The use of digital cameras in plant phenotyping studies using RGB sensors has increased. However, the need for standardization has become apparent because of the diverse analytical approaches used by individual researchers. In this study, we optimized the image acquisition conditions for apples, including scaling tool positioning, lighting conditions, and background color selection. In addition, we developed an ImageJ-based automated image acquisition and analysis program. We generated 240 images of four apple cultivars (Hongan, Hongro, Fuji, and Hwangok) and used 12 image indices to analyze the fruit size, width, length, and shape. We measured the accuracy by comparing the results with actual measurements. Significantly high correlation values were observed between fruit width and the major index (R²=0.947-0.993) as well as between fruit length and the height index (R²=0.964-0.984) based on the analysis using R-squared values to assess accuracy. These findings are expected to enhance the efficiency of apple fruit sorting in the future and can be applied to investigate the shapes of other fruits.

Low-temperature damage at the seedling stage is one of the most significant natural obstacles to wheat’s growth. In domestic wheat breeding programs, the selection of cold-tolerant varieties is crucial for the development of superior wheat varieties. Traditionally, the extent of damage caused by freezing wheat is estimated through visual observation. In this study, we compared the RGB image analysis method with conventional visual evaluation and chlorophyll content analysis methods to determine if this method could accurately quantify the cold tolerance discrimination of wheat in the field. First, single-leaf-level RGB image analysis revealed a pattern similar to dead leaf ratio and chlorophyll content in three grades of freezing injury. Next, we compared the significance of plant-level RGB image analysis. The greenness index by RGB image analysis showed a higher correlation with dead leaf ratio by visual evaluation. Finally, 40 wheat varieties were planted in the field and wheat canopy images were collected at the seedling stage after wintering. There was a high correlation between the greenness index and the visual evaluation. However, there was no correlation between dead leaf ratio and visual evaluation or greenness index as determined by RGB image analysis. These findings suggest that using RGB image analysis rather than visual evaluation can be useful in assessing freeze damage in wheat fields.