Comparison of Grape Flower Counting Using Patch-Based Instance Segmentation and Density-Based Estimation with Convolutional Neural Networks

EasyChair Preprint no. 6539

Abstract

Information on flower number per grapevine inflorescence is critical for grapevine genetic improvement, early yield estimation and vineyard management. Previous approaches to automize this process by traditional image processing techniques such as color and morphology analysis, have failed in the improvement of a universal system that can be applied to multiple grapevine cultivars during different growth stages under various illumination conditions. Deep neural networks present numerous opportunities for image-based plant phenotyping. In this study, we evaluated three deep learning-based approaches for automatic counting of flower numbers on inflorescence images, built on instance segmentation using Mask R-CNN, object density-map estimation using U-Net and patch-based instance segmentation using Mask R-CNN, respectively. The results were analyzed on a publicly available grapevine inflorescence dataset of 204 images of four different cultivars during various growth stages, providing a high diversity for inflorescence morphology. The algorithm, based on patch-based instance segmentation using Mask R-CNN, produced counting results highly correlated to manual counts (R2 = 0.96). Practically constant MAPE values among different cultivars (from 5.50% to 8.45%), implying a high robustness in this method. Achieving the fastest counting (0.33 sec. per image of size 512 × 512) with slightly lower counting accuracy (R2 = 0.91), the method based on object density-map turned out to be suitable for real-time flower counting systems.

Keyphrases: Computer vision-based phenotyping, Convolutional Neural Network (CNN), flower counting, grape yield estimation, instance segmentation, object density map

@Booklet{EasyChair:6539,
  author = {Umme Fawzia Rahim and Tomoyoshi Utsumi and Hiroshi Mineno},
  title = {Comparison of Grape Flower Counting Using Patch-Based Instance Segmentation and Density-Based Estimation with Convolutional Neural Networks},
  howpublished = {EasyChair Preprint no. 6539},

  year = {EasyChair, 2021}}