Accurately obtaining the spatial distribution information of fruit tree planting is of great significance to the development of fruit tree growth monitoring, disease and pest control, and yield estimation. In this study, the Sentenel-2 multispectral remote sensing imageries of different months during the growth period of the fruit trees were used as the data source, and single month vegetation indices, accumulated monthly vegetation indices (∑VIs), and difference vegetation indices between adjacent months (∆VIs) were constructed as input variables. Four conventional vegetation indices of NDVI, PSRI, GNDVI, and RVI and four improved vegetation indices of NDVIre1, NDVIre2, NDVIre3, and NDVIre4 based on the red-edge band were selected to construct a decision tree classification model combined with machine learning technology. Through the analysis of vegetation indices under different treatments and different months, combined with the attribute of Feature_importances_, the vegetation indices of different periods with high contribution were selected as input features, and the Max_depth values of the decision tree model were determined by the hyperparameter learning curve. The results have shown that when the Max_depth value of the decision tree model of the vegetation indices under the three treatments was 6, 8, and 8, the model classification was the best. The accuracy of the three vegetation index processing models on the training set were 0.8936, 0.9153, and 0.8887, and the accuracy on the test set were 0.8355, 0.7611, and 0.7940, respectively. This method could be applied to remote sensing classification of fruit trees in a large area, and could provide effective technical means for monitoring fruit tree planting areas with medium and high resolution remote sensing imageries.

Arctium lappa L. is a medicinal edible homologous plant, commonly known as burdock or bardana, which belongs to the Asteraceae family and is abundant all over the world. Genetic diversity assessment is essential for A. lappa germplasm resource conservation and breeding. The assessment techniques include morphological, biochemical, and DNA marker analysis. However, the limited number of available DNA markers is insufficient to conduct related genetic diversity assessment studies. In this study, we conducted RNA sequencing of the A. lappa cultivar 'Yanagikawa Ideal' and developed SSR markers to characterize the genetic diversity and population structure of 56 A. lappa accessions and 8 wild relative accessions. A total of 4,851 simple sequence repeats (SSRs) loci were identified. The proportions of mono-, di- and tri-nucleotide repeat motifs were 30.40%, 21.50% and 33.10%, respectively. We developed and verified the reliability of 28 SSR core primer pairs through electronic polymerase chain reaction (ePCR) and the PCR amplification process. The polymorphism information content (PIC) values of the 28 SSR core primer pairs ranged from 0.246 to 0.848, with 14 pairs of SSR primers displaying high polymorphism (PIC > 0.5). The 28 SSR core primer pairs showed 100% mobility in Arctium tomentosum Miller and 96.43% mobility in Synurus deltoides (Aiton) Nakai, indicating their high versatility. The average Shannon information index (I) was 1.231, and the average observed heterozygosity (Ho) was 0.132, the average expected heterozygosity (He) was 0.564. The 64 accessions were divided into three clusters at a genetic distance of 0.558. AMOVA analysis shows 83% genetic variation within populations and 17% among populations, highlighting implications for conservation and breeding strategies. Our study provides 28 newly high-quality SSR markers to enhance genetic resource conservation and breeding programs for A. lappa, as well as to support comparative genomics and cross-species breeding strategies for related species.

The saccharification of sweetpotato storage roots is a common phenomenon in the cooking process, which determines the edible quality of table use sweetpotato. In the present study, two high saccharified sweetpotato cultivars (Y25, Z13) and one low saccharified cultivar (X27) in two growth periods (S1, S2) were selected as materials to reveal the molecular mechanism of sweetpotato saccharification treated at high temperature by transcriptome sequencing and non-targeted metabolome determination. The results showed that the comprehensive taste score, sweetness, maltose content and starch change of X27 after steaming were significantly lower than those of Y25 and Z13. Through transcriptome sequencing analysis, 1918 and 1520 differentially expressed genes were obtained in the two periods of S1 and S2, respectively. Some saccharification-related transcription factors including MYB families, WRKY families, bHLH families and inhibitors were screened. Metabolic analysis showed that 162 differentially abundant metabolites related to carbohydrate metabolism were significantly enriched in starch and sucrose capitalization pathways. The correlation analysis between transcriptome and metabolome confirmed that the starch and sucrose metabolic pathways were significantly co-annotated, indicating that it is a vitally important metabolic pathway in the process of sweetpotato saccharification. The data obtained in this study can provide valuable resources for follow-up research on sweetpotato saccharification and will provide new insights and theoretical basis for table use sweetpotato breeding in the future.

