Introduction: Flesh color is an important trait in watermelon (Citrullus lanatus L.). Several flesh color genes have been identified in watermelon; however, the inheritance of and the molecular basis underlying the white flesh trait remain largely unknown.Methods: In this study, segregation populations were constructed by crossing the canary yellow flesh line HSH-F with the white flesh line Sanbai to fine-map the white flesh gene in watermelon.Results: Genetic analysis indicated that the white flesh trait is controlled by a single recessive locus, termed Clwf2. Map-based cloning delimited the Clwf2 locus to a 132.3-kb region on chromosome 6. The candidate region contains 13 putative genes, and four of them—Cla97C06G121860, Cla97C06G121880, Cla97C06G121890, and Cla97C06G121900—were significantly downregulated in the white flesh compared to the canary yellow flesh watermelon fruits. The Cla97C06G121890 gene, which encodes a tetratricopeptide repeat protein, showed almost no expression in the white flesh fruit before maturity, whereas it had a very high expression in the canary yellow flesh fruit at 18 days after pollination. Transmission electron microscopy revealed rounded and regularly shaped chromoplasts in both the canary yellow and white flesh fruits. Further quantitative real-time PCR analysis showed that the expression levels of several key plastid division genes and almost the entire carotenoid biosynthesis pathway genes were downregulated in the white flesh compared to the canary yellow flesh fruits.Discussion: This study suggests that the proliferation inhibition of chromoplasts and downregulation of the CBP genes block the accumulation of carotenoids in watermelon and lead to white flesh. These findings advance and extend the understanding of the molecular mechanisms underlying white flesh trait formation and carotenoid biosynthesis in watermelon.

The color of the rind is one of the most crucial agronomic characteristics of watermelon (Citrullus lanatus L.). Its genetic analysis was conducted to provide the identification of genes regulating rind color and improving the quality of watermelon appearance. In this study, a mapping population of 505 F₂ plants, derived from a cross between green (CG058) and light-green (CG265) rinds, along with a high-density genetic linkage (average 0.9 cM distance between bin markers), was used to map and identify possible candidate genes. The green rind trait was determined to be regulated by a single Mendelian locus and was precisely located within a 110 kb genomic site on chromosome nine (Chr 9). In the respective region, two potential genes, Cla97C09G175170 and Cla97C09G175180, were substantially downregulated in the light-green rind in comparison to the green rind. Previous studies revealed that Cla97C09G175170, encoding a two-component response regulator-like protein (APRR2), is possibly involved in the green rind trait in watermelon. Virus-induced gene silencing (VIGS) assay confirmed that ClAPRR2 is a key gene responsible for green rind color. Moreover, qRT-PCR analysis revealed that the transcription levels of multiple key genes in the chlorophyll (Chl) biosynthesis pathway were downregulated in the light-green rind relative to the green rind. The current findings have the potential to clarify the regulatory mechanisms that underlie the color of the watermelon rind. These data would provide valuable insights for the targeted molecular design and development of watermelon rinds.

Water is a crucial element for the growth of watermelon plants, making rapid and non-destructive monitoring of plant water content vital for precision irrigation in watermelon farming. While previous research has demonstrated the sensitivity of short-wave infrared (SWIR) bands to plant water content, their high costs limit widespread application. In contrast, visible and near-infrared (VNIR) spectral instruments offer significant advantages in terms of affordability, compactness, and spectral resolution. However, their potential for predicting the leaf water content (LWC) of watermelon plants has yet to be fully investigated. This study aims to assess the efficacy of hyperspectral reflectance measured with VNIR spectral instruments in estimating the LWC of watermelon plants at various leaf layers. Hyperspectral reflectance data (350−1100 nm) were collected from three leaf layers (upper, middle, and lower) under various drought treatments. Models for estimating LWC were developed using both spectral indices and full wavelength data. The results indicated that the middle leaf layer was the most effective for estimating LWC, and using full wavelength data achieved higher accuracy in LWC estimation. Furthermore, compared to the simple regression model, the AdaBoost-based machine learning model demonstrated superior performance, achieving an R² of 0.9636 in estimating LWC through five-fold cross-validation, which indicates high predictive accuracy. Ensemble learning significantly outperforms traditional methods, providing a substantial improvement in model accuracy. The findings offer important technical assistance for the spectral monitoring of LWC and precision irrigation in watermelon cultivation.