Plant height (PH) is closely linked to yield potential, lodging resistance, and mechanized harvesting efficiency in peanut cultivation. However, breeding efforts for optimized PH are hindered by limited understanding of its genetic architecture. In this study, we utilized a UAV-based high-throughput phenotyping platform to monitor the dynamic growth of 241 peanut accessions across four trials. Using UAV-LiDAR data, we precisely measured time-series PH and applied Gaussian fitting and principal component analysis (PCA) to extract five dynamic growth parameters: parameter a (maximum plant height), b (time to reach maximum height), c (variation extent of PH), (interpreted as average height), and (growth rate). Genome-wide association studies (GWAS) identified 1133 candidate genes associated with parameters a, b, c, and, and differential expression of genes (DEGs) analysis combined with weighted correlation network analysis (WGCNA) further identified Arahy.1026BX as a candidate gene. This gene is involved in the shikimate pathway and is crucial for the synthesis of auxin and lignin. Reverse transcription quantitative real-time PCR (RT-qPCR) and virus-induced gene silencing (VIGS) experiments validated the significant effect of Arahy.1026BX on peanut PH. Overall, our study integrates advanced UAV-LiDAR time-series phenotyping with genome-wide association study to identify potential candidate genes associated with PH, which providing valuable breeding insights for developing peanut varieties with ideal PH and improving peanut yield.

This study investigated the effect of magnesium application on peanut growth and yield under two nitrogen (N) application rates in acidic soil in southern China. The chlorophyll content, net photosynthetic rate and dry matter accumulation of the N-sensitive cultivar decreased under reduced N treatments, whereas no effect was observed on the relevant indicators in the N-insensitive variety GH1026. Mg application increased the net photosynthetic rate by increasing the expression of genes involved in chlorophyll synthesis and Rubisco activity in the leaves during the pegging stage under 50%N treatment, while no effect on the net photosynthetic rate was observed under the 100%N treatment. The rate of dry matter accumulation at the early growth stage, total dry matter accumulation and pod yield at harvest increased after Mg application under 50%N treatment by increasing the transportation of assimilates from stems and leaves to pods in both peanut varieties, whereas no effect was found under 100%N treatment. Moreover, Mg application increased the NUE under 50%N treatment. No improvement of NUE in either peanut variety was found under 100%N treatment, while Mg application under the 50%N treatment can obtain a higher economic benefit than the 100%N treatment. In acidic soil, application of 307.5 kg ha⁻¹ of Mg sulfate fertilizer under 50% reduced nitrogen application is a suitable fertilizer management measure for improving carbon assimilation, NUE and achieve high peanut yields in southern China.

Quantitative inversion is a major topic in remote sensing science. The development of visible light-based hyperspectral reconstruction techniques has opened novel prospects for low-cost, high-precision remote sensing inversion in agriculture. The aim of this study was to assess the effectiveness of hyperspectral reconstruction technology in agricultural remote sensing applications. Hyperspectral images were reconstructed using the MST++ hyperspectral reconstruction model and compared with the original visible light images in terms of their correlations with physiological parameters, the accuracy of single-feature modeling, and the accuracy of combined feature modeling. The results showed that compared to the visible light image, the reconstructed data exhibited a stronger correlation with the measured physiological parameters, and the accuracy was improved for both the single feature and combined feature inversion modes. However, compared to multispectral sensors, hyperspectral reconstruction provided limited improvement of the inversion model accuracy. The results suggest that for physiological parameters that are not easy to observe directly, deep mining of features in visible light data through hyperspectral reconstruction technology can improve the accuracy of the inversion model. However, appropriate feature selection and simple models are more suitable for the remote sensing inversion task of traditional agronomic plot experiments. To strengthen the application of hyperspectral reconstruction technology in agricultural remote sensing, further development is necessary with broader wavelength ranges and more diverse agricultural scenarios.