Stress-associated protein (SAP) genes—encoding A20/AN1 zinc-finger domain-containing proteins—play pivotal roles in regulating stress responses, growth, and development in plants. They are considered suitable candidates to improve abiotic stress tolerance in plants. However, the SAP gene family in sweetpotato (Ipomoea batatas) and its relatives is yet to be investigated. In this study, 20 SAPs in sweetpotato, and 23 and 26 SAPs in its wild diploid relatives Ipomoea triloba and Ipomoea trifida were identified. The chromosome locations, gene structures, protein physiological properties, conserved domains, and phylogenetic relationships of these SAPs were analyzed systematically. Binding motif analysis of IbSAPs indicated that hormone and stress responsive cis-acting elements were distributed in their promoters. RT-qPCR or RNA-seq data revealed that the expression patterns of IbSAP, ItbSAP, and ItfSAP genes varied in different organs and responded to salinity, drought, or ABA (abscisic acid) treatments differently. Moreover, we found that IbSAP16 driven by the 35 S promoter conferred salinity tolerance in transgenic Arabidopsis. These results provided a genome-wide characterization of SAP genes in sweetpotato and its two relatives and suggested that IbSAP16 is involved in salinity stress responses. Our research laid the groundwork for studying SAP-mediated stress response mechanisms in sweetpotato.

The worldwide climate changes every year due to global warming, waterlogging, drought, salinity, pests, and pathogens, impedes crop productivity. Brassica napus is one of the most important oil crops in the world, and rapeseed oil is considered one of the most health-beneficial edible vegetable oils. Recently, miRNAs have been found and confirmed to control the expression of targets under disruptive environmental conditions. The mechanism is through the formation of the silencing complex that mediates post-transcriptional gene silencing, which pairs the target mRNA and target cleavage and/or translation inhibition. However, the functional role of miRNAs and targets in B. napus is still not clarified. This review focuses on the current knowledge of miRNAs concerning development regulation and biotic and abiotic stress responses in B. napus. Moreover, more strategies for miRNA manipulation in plants are discussed, along with future perspectives, and the enormous amount of transcriptome data available provides cues for miRNA functions in B. napus. Finally, the construction of the miRNA regulatory network can lead to the significant development of climate change-tolerant B. napus through miRNA manipulation.

Remote sensing has been used as an important means of monitoring crop growth, especially for the monitoring of the formation of crop yield in the middle and late growth period. The information acquisition on the yield formation period of winter wheat is of great significance for winter wheat growth monitoring, yield estimation and scientific management. Hence, the main goal of this study was to verify the possibility of monitoring the grain-filling process of winter wheat and its in-field variability using an alternative non-destructive method based on orbital remote sensing. High-resolution satellite imageries (3 m) were obtained from the PlanetScope platform for three commercial winter wheat fields in Jiangsu Province, China during the reproductive stage of the winter wheat (185–215/193–223/194–224 days after sowing (DAS)). Based on the quantitative analysis of vegetation indices (VIs) obtained from high-resolution satellite imageries and three indicators of the winter wheat grain-filling process, linear, polynomial and logistic growth models were used to establish the relationship between VIs and the three indicators. The research showed a high Pearson correlation (p < 0.001) between winter wheat maturity and most VIs. In the overall model, the remote sensing inversion of the dry thousand-grain weight has the highest accuracy and its R² reaches more than 0.8, which is followed by fresh thousand-grain weight and water content, the accuracies of which are also considerable. The results indicated a great potential to use high-resolution satellite imageries to monitor winter wheat maturity variability in fields and subfields. In addition, the proposed method contributes to monitoring the dynamic spatio-temporality of the grain-filling progression, allowing for more accurate management strategies in regard to winter wheat.