| （英） |
Pear pollination is generally done by artificial pollination, which requires the collection of pollen. Pollen collection is hard work, requiring long hours at high altitudes, and in recent years, much of the pollen is imported. However, if a disease occurs in a pollen exporting country, imports are halted, resulting in a shortage of fruiting material. To solve this problem, it is necessary to mechanize the pollen collection process and strengthen the domestic supply and demand system. In this study, we propose an AI (Artificial Intelligence)-based method for estimating pear pollen quantity. Specifically, we use YOLO (You Only Look Once), a deep learning-based object detection algorithm, to detect pear blossoms on photographed branches by classifying them into five stages, from bud to blossom. The number of flowers in each stage of flowering detected and the average amount of pollen per flower are used to calculate the amount of pollen per branch. In this paper, we report on the evaluation of the estimation accuracy of the developed AI pollen amount estimation. |