The decline in the number of farmers and the consequent loss of agricultural expertise poses a major challenge in passing on important skills to the next generation. The art of cherry mirror-packing, which requires many years of training and dexterity, is a typical example of this challenge. In this study, we research and develop a skill acquisition system to support the transmission and acquisition of the mirror-packing technique. Two expert and one novice packers from Yamagata Prefecture, one of Japan's major cherry-producing regions, were filmed and the 3D hand landmark coordinates of their hands were tracked using Mediapipe. Angular changes between the finger joints were calculated as a time series while the cherries were being packed. To handle time series of various lengths, kNN classification models were trained using DTW similarity distance, and separate models were created for the left and right hands. Based on the test results, classification achieved an accuracy of 0.86 for the left hand and 0.78 for the right hand. These results highlight the feasibility of automatic expertise evaluation for skill transfer and acquisition systems.