Shape is one of the most important traits of agricultural products due to its relationships with the quality, quantity, and value of the products. For strawberries, the nine types of fruit shape were defined and classified by humans based on the sampler patterns of the nine types. In this study, we tested the classification of strawberry shapes by machine learning in order to increase the accuracy of the classification, and we introduce the concept of computerization into this field. Four types of descriptors were extracted from the digital images of strawberries: (1) the Measured Values (MVs) including the length of the contour line, the area, the fruit length and width, and the fruit width/length ratio; (2) the Ellipse Similarity Index (ESI); (3) Elliptic Fourier Descriptors (EFDs), and (4) Chain Code Subtraction (CCS). We used these descriptors for the classification test along with the random forest approach, and eight of the nine shape types were classified with combinations of MVs + CCS + EFDs. CCS is a descriptor that adds human knowledge to the chain codes, and it showed higher robustness in classification than the other descriptors. Our results suggest machine learning's high ability to classify fruit shapes accurately. We will attempt to increase the classification accuracy and apply the machine learning methods to other plant species.
A strawberry Multi-parent Advanced Generation Intercrosses (MAGIC) population, derived from crosses using six strawberry cultivars was successfully developed. The population was composed of 338 individuals; genome conformation was evaluated by expressed sequence tag-derived simple short repeat (EST-SSR) markers. Cluster analysis and principal component analysis (PCA) based on EST-SSR marker polymorphisms revealed that the MAGIC population was a mosaic of the six founder cultivars and covered the genomic regions of the six founders evenly. Fruit quality related traits, including days to flowering (DTF), fruit weight (FW), fruit firmness (FF), fruit color (FC), soluble solid content (SC), and titratable acidity (TA), of the MAGIC population were evaluated over two years. All traits showed normal transgressive segregation beyond the founder cultivars and most traits, except for DTF, distributed normally. FC exhibited the highest correlation coefficient overall and was distributed normally regardless of differences in DTF, FW, FF, SC, and TA. These facts were supported by PCA using fruit quality related values as explanatory variables, suggesting that major genetic factors, which are not influenced by fluctuations in other fruit traits, could control the distribution of FC. This MAGIC population is a promising resource for genome-wide association studies and genomic selection for efficient strawberry breeding.
Fruit shape of cultivated strawberry (
Fragaria
×
ananassa
Duch.) is an important breeding target. To detect genomic regions associated with this trait, its quantitative evaluation is needed. Previously we created a multi-parent advanced-generation inter-cross (MAGIC) strawberry population derived from six founder parents. In this study, we used this population to quantify fruit shape. Elliptic Fourier descriptors (EFDs) were generated from 2 969 two-dimensional binarized fruit images, and principal component (PC) scores were calculated on the basis of the EFD coefficients. PC1–PC3 explained 96% of variation in shape and thus adequately quantified it. In genome-wide association study, the PC scores were used as phenotypes. Genome wide association study using mixed linear models revealed 2 quantitative trait loci (QTLs) for fruit shape. Our results provide a novel and effective method to analyze strawberry fruit morphology; the detected QTLs and presented method can support marker-assisted selection in practical breeding programs to improve fruit shape.
For the purpose of reducing heating costs, a basal stem heating system was developed using a heat duct at the ground parts of the plants in a plastic tunnel on the forcing culture of eggplant. The basal stem temperature under the heating system in the night was markedly (6.3°C) higher than that of the control, and the air temperature in the plastic tunnel was 3°C higher than that of the control even under the non-heating condition. The basal stem heating system accelerated the growth of the lateral shoots and fruit, and the number of harvested fruits and marketable fruit yields under the heating system were higher than those under the control. The marketable fruit yield under the heating system in the plastic house at 10°C was similar to that under the control at 12°C. Setting up the basal stem heating system was not costly (20,000 yen・10a −1 ) since the heating system can be prepared using existing heating machines. It is, therefore, suggested that a basal stem heating system with a plastic tunnel and branch duct is a useful and practical method for reducing the cost of the forcing culture of eggplant.
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