Vision-Based Apple Counting and Yield Estimation

被引：10

作者：

Roy, Pravakar ^{[1
]}

Isler, Volkan ^{[1
]}

机构：

[1] Univ Minnesota, Dept Comp Sci, Minneapolis, MN 55455 USA

来源：

2016 INTERNATIONAL SYMPOSIUM ON EXPERIMENTAL ROBOTICS | 2017年 / 1卷

基金：

美国国家科学基金会;

关键词：

D O I：

10.1007/978-3-319-50115-4_42

中图分类号：

TP24 [机器人技术];

学科分类号：

080202 ; 1405 ;

摘要：

We present a novel method for yield estimation in apple orchards. Our method takes segmented and registered images of apple clusters as input. It outputs number and location of individual apples in each cluster. Our primary technical contributions are a representation based on a mixture of Gaussians, and a novel selection criterion to choose the number of components in the mixture. The method is experimentally verified on four different datasets using images acquired by a vision platform mounted on an aerial robot, a ground vehicle and a hand-held device. The accuracy of the counting algorithm itself is 91%. It achieves 81-85% accuracy coupled with segmentation and registration which is significantly higher than existing image based methods.

引用

页码：478 / 487

页数：10

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