive manner. In this review, we discuss recent developments of high-throughput plant phenotyping infrastructure including imaging techniques and corresponding principles for phenotype data analysis.