Mangrove ecosystems play a dominant role in global, tropical, and subtropical coastal wetlands. Remote sensing plays a central role in mangrove conservation, as it is the preferred tool for monitoring changes in spatiotemporal distribution. To improve correlated estimation accuracies and explore the influencing mechanisms based on the mangrove ground survey, this study used a support vector machine (SVM) machine learning and Res-UNet deep learning algorithms to identify the land area of mangrove forests and the crown surface cover area of mangrove forests in the Hainan Island from 1991 to 2021. Both classification techniques were verified by a confusion matrix, which from 1991 to 2021, revealed overall accuracies of 93.11 ± 1.54% and 96.43 ± 1.15% for SVM and Res-UNet, respectively. Res-UNet was more accurate in identifying the crown surface cover area, whereas SVM was more suitable for obtaining the land area. Furthermore, based on the crown surface cover area of the mangrove forests on the Hainan Island, influencing mechanisms were analyzed through dynamic changes and landscape patterns. Since 1991, the Hainan Island mangrove forest area has increased, with the center of mass moving from coastal areas to the ocean and increasing the overall landscape fragmentation. Moreover, the change in the mangrove forests area was correlated with economic development and the increasingly urban population of the entire island. Altogether, the reliable assessment of the tropical mangrove forest land area and crown surface cover provides an important research foundation for the protection and restoration plans of tropical mangrove forests.

Dalbergia odorifera is a rare and precious rosewood specie, whose wood is a very high-quality material for valuable furniture and carving crafts. However, limited information is available about the process of wood formation in D. odorifera. To determine genes that might be closely associated with the xylem differentiation process, we analyzed the differentially expressed genes (DEGs) and microRNAs (miRNAs) from specific xylem tissues of D. odorifera by RNA sequencing (RNA-seq) and small RNA sequencing (small RNA-seq). In total, we obtained 134,221,955 clean reads from RNA-seq and 90,940,761 clean reads from small RNA-seq. By comparing the transition zone (Dotz) and sapwood (Dosw) samples, a total of 395 DEGs were identified. Further analysis revealed that DEGs encoded for WRKY transcription factors (eight genes), lignin synthesis (PER47, COMT, CCR2), cell wall composition (UXS2), gibberellin synthesis (KAO2, GA20OX1), jasmonic acid synthesis (OPR2, CYP74A), and synthesis of flavonoids (PAL2) and terpenoids (CYP71A1). Subsequently, a preliminary analysis by small RNA-seq showed that the expressions of 14 miRNAs (such as miR168a-5p, miR167f-5p, miR167h-5p, miR167e, miR390a, miR156g, novel_52, and novel_9) were significantly different between Dotz and Dosw. Further analysis revealed that the target genes of these differentially expressed miRNAs were enriched in the GO terms “amino acid binding,” “cellulase activity,” and “DNA beta-glucosyltransferase activity”. Further, KEGG pathway annotation showed significant enrichment in “fatty acid elongation” and “biosynthesis of unsaturated fatty acids”. These processes might be participating in the xylem differentiation of D. odorifera. Next, expression correlation analysis showed that nine differentially expressed miRNAs were significantly negatively associated with 21 target genes, which encoded for proteins such as pyrH, SPL6, SPL12, GCS1, and ARF8. Overall, this is the first study on miRNAs and their potential functions in the xylem development of D. odorifera, which provides a stepping stone for a detailed functional investigation of D. odorifera miRNAs.

Bacterial blight (BB) caused by Xanthomonas oryzae pv. oryzae (Xoo) is a major threat to global rice productivity. Although hybrid rice breeding has significantly enhanced yields, persistent genetic vulnerabilities within restorer lines continue to compromise BB resistance. This study addresses this challenge by implementing functional marker-assisted selection (FMAS) to pyramid two broad-spectrum resistance (R) genes, Xa21 and Xa23, into the elite, yet BB-susceptible, restorer line K608R. To enable precise Xa23 genotyping, we developed a novel three-primer functional marker (FM) system (IB23/CB23/IR23). This system complements the established U1/I2 markers used for Xa21. This recombination-independent FMAS platform facilitates simultaneous, high-precision tracking of both homozygous and heterozygous alleles, thereby effectively circumventing the linkage drag limitations typical of conventional markers. Through six generations of marker-assisted backcrossing followed by intercrossing, we generated K608R2123 pyramided lines harboring both R genes in homozygous states, achieving a recurrent parent genome recovery rate of 96.93%, as determined by single nucleotide polymorphism (SNP) chip analysis. The pyramided lines exhibited enhanced resistance against six virulent Xoo pathogenic races while retaining parental yield performance across key agronomic traits. Our FMAS strategy overcomes the historical trade-off between broad-spectrum resistance and the preservation of elite phenotypes, with the developed lines exhibiting resistance coverage complementary to that of both introgressed R genes. This integrated approach provides breeders with a reliable molecular tool to accelerate the development of high-yielding, disease-resistant varieties, demonstrating significant potential for practical deployment in rice improvement programs. The K608R2123 germplasm represents a dual-purpose resource suitable for both commercial hybrid seed production and marker-assisted breeding programs, and it confers synergistic resistance against diverse Xoo races, thereby providing a pivotal breeding resource for sustainable BB control in epidemic regions.

Most rice growth stage predictions are currently based on a few rice varieties for prediction method studies, primarily using linear regression, machine learning, and other methods to build growth stage prediction models that tend to have poor generalization ability, low accuracy, and face various challenges. In this study, multispectral images of rice at various growth stages were captured using an unmanned aerial vehicle, and single-plant rice silhouettes were identified for 327 rice varieties by establishing a deep-learning algorithm. A growth stage prediction method was established for the 327 rice varieties based on the normalized vegetation index combined with cubic polynomial regression equations to simulate their growth changes, and it was first proposed that the growth stages of different rice varieties were inferred by analyzing the normalized difference vegetation index growth rate. Overall, the single-plant rice contour recognition model showed good contour recognition ability for different rice varieties, with most of the prediction accuracies in the range of 0.75–0.93. The accuracy of the rice growth stage prediction model in recognizing different rice varieties also showed some variation, with the root mean square error between 0.506 and 3.373 days, the relative root mean square error between 2.555% and 14.660%, the Bias between1.126 and 2.358 days, and the relative Bias between 0.787% and 9.397%; therefore, the growth stage prediction model of rice varieties can be used to effectively improve the prediction accuracy of the growth stage periods of rice.