Amoah Edward I, White Khayri, Patch Harland M, Grozinger Christina M
Intercollege Graduate Degree Program in Ecology, Huck Institutes of the Life Sciences, Penn State University, University Park, Pennsylvania, United States of America.
Department of Entomology, Center for Pollinator Research, Huck Institutes of the Life Sciences, Penn State University, University Park, Pennsylvania, United States of America.
PLoS One. 2025 Sep 12;20(9):e0332165. doi: 10.1371/journal.pone.0332165. eCollection 2025.
Floral area is a major predictor of the attractiveness of a flowering plant for pollinators, yet the measurement of floral area is time-consuming and inconsistent across studies. Here, we developed an AI-powered algorithm, FloralArea, to automate floral area measurement from an image. The FloralArea algorithm has two main components: an object segmentation module and an area estimation module. The object segmentation module extracts the pixels of flowers and the reference object in an image. The area estimation module predicts floral area based on the ratio between flower and reference object pixels. We fine-tuned two YOLOv8 segmentation models for flower and reference object segmentation. The flower segmentation model achieved moderate precision, recall, mAP0.5, and mAP0.5-0.95 of 0.794, 0.68, 0.741, and 0.455 on the test dataset, while the reference object model achieved an impressive performance of 0.907, 0.940, 0.933, and 0.832. We evaluated FloralArea using 75 images of flowering plants. We used ImageJ to calculate the actual floral area for all the images and compared them with the predicted floral area from FloralArea. The predicted floral area correlated well with the measured floral area with a coefficient of determination (R2) of 0.93 and a root mean square error of 20.58 cm2. The FloralArea algorithm reduced the time it takes to calculate floral area from an image by 99.24% compared with traditional methods with image processing tools like ImageJ. By streamlining floral area estimation, the FloralArea algorithm provides a scalable, efficient, consistent, and accessible tool for researchers, particularly to aid in assessing plant attractiveness to different pollinator groups.
花部面积是开花植物对传粉者吸引力的一个主要预测指标，然而花部面积的测量既耗时，而且不同研究之间的结果也不一致。在此，我们开发了一种由人工智能驱动的算法FloralArea，用于从图像中自动测量花部面积。FloralArea算法有两个主要组成部分：一个目标分割模块和一个面积估计模块。目标分割模块提取图像中花朵和参考物体的像素。面积估计模块根据花朵与参考物体像素之间的比例来预测花部面积。我们对两个YOLOv8分割模型进行了微调，用于花朵和参考物体的分割。花朵分割模型在测试数据集上的精度、召回率、mAP0.5和mAP0.5 - 0.95分别达到了0.794、0.68、0.741和0.455，而参考物体模型则取得了令人印象深刻的性能，分别为0.907、0.940、0.933和0.832。我们使用75张开花植物图像对FloralArea进行了评估。我们使用ImageJ计算了所有图像的实际花部面积，并将其与FloralArea预测的花部面积进行比较。预测的花部面积与测量的花部面积具有良好的相关性，决定系数（R2）为0.93，均方根误差为20.58平方厘米。与使用ImageJ等图像处理工具的传统方法相比，FloralArea算法将从图像计算花部面积所需的时间减少了99.24%。通过简化花部面积估计，FloralArea算法为研究人员提供了一种可扩展、高效、一致且易于使用的工具，特别是有助于评估植物对不同传粉者群体的吸引力。
