Predicting the quality attributes related to geographical growing regions in red-fleshed kiwifruit by data fusion of electronic nose and computer vision systems.

The ability of a data fusion system composed of a computer vision system (CVS) and an electronic nose (e-nose) was evaluated to predict key physiochemical attributes and distinguish red-fleshed kiwifruit produced in three distinct regions in northern Iran. Color and morphological features from whole and middle-cut kiwifruits, along with the maximum responses of the 13 metal oxide semiconductor (MOS) sensors of an e-nose system, were used as inputs to the data fusion system. Principal component analysis (PCA) revealed that the first two principal components (PCs) extracted from the e-nose features could effectively differentiate kiwifruit samples from different regions. The PCA-SVM algorithm achieved a 93.33% classification rate for kiwifruits from three regions based on data from individual e-nose and CVS. Data fusion increased the classification rate of the SVM model to 100% and improved the performance of Support Vector Regression (SVR) for predicting physiochemical indices of kiwifruits compared to individual systems. The data fusion-based PCA-SVR models achieved validation R2 values ranging from 90.17% for the Brix-Acid Ratio (BAR) to 98.57% for pH prediction. These results demonstrate the high potential of fusing artificial visual and olfactory systems for quality monitoring and identifying the geographical growing regions of kiwifruits.

Four genetic loci control compact plant size with yellow pear-shaped fruit in ornamental tomato (Solanum lycopersicum L.).

Tomato is an attractive fruiting vegetable crop that can be used as an ornamental plant. Agronomical traits have been subjected to extensive genetic dissection to enhance vegetable breeding programs. By contrast, there are few genetic studies of ornamental traits for the development of ornamental tomato varieties. To investigate genetic loci linked to desired ornamental traits, we performed genetic analyses using an intraspecific mapping population that segregated for fruit color (yellow or red), fruit shape (round or pear), and plant height (high or compact). A genetic map was constructed with 965 single nucleotide polymorphisms (SNPs) and 33 simple sequence repeat markers. Subsequent linkage analysis using quantitative locus analysis and genome-wide association study detected four genetic loci for the three selected traits, all of which were located near the reported genes. We performed KASP-kompetitive allele-specific PCR-to develop SNP markers that were tightly linked to the four loci. Highly accurate genotyping data were obtained from the four SNPs across 187 F2 plants, which enabled us to select two lines with homozygous alleles for compact plant size and yellow pear-shaped fruits. These newly developed SNP markers and genetic strategies could be used to accelerate breeding programs for ornamental tomato plants.