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A Framework for Single-Panicle Litchi Flower Counting by Regression with Multitask Learning.

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This study introduces FlowerNet, a deep learning model for accurately counting litchi flowers in panicles. This automated method improves upon manual counting and density map approaches for litchi flowering estimation.

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Deep Learning

Background:

  • Accurate litchi flower quantification is crucial for assessing tree growth and conducting phenotypic studies.
  • Manual flower counting is labor-intensive and prone to errors, while existing automated methods struggle with dense flower clusters and background interference.
  • There is a need for advanced deep learning techniques to reliably estimate litchi flower numbers, especially at the individual panicle level.

Purpose of the Study:

  • To develop and evaluate a novel deep learning framework, FlowerNet, for accurate automated counting of small, dense male litchi flowers within panicles.
  • To address the limitations of current density map-based methods by minimizing background interference.
  • To provide a robust tool for litchi flowering quantity estimation and support orchard management.

Main Methods:

  • A two-stage framework was employed: YOLACT++ for individual litchi panicle segmentation, followed by FlowerNet for flower counting within each segmented panicle.
  • FlowerNet utilizes a multitask learning approach for density map regression, effectively integrating foreground and background information to improve pixel-level accuracy.
  • A regression equation was established using inflorescence data to validate FlowerNet's performance against manual counts.

Main Results:

  • FlowerNet achieved a mean absolute error (MAE) of 47.71 and a root mean squared error (RMSE) of 61.78 on the constructed flower dataset.
  • The established regression equation demonstrated a strong correlation between FlowerNet's predicted flower counts and manual counts, with a determination coefficient (R²) of 0.81.
  • The proposed method effectively overcomes background interference, offering improved accuracy for dense litchi flower quantification.

Conclusions:

  • The developed FlowerNet algorithm provides a promising solution for the automated estimation of litchi flowering quantity.
  • This deep learning approach offers a valuable and reliable reference for litchi orchard management, particularly during the critical flowering period.
  • The framework enhances precision in phenotypic studies by enabling accurate quantification of individual flowers on litchi panicles.