基于多特征融合和支持向量机的三维苹果树器官分类及产量估计算法

IF 7.7 Q1 AGRICULTURE, MULTIDISCIPLINARY Information Processing in Agriculture Pub Date : 2022-09-01 DOI:10.1016/j.inpa.2021.04.011
Luzhen Ge, Kunlin Zou, Hang Zhou, Xiaowei Yu, Yuzhi Tan, Chunlong Zhang, Wei Li
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引用次数: 8

摘要

苹果树器官的自动分类对苹果树的自动修剪、苹果果实的自动采摘和果实产量的估计具有重要意义。但是,苹果果实存在叶密、部分遮挡和聚类等问题。这些问题都给苹果的器官分类和产量估算带来了困难。提出了一种基于颜色与形状多特征融合和支持向量机的三维苹果树器官分类与产量估计方法。该方法适用于矮小、密集种植的早熟和晚熟苹果树。首先提取由红绿蓝(RGB)、色相饱和度(HSV)、曲率、快速点特征直方图(FPFH)和旋转图像组成的196维特征向量;然后对基于线性核函数的支持向量机进行训练,将训练好的支持向量机用于苹果树器官分类。然后采用位置加权平滑算法对分类苹果树器官进行平滑处理。然后采用聚类分层聚类算法对苹果单果进行识别,进行产量估算。在相同的训练集和测试集上,实验结果表明,基于线性核函数的支持向量机优于KNN算法和集成算法。该方法产率估计的召回率、精确率和F1分数分别为93.75%、96.15%和94.93%。综上所述,为解决天然苹果园苹果树器官分类和产量估算问题,提出了一种基于多特征融合和支持向量机的新颖方法,并取得了较好的效果。该方法可为果园苹果自动采摘、果树自动修剪、果园信息自动采集与管理提供技术支持。
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Three dimensional apple tree organs classification and yield estimation algorithm based on multi-features fusion and support vector machine

The automatic classification of apple tree organs is of great significance for automatic pruning of apple trees, automatic picking of apple fruits, and estimation of fruit yield. However, there are some problems of dense foliage, partial occlusion and clustering of apple fruits. All of the problems above would contribute to the difficulties of organs classification and yield estimation of the apple trees. In this paper a method based on Color and Shape Multi-features Fusion and Support Vector Machine (SVM) for 3D apple tree organs classification and yield estimation was proposed. The method was designed for dwarf and densely planted apple trees at the early and late maturity stages. 196-dimensional feature vectors composed with Red Green Blue (RGB), Hue Saturation Value (HSV), Curvatures, Fast Point Feature Histogram (FPFH), and Spin Image were extracted firstly. And then the SVM based on linear kernel function was trained, after that the trained SVM was used for apple tree organs classification. Then the position weighted smoothing algorithm was used for classified apple tree organs smoothing. Then the agglomerative hierarchical clustering algorithm was used to recognize single apple fruit for yield estimation. On the same training and test set the experimental results showed that the SVM based on linear kernel function outperformed the KNN algorithm and Ensemble algorithm. The Recall, Precision and F1 score of the proposed method for yield estimation were 93.75%, 96.15% and 94.93% respectively. In summary, to solve the problems of apple tree organs classification and yield estimation in natural apple orchard, a novelty method based on multi-features fusion and SVM was proposed and achieve good performance. Moreover, the proposed method could provide technical support for automatic apple picking, automatic pruning of fruit trees, and automatic information acquisition and management in orchards.

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来源期刊
Information Processing in Agriculture
Information Processing in Agriculture Agricultural and Biological Sciences-Animal Science and Zoology
CiteScore
21.10
自引率
0.00%
发文量
80
期刊介绍: Information Processing in Agriculture (IPA) was established in 2013 and it encourages the development towards a science and technology of information processing in agriculture, through the following aims: • Promote the use of knowledge and methods from the information processing technologies in the agriculture; • Illustrate the experiences and publications of the institutes, universities and government, and also the profitable technologies on agriculture; • Provide opportunities and platform for exchanging knowledge, strategies and experiences among the researchers in information processing worldwide; • Promote and encourage interactions among agriculture Scientists, Meteorologists, Biologists (Pathologists/Entomologists) with IT Professionals and other stakeholders to develop and implement methods, techniques, tools, and issues related to information processing technology in agriculture; • Create and promote expert groups for development of agro-meteorological databases, crop and livestock modelling and applications for development of crop performance based decision support system. Topics of interest include, but are not limited to: • Smart Sensor and Wireless Sensor Network • Remote Sensing • Simulation, Optimization, Modeling and Automatic Control • Decision Support Systems, Intelligent Systems and Artificial Intelligence • Computer Vision and Image Processing • Inspection and Traceability for Food Quality • Precision Agriculture and Intelligent Instrument • The Internet of Things and Cloud Computing • Big Data and Data Mining
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