Related Experiment Video
Updated: Sep 10, 2026

High-Throughput, In-Field Screening of Photosynthetic Efficiency in Crop Plants Using an Autonomous Robot
Published on: January 9, 2026
Joint validation of yield and inter-annual consistency and elite lines selection in 52 advanced wheat lines based on
Yinghao Zhang1,2, Mingjun Ai1,2, Shuaiguo Ma1,2
1College of Agriculture, Tarim University, Alar, China.
Abstract:
In arid-region wheat breeding, environmental fluctuations are large, and screening based on single traits or single-year data is often inaccurate. To address these challenges, this study constructed a multi-model collaborative evaluation system to accurately screen advanced lines for high yield potential and inter-annual consistency. Based on 10 yield traits evaluated in 52 advanced wheat lines over two growing seasons, we utilized systematic clustering to classify lines and stepwise regression to pinpoint core contributing traits. Subsequently, principal component analysis (PCA), membership functions, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) were integrated for joint validation, enabling the robust identification of lines with superior and inter-annually consistent yield potential. The results showed that: (1) TD-48 and TD-26 ranked among the top ten across all three evaluation methods in both experimental years, showing strong potential for high yield and inter-annual consistency. (2) Cluster analysis divided all tested lines into six categories: High-yield and inter-annually consistent type, High-yield but inter-annually fluctuating type, High-yield type, Medium-yield type, Low-yield type, and Low-yield sensitive type. TD-48 and TD-26 were classified into the high-yield and inter-annually consistent group. (3) A predictive regression model was established via stepwise regression: D = 0.16 + 0.294GY+0.123GWS+0.064TKW+0.107GNP+0.054SNP+0.018BSSN (R2=0.983, P<0.001). Accordingly, grain yield (GY in t/ha), grain weight per spike (GWS in g), thousand kernel weight (TKW in g), grain number per plant (GNP), spike number per plant (SNP), and basal sterile spikelet number (BSSN) were identified as the core evaluation indicators. The multi-model consensus screening system established in this study can effectively overcome the limitations of a single-method evaluation. Providing reliable methodological support and valuable candidate germplasm resources for the accurate evaluation and efficient screening of breeding materials in arid regions.
Related Concept Videos
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Plant Breeding and Biotechnology
Punnett Squares
Punnett Squares
Multiple Allele Traits
Multiple Allele Traits