Publications by authors named "Keling Tu"
Article Synopsis
- Seeds are vital for agricultural success, influencing seedling quality and crop yields, making accurate vigor assessment essential for productivity.
- The study seeks to create a non-destructive method to evaluate maize seed vigor, overcoming the limitations of traditional testing methods, by using a large set of maize inbred lines and advanced technologies like machine vision and hyperspectral imaging.
- The findings indicate that machine vision is the most effective method for seed vigor detection with about 90% accuracy, and it also uncovers key genetic and metabolic traits linked to seed germination, providing insights into improving seed vigor in maize.
The roots of var. (AMM) and A. membranaceus (AM) are widely used in traditional Chinese medicine.
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- The main methods for producing hybrid wheat involve chemical hybridization and genic male sterility, but achieving complete sterility in female plants is challenging due to various factors, leading to seed purity issues.
- Traditional methods for detecting seed purity are labor-intensive and destructive, prompting the need for a non-destructive classification technique.
- The study utilized hyperspectral imaging and machine learning (PLS-DA) to distinguish between hybrid and female parent seeds, achieving high accuracy rates (up to 98.25%), which shows promise for faster and more efficient seed purity detection in the future.
Background: Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties.
View Article and Find Full Text PDFWheat yield is not only affected by three components of yield, but also affected by plant height (PH). Identification and utilization of the quantitative trait loci (QTL) controlling these four traits is vitally important for breeding high-yielding wheat varieties. In this work, we conducted a QTL analysis using the recombinant inbred lines (RILs) derived from a cross between two winter wheat varieties of China, "Nongda981" (ND981) and "Nongda3097" (ND3097), exhibiting significant differences in spike number per unit area (SN), grain number per spike (GNS), thousand grain weight (TGW), and PH.
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