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Citrus yield estimation for individual trees integrating pruning intensity and image views

文献类型： 外文期刊

作者： Zhu, Yihang 1 ; Liu, Feng 2 ; Zhao, Yiying 1 ; Gu, Qing 1 ; Zhang, Xiaobin 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Inst Digital Agr, Hangzhou 310021, Zhejiang, Peoples R China

2.Zhejiang Acad Agr Sci, Inst Citrus Res, Taizhou 318020, Zhejiang, Peoples R China

关键词： Citrus yield; Image detection; Yield estimation; Individual trees; Pruning

期刊名称：EUROPEAN JOURNAL OF AGRONOMY （ 影响因子：5.5； 五年影响因子：5.9 ）

ISSN： 1161-0301

年卷期： 2024 年 161 卷

页码：

收录情况： SCI

摘要： Accurately estimating the yield of citrus fruit on individual trees is essential for precise orchard management and the income of producers. However, estimating the yield of citrus fruit from images of trees remains challenging among different processes of tree pruning and image acquisition. This study adopted a deep learning based detection model to count fruit in tree images and machine learning models to estimate the yield of individual trees from the fruit count. Trees under four levels of pruning intensity (no pruning, 0-5 %, 5-10 %, and 10-15 % of new sprouts pruned) and imaged from three different views (two, four, and six images per tree) to determine the optimal conditions for yield estimation. The variables considered for yield estimation included fruit count, pruning intensity and image views. Dataset containing 1200 tree images were used to train and test four machine learning models: random forest, support vector machine, extreme gradient boosting (XGBoost), and generalized linear model. The XGBoost model achieved the lowest errors in both training and testing. The optimal yield estimation occurs when there are two, four, and six image views and trees that have been pruned >10 %, 5-10 %, and <= 5 %, respectively. The findings can enhance the accuracy of image based citrus fruit yield estimation for individual trees and reveal the influences of pruning and image views.

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