Tingting Chen

Seed vigor significantly affects peanut breeding and agricultural yield by influencing seed germination and seedling growth and development. Traditional vigor testing methods are inadequate for modern high-throughput assays. Although hyperspectral technology shows potential for monitoring various crop traits, its application in predicting peanut seed vigor is still limited. This study developed and validated a method that combines hyperspectral technology with genome-wide association studies (GWAS) to achieve high-throughput detection of seed vigor and identify related functional genes. Hyperspectral phenotyping data and physiological indices from different peanut seed populations were used as input data to construct models using machine learning regression algorithms to accurately monitor changes in vigor. Model-predicted phenotypic data from 191 peanut varieties were used in GWAS, gene-based association studies, and haplotype analyses to screen for functional genes. Real-time fluorescence quantitative PCR (qPCR) was used to analyze the expression of functional genes in three high-vigor and three low-vigor germplasms. The results indicated that the random forest and support vector machine models provided effective phenotypic data. We identified Arahy.VMLN7L and Arahy.7XWF6F, with Arahy.VMLN7L negatively regulating seed vigor and Arahy.7XWF6F positively regulating it, suggesting distinct regulatory mechanisms. This study confirms that GWAS based on hyperspectral phenotyping reveals genetic relationships in seed vigor levels, offering novel insights and directions for future peanut breeding, accelerating genetic improvements, and boosting agricultural yields. This approach can be extended to monitor and explore germplasms and other key variables in various crops.

Peanut is an important oilseed crop around the world which provides vegetable oil, protein and vitamins for humans. Major latex-like proteins (MLPs) play important roles in plant growth and development, as well as responses to biotic and abiotic stresses. However, their biological function in peanut is still unclear. In this study, a genome-wide identification of MLP genes in cultivated peanut and two diploid ancestor species was analyzed to determine their molecular evolutionary characteristics and the expression profile under drought and waterlogging stress conditions. Firstly, a total of 135 MLP genes were identified from the genome of tetraploid peanut (Arachis hypogaea) and two diploid species Arachis. duranensis and Arachis. ipaensis. Then, phylogenetic analysis revealed that MLP proteins were divided into five different evolutionary groups. These genes were distributed unevenly at the ends of chromosomes 3, 5, 7, 8, 9 and 10 in three Arachis species. The evolution of MLP gene family in peanut was conserved and led by tandem and segmental duplication. The prediction analysis of cis-acting elements showed that the promoter region of peanut MLP genes contained different proportions of transcription factors, plant hormones-responsive elements and so on. The expression pattern analysis showed that they were differentially expressed under waterlogging and drought stress. These results of this study provide a foundation for further research on the function of the important MLP genes in peanut.

Editorial: Advances in peanut research

Estimating plant physiological indicators with remote sensing technology is critical for ensuring precise field management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolution that can be used to clearly identify vegetation. However, the information of UAV image data is relatively complex and difficult to analyze, which is the main problem limiting its large-scale use at present. In order to monitor plant physiological indexes from the multi-spectral data, a new method based on machine learning is studied in this paper. Using UAV for deriving the absorption coefficients of plant canopies and whole leaf area, this paper quantifies the effects of plant physiological indicators such as the soil and plant analyzer development (SPAD) value, whole leaf area, and dry matter accumulation on the relationship between the reflectance spectra. Nine vegetation indexes were then extracted as the sensitive vegetation indexes of the rice physiological indicators. Using the SVM model to predict the SPAD value of the plant, the mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and symmetric mean absolute percentage error (SMAPE) values of the model were 1.90, 1.38, 0.13, 0.86, and 4.13, respectively. The results demonstrate that the rice plants display a considerable biochemical and spectral correlation. Using SVM to predict the SPAD value has a better effect because of a better adaptation and a higher accuracy than other models. This study suggests that the multi-spectral data acquired using UAV can quickly estimate field physiological indicators, which has potential in the pre-visual detection of SPAD value information in the field. At the same time, it can also be extended to the detection and inversion of other key variables of crops.

