During cultivation of agricultural crops, it is important to accurately evaluate crop growth and perform the appropriate work at the right time. However, because a system for inexpensively and accurately determining crop growth automatically over time has not been developed, it is generally difficult for farmers to decide when to perform agricultural work. Therefore, we developed a system that can inexpensively and accurately evaluate the growth of crops over time. First, we developed a high-definition image monitoring device and periodically took images of an apple tree from a fixed point for 2 years from 2016 to 2017. Then, to obtain apple growth information, the radii of the apples in the images were measured manually, and the average radius was standardized from data for both years. We assumed that metrics for evaluating apple growth could be calculated from high-definition field monitoring images of the apple tree. Then, we introduced the green–blue vegetation index (GBVI), which can be easily calculated from images taken with a visible light camera installed near an apple tree, and calculated the GBVI leaf area. Then, we analyzed the metrics that can evaluate apple growth. Specifically, logistic curve approximations were performed for both years with a standardized average apple radius as the dependent variable, and the effective cumulative temperature, cumulative sunshine duration, cumulative precipitation, cumulative maximum GBVI leaf area, cumulative average GBVI leaf area, and elapsed time were taken as independent variables. The obtained logistic curves were defined as growth curves. The results showed that when the cumulative maximum GBVI leaf area was used as an independent variable, the growth curves were almost the same in both years. Moreover, as a result of estimating the standardized average apple radius using each growth curve, the estimation error for the cumulative maximum GBVI leaf area used as an independent variable was the smallest among all the independent variables. These results suggested that the cumulative maximum GBVI leaf area calculated from images was the most useful metric for accurately evaluating apple growth. In addition, it was suggested that the harvestable apple radius could be predicted using the growth curve with the cumulative maximum GBVI leaf area as the independent variable. By incorporating the methods for calculating these metrics into the high-definition image monitoring device, we can develop an automatic apple growth evaluation system based on high-definition field monitoring images.