Watermelon (Citrulluslanatus) is an economically important fruit crop worldwide. Gummy stem blight (GSB) is one of the most damaging diseases encountered during watermelon cultivation. In the present study, we identified quantitative trait loci (QTLs) associated with GSB resistance in an F₂ population derived from a cross between maternal-susceptible line ‘920533’ (C. lanatus) and the paternal-resistant line ‘PI 189225’ (C. amarus). The resistance of 178 F₂ plants was assessed by two different evaluation methods, including leaf lesion (LL) and stem blight (SB). To analyze the QTLs associated with GSB resistance, a linkage map was constructed covering a total genetic distance of 1070.2 cM. QTL analysis detected three QTLs associated with GSB resistance on chromosome 8 and 6. Among them, two QTLs, qLL8.1 and qSB8.1 on chromosome 8 identified as major QTLs, explaining 10.5 and 10.0% of the phenotypic variations localizing at same area and sharing the same top markers for both LL and SB traits, respectively. A minor QTL, qSB6.1, explains 9.7% of phenotypic variations detected on chromosome 6 only for the SB trait. High-throughput markers were developed and validated for the selection of resistant QTLs using watermelon accessions, and commercial cultivars. Four potential candidate genes were predicted associated with GSB resistance based on the physical location of flanking markers on chromosome 8. These findings will be helpful for the development of watermelon cultivars resistant to GSB.

As global warming increases day/night temperatures as well as frequencies of heat waves, studying physiological responses in long-term heat stress is required for sustainable yield production in the future. In this study, effects of long-term heat stress on photosynthetic, morphological, and yield parameters of three cherry tomato accessions, HR17, HR22, and HR24, were evaluated. The experiment was conducted under two temperature greenhouse conditions, where temperature set-point for ventilation was 30 °C and 35 °C during the day for 57 days, respectively. Plants were harvested on the 35th days and 57th days after heat treatments, and their physiological and morphological characteristics and yield traits were measured. Under control conditions, HR17 and HR22 had 0.5–0.6 harvest index, while HR24 had 0.3 harvest index. On 35th days after heat treatment, although yield loss percentage of HR17 was high (43%), it produced the highest fruit yield among all three accessions. However, after longer heat treatment, HR24 produced the highest fruit yields among all accessions with the smallest yield loss (34%). Furthermore, yield loss was highly associated with reductions in nitrogen use efficiency and water content in plant body under heat stress. The results of this study will provide breeders with a new insight into selecting heat-tolerant genotypes in cherry tomatoes.

Watermelon grafting is practiced in order to improve tolerance to poor environments such as low temperature. This study was conducted to investigate the effect of low-temperature tolerant bottle gourd (Lagenaria siceraria) rootstocks on the growth and fruit quality of watermelon in semi-forcing and retarding culture where plants were exposed to low or high temperature. Five bottle gourd accessions (FR79, IT207112, BG702, BG703, and FRD22) with low temperature tolerance were evaluated as rootstock for the watermelon scion ‘Sambokkul’. Non-grafted watermelon and watermelon grafted onto commercial rootstock ‘Shintozwa’ (Cucurbita maxima D. × C. moschata D.) or ‘Bullojangsaeng’ (L. siceraria) were used as controls. Watermelons were cultivated in spring (April to June, semi-forcing culture) and autumn (August to October, retarding culture). The responses to low-temperature, growth, yield, and fruit quality differed depending on the rootstocks and growing season. In semi-forcing culture, the monthly averages of daily and minimum temperature in April were, respectively, 13.4 and 1.5 °C. Although the low temperature of the early growth stage suppressed the initial growth of watermelons, grafting mitigated the low-temperature stress. The fruit quality of non-grafted watermelons was greater, but the fruit mass was the lowest (4.8 kg). Grafting onto ‘Shintozwa’ increased the fruit weight (7.0 kg) but reduced the fruit quality. Grafting onto bottle gourd rootstocks had high affinity, good root growth, tolerance to low temperature, and little effect on fruit quality. In retarding culture, the temperature conditions in early and late growth were very high and low, respectively. The growth and fruit quality of grafted watermelons were not superior to those of non-grafted watermelons. Accordingly, these results suggest that watermelon grafting onto the bottle gourd rootstocks may increase the low-temperature tolerance, especially in the early growth stage, and the marketable yield without a reduction in fruit quality. The most promising accession for this purpose was found to be ‘FR79’.

There is a growing need to establish a breed reassessment system responding to tomato spotted wilt virus (TSWV) mutations. Conventional visual survey methods allow for assessing TSWV severity and disease incidence, while enzyme-linked Immunosorbent Assay (ELISA) data analysis can replace and validate visual surveys. This study proposes a non-destructive evaluation technique for TSWV using an open software platform based on image processing and machine learning. Many studies have evaluated resistance to the TSWV. However, as strains that destroy TSWV resistance emerge, an evaluation technique that can identify new genetic resources with resistance to the variants is needed. Evaluation techniques based on images and machine learning have the strength to respond quickly and accurately to the emergence of new variants. However, studies on resistance to viruses rely on empirical judgment based on visual surveys. The accuracy of the training model using Support Vector Machine (SVM), Logistic Regression (LR), and neural networks (NNs) was excellent, in the following order: NNs (0.86), LR (0.81), SVM (0.65). Meanwhile, the accuracy of the validation model was good, in the following order NN (0.84), LR (0.79), SVM (0.71). NNs’ prediction performance was verified through ELISA data analysis, showing a causal relationship between the two data sets with an R² of 0.86 with statistical significance. Imaging and NN-based TSWV resistance assessment technologies show significant potential as key tools in genetic resource reassessment systems that ensure a rapid and accurate response to the emergence of new TSWV strains.