2025 Volume 94 Issue 1 Pages 64-72
Pollinator insects are required to pollinate flowers in the production of some fruits and vegetables, and strawberries fall into this category. However, the function of pollinators has not been clarified by quantitative metrics such as the duration of pollinator visits needed by flowers. Due to the long activity time of pollinators (approximately 10-h), it is not easy to observe the visitation characteristics manually. Therefore, we developed software for evaluating pollinator performance using two types of artificial intelligence (AI), YOLOv4, which is an object detection AI, and VGG16, which is an image classifier AI. In this study, we used Phaenicia sericata Meigen (green blow fly) as the strawberry pollinator. The software program can automatically estimate the visit duration of a fly on a flower from video clips. First, the position of the flower is identified using YOLO, and the identified location is cropped. Next, the cropped image is classified by VGG16 to determine if the fly is on the flower. Finally, the results are saved in CSV and HTML format. The program processed 10 h of video (collected from 07:00 h to 17:00 h) taken under actual growing conditions to estimate the visit durations of flies on flowers. The recognition accuracy was approximately 97%, with an average difference of 550 s. The software was run on a small computer board (the Jetson Nano), indicating that it can easily be used without a complicated AI configuration. This means that the software can be used immediately by distributing pre-configured disk images. When the software was run on the Jetson Nano, it took approximately 11 min to estimate one day of 2-h video. It is therefore clear that the visit duration of a fly on a flower can be estimated much faster than by manually checking videos. Furthermore, this system can estimate the visit durations of pollinators to other flowers by changing the YOLO and VGG16 model files.
