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

文献类型： 外文期刊

作者： Lin, Jiaquan 1 ; Li, Jun 1 ; Ma, Zhe 1 ; Li, Can 1 ; Huang, Guangwen 1 ; Lu, Huazhong 4 ;

作者机构： 1.South China Agr Univ, Coll Engn, Guangzhou 510642, Peoples R China

2.Guangdong Lab Lingnan Modern Agr, Guangzhou 510642, Peoples R China

3.State Key Lab Agr Equipment Technol, Beijing 100083, Peoples R China

4.Guangdong Acad Agr Sci, Guangzhou 510640, Peoples R China

期刊名称：PLANT PHENOMICS （ 影响因子：6.5； 五年影响因子：7.5 ）

ISSN： 2643-6515

年卷期： 2024 年 6 卷

页码：

收录情况： SCI

摘要： The number of flowers is essential for evaluating the growth status of litchi trees and enables researchers to estimate flowering rates and conduct various phenotypic studies, particularly focusing on the information of individual panicles. However, manual counting remains the primary method for quantifying flowers, and there has been insufficient emphasis on the advancement of reliable deep learning methods for estimation and their integration into research. Furthermore, the current density map -based methods are susceptible to background interference. To tackle the challenges of accurately quantifying small and dense male litchi flowers, a framework counting the flowers in panicles is proposed. Firstly, an existing effective algorithm YOLACT ++ is utilized to segment individual panicles from images. Secondly, a novel algorithm FlowerNet based on density map regression is proposed to accurately count flowers in each panicle. By employing a multitask learning approach, FlowerNet effectively captures both foreground and background information, thereby overcoming interference from non -target areas during pixel -level regression tasks. It achieves a mean absolute error of 47.71 and a root mean squared error of 61.78 on the flower dataset constructed. Additionally, a regression equation is established using a dataset of inflorescences to examine the application of the algorithm for flower counting. It captures the relationship between the predicted number of flowers by FlowerNet and the manually counted number, resulting in a determination coefficient ( R 2 ) of 0.81. The proposed algorithm shows promise for automated estimation of litchi flowering quantity and can serve as a valuable reference for litchi orchard management during flowering period.

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