Fifteen peanut varieties at the pod filling stage were exposed to waterlogging stress for 7 days, the enzyme activities and fluorescence parameters were measured after 7 days of waterlogging and drainage. The waterlogging tolerance and recovery capability of varieties were identified. After waterlogging, waterlogging tolerance coefficient (WTC) of relative electrolyte linkage (REL), malondialdehyde (MDA) content, superoxide dismutase (SOD) activity, and catalase (CAT) activity, non-photochemical quenching (NPQ) and photochemical quenching (qL) of leaves of most peanut varieties were increased, while the WTC of the soil and plant analysis development (SPAD) value, PS II actual quantum yield (Φ_PS II), maximum photochemical efficiency (Fv/Fm) were decreased. After drainage, the WTC of REL, MDA content, SOD and CAT activity of leaves were decreased compared with that of after waterlogging, but these indicators of a few cultivars were increased. Φ_PS II, Fv/Fm and qL can be used as important indexes to identify waterlogging recovery capability. There was a significant negative correlation between recovery capability and the proportion of reduction in yield, while no significant correlation was found between waterlogging tolerance and the proportion of reduction in yield. Therefore, it is recommended to select varieties with high recovery capability and less pod number reduction under waterlogging in peanut breeding and cultivation.

We aimed to elucidate the possible yield-increasing mechanisms through regulation of shade-avoidance responses at both physiological and molecular levels under monoseeding. Our results revealed that monoseeding decreased the main stem height but increased the main stem diameter and the number of branches and nodes compared to the traditional double- and triple-seeding patterns. The chlorophyll contents were higher under monoseeding than that under double- and triple-seeding. Further analysis showed that this, in turn, increased the net photosynthetic rate and reallocated higher levels of assimilates to organs. Monoseeding induced the expression patterns of Phytochrome B (Phy B) gene but decreased the expression levels of Phytochrome A (Phy A) gene. Furthermore, the bHLH transcription factors (PIF 1 and PIF 4) that interact with the phytochromes were also decreased under monoseeding. The changes in the expression levels of these genes may regulate the shade-avoidance responses under monoseeding. In addition, monoseeding increased pod yield at the same population density through increasing the number of pods per plant and 100-pod weight than double- and triple-seeding patterns. Thus, we inferred that monoseeding is involved in the regulation of shade-avoidance responsive genes and reallocating assimilates at the same population density, which in turn increased the pod yield.

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

Waterlogging has a negative effect on peanut production, but few studies have focused on the relationship between the geographical origin and waterlogging tolerance of peanut varieties. To explore this problem, three different peanut ecotypes (Zhanhong 2, Zhongkaihua 1, and Huayu 39) were waterlogged for 5, 10, and 15 days at seedling stage (S), flowering and pegging stage (F), and pod-filling stage (P), respectively. The relationship between the ecotype and waterlogging tolerance was determined by analyzing the effects of waterlogging on dry matter accumulation, photosynthetic characteristics, yield, and the yield components of peanut. The soil and plant analysis development (SPAD), net photosynthetic rate (Pn), stomatal conductance (Gs), transpiration rate (Tr), and intercellular CO₂ concentration (Ci) values in leaves decreased under waterlogging stress, which led to a significant decrease in yield and yield components. The most noticeable effect of waterlogging stress appeared at the P stage and deleterious effects increased with an extension of the duration of waterlogging, where the yield loss was mainly attributed to the decrease in the number of total pods (TP) and the number of full pods (FP). Significant relationships were found between total dry weight (TDW), leaf dry weight (LDW), pod dry weight (PDW), TP, and FP, and the waterlogging stress tolerance index (WTI). Additionally, the waterlogging tolerance of peanut ecotypes is closely related to their geographic origin, where the most waterlogging-resistant ecotype was Zhanhong 2, followed by Zhongkaihua 1 and Huayu 39. Finally, breeding backgrounds and screening indices (SPAD, Pn, dry matter accumulation, and pod characteristics) beneficial to waterlogging tolerance breeding are suggested.