Dissection of Hyperspectral Reflectance to Estimate Photosynthetic Characteristics in Upland Cotton (Gossypium hirsutum L.) under Different Nitrogen Fertilizer Application Based on Machine Learning Algorithms.

Hyperspectral technology has enabled rapid and efficient nitrogen monitoring in crops. However, most approaches involve direct monitoring of nitrogen content or physiological and biochemical indicators directly related to nitrogen, which cannot reflect the overall plant nutritional status. Two important photosynthetic traits, the fraction of absorbed photosynthetically active radiation (FAPAR) and the net photosynthetic rate (Pn), were previously shown to respond positively to nitrogen changes. Here, Pn and FAPAR were used for correlation analysis with hyperspectral data to establish a relationship between nitrogen status and hyperspectral characteristics through photosynthetic traits. Using principal component and band autocorrelation analyses of the original spectral reflectance, two band positions (350-450 and 600-750 nm) sensitive to nitrogen changes were obtained. The performances of four machine learning algorithm models based on six forms of hyperspectral transformations showed that the light gradient boosting machine (LightGBM) model based on the hyperspectral first derivative could better invert the Pn of function-leaves in cotton, and the random forest (RF) model based on hyperspectral first derivative could better invert the FAPAR of the cotton canopy. These results provide advanced metrics for non-destructive tracking of cotton nitrogen status, which can be used to diagnose nitrogen nutrition and cotton growth status in large farms.

Genome-wide association study and transcriptome analysis reveal key genes controlling fruit branch angle in cotton.

Fruit branch angle (FBA), a pivotal component of cotton plant architecture, is vital for field and mechanical harvesting. However, the molecular mechanism of FBA formation is poorly understood in cotton. To uncover the genetic basis for FBA formation in cotton, we performed a genome-wide association study (GWAS) of 163 cotton accessions with re-sequencing data. A total of 55 SNPs and 18 candidate genes were significantly associated with FBA trait. By combining GWAS and transcriptome analysis, four genes underlying FBA were identified. An FBA-associated candidate gene Ghi_A09G08736, which is homologous to SAUR46 in Arabidopsis thaliana, was detected in our study. In addition, transcriptomic evidence was provided to show that gravity and light were implicated in the FBA formation. This study provides new insights into the genetic architecture of FBA that informs architecture breeding in cotton.

Genetic Mapping and Analysis of a Compact Plant Architecture and Precocious Mutant in Upland Cotton.

With the promotion and popularization of machine cotton-picking, more and more attention has been paid to the selection of early-maturity varieties with compact plant architecture. The type of fruit branch is one of the most important factors affecting plant architecture and early maturity of cotton. Heredity analysis of the cotton fruit branch is beneficial to the breeding of machine-picked cotton. Phenotype analysis showed that the types of fruit branches in cotton are controlled by a single recessive gene. Using an F2 population crossed with Huaxin102 (normal branch) and 04N-11 (nulliplex branch), BSA (Bulked Segregant Analysis) resequencing analysis and GhNB gene cloning in 04N-11, and allelic testing, showed that fruit branch type was controlled by the GhNB gene, located on chromosome D07. Ghnb5, a new recessive genotype of GhNB, was found in 04N-11. Through candidate gene association analysis, SNP 20_15811516_SNV was found to be associated with plant architecture and early maturity in the Xinjiang natural population. The GhNB gene, which is related to early maturity and the plant architecture of cotton, is a branch-type gene of cotton. The 20_15811516_SNV marker, obtained from the Xinjiang natural population, was used for the assisted breeding of machine-picked cotton varieties.