The leaf area index (LAI) is a crucial metric for indicating crop development in the field, essential for both research and the practical implementation of precision agriculture. Unmanned aerial vehicles (UAVs) are widely used for monitoring crop growth due to their rapid, repetitive capture ability and cost-effectiveness. Therefore, we developed a non-destructive monitoring method for peanut LAI, combining UAV vegetation indices (VI) and texture features (TF). Field experiments were conducted to capture multispectral imagery of peanut crops. Based on these data, an optimal regression model was constructed to estimate LAI. The initial computation involves determining the potential spectral and textural characteristics. Subsequently, a comprehensive correlation study between these features and peanut LAI is conducted using Pearson’s product component correlation and recursive feature elimination. Six regression models, including univariate linear regression, support vector regression, ridge regression, decision tree regression, partial least squares regression, and random forest regression, are used to determine the optimal LAI estimation. The following results are observed: (1) Vegetation indices exhibit greater correlation with LAI than texture characteristics. (2) The choice of GLCM parameters for texture features impacts estimation accuracy. Generally, smaller moving window sizes and higher grayscale quantization levels yield more accurate peanut LAI estimations. (3) The SVR model using both VI and TF offers the utmost precision, significantly improving accuracy (R2 = 0.867, RMSE = 0.491). Combining VI and TF enhances LAI estimation by 0.055 (VI) and 0.541 (TF), reducing RMSE by 0.093 (VI) and 0.616 (TF). The findings highlight the significant improvement in peanut LAI estimation accuracy achieved by integrating spectral and textural characteristics with appropriate parameters. These insights offer valuable guidance for monitoring peanut growth.

Flavor profiles of various Pyrus spp. cultivars exhibit significant variations, yet the underlying flavor-contributing factors remain elusive. In this investigation, a comprehensive approach encompassing metabolomics analysis, volatile fingerprint analysis, and descriptive sensory analysis was employed to elucidate the flavor disparities among Nanguoli, Korla fragrant pear, and Qiuyueli cultivars and uncover potential flavor contributor. The study comprehensively characterized the categories and concentrations of nonvolatile and volatile metabolites, and 925 metabolites were identified. Flavonoids and esters dominated the highest cumulative response, respectively. Utilizing weighted correlation network analysis (WGCNA), seven highly correlated modules were identified, yielding 407 pivotal metabolites. Further correlation analysis of the differential substances provided potential flavor constituents strongly associated with various sensory attributes; taste factors had a certain association with olfactory characteristics. Our findings demonstrated the manifestation of flavor was a result of the synergistic effect of various compounds; evaluation olfactory flavor necessitated a comprehensive consideration of taste substances.

Unmanned aerial vehicles (UAV) has been increasingly popular in the fields of crop growth monitoring, with their practical advantages, such as their ability of low-cost, rapid and repetitive capture. LAI is a key indicator for evaluating crop population growth and canopy structures and its accurate measurements would be of significance in the modern precision agriculture research. Therefore, we develop a peanut leaf area index estimation method based on fusion of texture and spectral information from UAV multispectral imagery. In our work, we compared the performance of LAI estimation using spectral and textural characteristics, and explore the potential of integrating them for estimating peanut LAI. Following this, LAI estimation models are constructed using spectral, textural characteristics, and a combination of spectral and textural characteristics based on some frequently-used statistical models. The results indicate that compared with only spectral or textural characteristics, fusion of both achieves better accuracy, which suggests that the texture features offer the critical information for improving the peanut LAI estimation accuracy. In addition, in order to select the best model, we compared four machine learning models and found that Support Vector Regression (SVR) is an optimal approach for the peanut LAI estimation, with higher R 2 (=0.817) and lower RMSE (=0.639) values for different feature sets. What’s more, the key parameters for computing texture features would affect the estimation performance, where the fluctuation is slight from the grayscale. Our observations would have important inspirations for the statistical model-based science discovery in the field phenotyping inversion.

The flavor profiles of cherries cultivated in greenhouse and those grown in open fields show significant variations, however, the underlying flavor-contributing factors remain unidentified. Hence, a joint investigation with widely targeted metabolomics analysis, volatile fingerprint analysis, and descriptive sensory analysis for the Russia 8 and Tieton cherry cultivars was conducted using UPLC-MS/MS and GC × GC-TOFMS to clarify the flavor differences of open-air and greenhouse-grown cherries. The study found that open-air cultivation could lead to the accumulation of non-volatile flavor substances and prompted appearance of higher acidity, astringency, plum-like flavor, and fresh herb notes; most of differential metabolites were significantly positively correlated with astringency, plum-like flavor and bitterness. Through correlation analysis and path analysis, potential flavor components and key important pathways contributing to flavor disparities were provided, and light intensity, soil moisture content, temperature and humidity were inferred as the main factors affecting the flavor profiles of open-air and greenhouse-grown cherries.

As an important oil crop, timely and accurate estimation of biochemical parameters in peanuts is crucial for improving yield. Although hyperspectral inversion algorithms for biochemical parameters have been proven effective for a few varieties, their accuracy and applicability for high-throughput phenotyping varieties have not been fully validated. In this study, the single vegetation index method (SVI) and the random forest model (RF) were constructed to invert biochemical parameters (chlorophyll content (Cab), equivalent water thickness (Cw), and dry matter content (Cm)) and to assess the accuracy and transferability of these models across different peanut varieties. Results showed that: (1) vegetation indices of TCARI, WI and mSR were strongly correlated with Cab, Cw and Cm, respectively; (2) RF models of estimating Cab (R 2 = 0.77, RMSE = 8.14 μg cm -2 ), Cw (R 2 = 0.67, RMSE = 1.1×10 -3 g cm -2 ), and Cm (R 2 = 0.50, RMSE = 6.2×10 -4 g cm -2 ) demonstrated higher accuracy than the SVI models; (3) SVI models performed superior to RF models with transferability in high-throughput phenotyping varieties, with R 2 of 0.58 (Cab), 0.10 (Cw) and 0.19 (Cm), respectively. This study accurately estimated the biochemical parameters of peanut leaves using hyperspectral remote sensing, providing a basis for the estimation of biochemical parameters in peanuts.

Microplastics (MPs) pollution severely threatened the healthy development of modern agriculture. However, the underlying mechanisms of the effects of MPs on the growth and nutrition uptake of crops, especially peanuts, remain unclear. In this study, the effect of two types of MPs on the nitrogen uptake of peanut plants ( Arachis hypogaea L.) was investigated. The results showed that MPs inhibited the vegetative growth and N uptake of the peanut plants by damaging root cells and influencing soil N cycling. The direct contact between MPs and peanut roots damaged root cells and increased the level of oxidative stress. Also, the decreased number of vessels due to the oxidative stress reduced the N uptake of peanut plants. Integrated metagenomic with metabolomic analyses revealed that the differential soil metabolites affected the rhizospheric N cycling key genes and the functional microbial community structure, resulting in the altered N transformation and the decreased soil available N content. To the best of our knowledge, this is the first study to elucidate the possible mechanisms of the effects of MPs on the peanut N uptake. Meanwhile, the current study sheds light on the importance of rational management of MPs for crop growth and yield in agroecosystems.