Lingfeng Duan

Bio: Lingfeng Duan is an academic researcher from Huazhong Agricultural University. The author has contributed to research in topics: Panicle & Threshing. The author has an hindex of 13, co-authored 30 publications receiving 1024 citations. Previous affiliations of Lingfeng Duan include Huazhong University of Science and Technology.

Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice

Abstract: Next-generation sequencing technology has made the generation of huge amounts of genetic data possible, but phenotype characterization remains slow and difficult. Here the authors develop a high-throughput phenotyping facility for rice that is able to accurately identify and characterize traits related to morphology, biomass and yield.

High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth

Abstract: With increasing demand for novel traits in crop breeding, the plant research community faces the challenge of quantitatively analyzing the structure and function of large numbers of plants. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In this study, we quantified 106 traits from a maize (Zea mays) recombinant inbred line population (n = 167) across 16 developmental stages using the automatic phenotyping platform. Quantitative trait locus (QTL) mapping with a high-density genetic linkage map, including 2,496 recombinant bins, was used to uncover the genetic basis of these complex agronomic traits, and 988 QTLs have been identified for all investigated traits, including three QTL hotspots. Biomass accumulation and final yield were predicted using a combination of dissected traits in the early growth stage. These results reveal the dynamic genetic architecture of maize plant growth and enhance ideotype-based maize breeding and prediction.

Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization

Abstract: Rice panicle phenotyping is important in rice breeding, and rice panicle segmentation is the first and key step for image-based panicle phenotyping. Because of the challenge of illumination differentials, panicle shape deformations, rice accession variations, different reproductive stages and the field’s complex background, rice panicle segmentation in the field is a very large challenge. In this paper, we propose a rice panicle segmentation algorithm called Panicle-SEG, which is based on simple linear iterative clustering superpixel regions generation, convolutional neural network classification and entropy rate superpixel optimization. To build the Panicle-SEG-CNN model and test the segmentation effects, 684 training images and 48 testing images were randomly selected, respectively. Six indicators, including Qseg, Sr, SSIM, Precision, Recall and F-measure, are employed to evaluate the segmentation effects, and the average segmentation results for the 48 testing samples are 0.626, 0.730, 0.891, 0.821, 0.730, and 76.73%, respectively. Compared with other segmentation approaches, including HSeg, i2 hysteresis thresholding and jointSeg, the proposed Panicle-SEG algorithm has better performance on segmentation accuracy. Meanwhile, the executing speed is also improved when combined with multithreading and CUDA parallel acceleration. Moreover, Panicle-SEG was demonstrated to be a robust segmentation algorithm, which can be expanded for different rice accessions, different field environments, different camera angles, different reproductive stages, and indoor rice images. The testing dataset and segmentation software are available online. In conclusion, the results demonstrate that Panicle-SEG is a robust method for panicle segmentation, and it creates a new opportunity for nondestructive yield estimation.

A novel machine-vision-based facility for the automatic evaluation of yield-related traits in rice

Abstract: The evaluation of yield-related traits is an essential step in rice breeding, genetic research and functional genomics research. A new, automatic, and labor-free facility to automatically thresh rice panicles, evaluate rice yield traits, and subsequently pack filled spikelets is presented in this paper. Tests showed that the facility was capable of evaluating yield-related traits with a mean absolute percentage error of less than 5% and an efficiency of 1440 plants per continuous 24 h workday.

Future scenarios for plant phenotyping.

Abstract: With increasing demand to support and accelerate progress in breeding for novel traits, the plant research community faces the need to accurately measure increasingly large numbers of plants and plant parameters. The goal is to provide quantitative analyses of plant structure and function relevant for traits that help plants better adapt to low-input agriculture and resource-limited environments. We provide an overview of the inherently multidisciplinary research in plant phenotyping, focusing on traits that will assist in selecting genotypes with increased resource use efficiency. We highlight opportunities and challenges for integrating noninvasive or minimally invasive technologies into screening protocols to characterize plant responses to environmental challenges for both controlled and field experimentation. Although technology evolves rapidly, parallel efforts are still required because large-scale phenotyping demands accurate reporting of at least a minimum set of information concerning experiment...

A review of imaging techniques for plant phenotyping.

Abstract: Given the rapid development of plant genomic technologies, a lack of access to plant phenotyping capabilities limits our ability to dissect the genetics of quantitative traits. Effective, high-throughput phenotyping platforms have recently been developed to solve this problem. In high-throughput phenotyping platforms, a variety of imaging methodologies are being used to collect data for quantitative studies of complex traits related to the growth, yield and adaptation to biotic or abiotic stress (disease, insects, drought and salinity). These imaging techniques include visible imaging (machine vision), imaging spectroscopy (multispectral and hyperspectral remote sensing), thermal infrared imaging, fluorescence imaging, 3D imaging and tomographic imaging (MRT, PET and CT). This paper presents a brief review on these imaging techniques and their applications in plant phenotyping. The features used to apply these imaging techniques to plant phenotyping are described and discussed in this review.

Seed priming: state of the art and new perspectives.

Abstract: Priming applied to commercial seed lots is widely used by seed technologists to enhance seed vigour in terms of germination potential and increased stress tolerance. Priming can be also valuable to seed bank operators who need improved protocols of ex situ conservation of germplasm collections (crop and native species). Depending on plant species, seed morphology and physiology, different priming treatments can be applied, all of them triggering the so-called ‘pre-germinative metabolism’. This physiological process takes place during early seed imbibition and includes the seed repair response (activation of DNA repair pathways and antioxidant mechanisms), essential to preserve genome integrity, ensuring proper germination and seedling development. The review provides an overview of priming technology, describing the range of physical–chemical and biological treatments currently available. Optimised priming protocols can be designed using the ‘hydrotime concept’ analysis which provides the theoretical bases for assessing the relationship between water potential and germination rate. Despite the efforts so far reported to further improve seed priming, novel ideas and cutting-edge investigations need to be brought into this technological sector of agri-seed industry. Multidisciplinary translational research combining digital, bioinformatic and molecular tools will significantly contribute to expand the range of priming applications to other relevant commercial sectors, e.g. the native seed market.