综合修剪强度和图像视图估算柑橘单株产量

IF 4.5 1区 农林科学 Q1 AGRONOMY
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引用次数: 0

摘要

准确估算单棵柑橘果树的产量对果园的精确管理和生产者的收入至关重要。然而,在树木修剪和图像采集的不同过程中,从树木图像中估算柑橘果实产量仍具有挑战性。本研究采用基于深度学习的检测模型来计算果树图像中的果实数量,并采用机器学习模型来根据果实数量估算单棵果树的产量。在四种修剪强度下(无修剪、0-5%、5-10% 和 10-15% 的新芽修剪)的树木,从三种不同视角(每棵树两张、四张和六张图像)进行成像,以确定产量估算的最佳条件。估算产量时考虑的变量包括果实数量、修剪强度和图像视角。包含 1200 张树木图像的数据集用于训练和测试四种机器学习模型:随机森林、支持向量机、极梯度提升(XGBoost)和广义线性模型。在训练和测试中,XGBoost 模型的误差最小。当分别有两个、四个和六个图像视图和经过剪枝处理的树时,产量估计达到最佳。这些发现可以提高基于图像的柑橘果实单株产量估算的准确性,并揭示修剪和图像视图的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Citrus yield estimation for individual trees integrating pruning intensity and image views

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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来源期刊
European Journal of Agronomy
European Journal of Agronomy 农林科学-农艺学
CiteScore
8.30
自引率
7.70%
发文量
187
审稿时长
4.5 months
期刊介绍: The European Journal of Agronomy, the official journal of the European Society for Agronomy, publishes original research papers reporting experimental and theoretical contributions to field-based agronomy and crop science. The journal will consider research at the field level for agricultural, horticultural and tree crops, that uses comprehensive and explanatory approaches. The EJA covers the following topics: crop physiology crop production and management including irrigation, fertilization and soil management agroclimatology and modelling plant-soil relationships crop quality and post-harvest physiology farming and cropping systems agroecosystems and the environment crop-weed interactions and management organic farming horticultural crops papers from the European Society for Agronomy bi-annual meetings In determining the suitability of submitted articles for publication, particular scrutiny is placed on the degree of novelty and significance of the research and the extent to which it adds to existing knowledge in agronomy.
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