| 张小花 1
，马瑞峻 2，吴卓葵 1
，黄泽鸿 1
，王嘉辉 1.基于机器视觉的果园成熟柑橘快速识别及产量预估研究[J].广东农业科学,2019,46(7):156-161 |
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基于机器视觉的果园成熟柑橘快速识别及产量预估研究 |
| Fast Detection and Yield Estimation of RipeCitrus Fruit Based on Machine Vision |
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| DOI：10.16768/j.issn.1004-874X.2019.07.022 |
| 中文关键词: 机器视觉 水果识别 产量预估 图像处理 MATLAB |
| 英文关键词: machine vision fruit detection yield estimation image processing MATLAB |
| 基金项目:广东省科技计划项目（2017A020208068）；广州市科技计划项目（201704030131）；广东省自然科学基金（2017A030310650） |
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| 摘要点击次数: 1723 |
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| 中文摘要: |
| 【目的】提供一种快速、准确的自然环境下成熟柑橘的识别及计数方法，解决传统的通过人工
采样的方法进行产量预估带来的成本高、时间长和精度低的不足，并为以后对柑橘进行自动采摘打下基础。
【方法】应用 RGB 相机采集柑橘园果树图像，并通过转换到 Lab 颜色空间，对与背景颜色有明显区别的柑
橘区分采用“a”分量，然后基于霍夫圆变换法应用 MATLAB 软件对剔除背景的柑橘进行计数，实现对柑橘
产量的预估。【结果】该图像处理方法与传统的水果与背景分离方法相比更简单快速，果实识别正确率达
94.01%，产量预估正确率达 96.58%，平均识别时间 1.03 s。选取 10 棵树共 20 个图片进行产量预估，将该算
法得到的柑橘数量与通过人眼计数得到的结果进行比较，其相关系数 R2 为 0.9879。【结论】该算法简单快速，
能精确实现水果的快速自动识别及产量预估，对果实的重叠性、果实遮挡有较好的鲁棒性，促进了机器学习
在现代农业的应用，具有较高的理论和实践意义，推动了果园智慧农业进一步发展。 |
| 英文摘要: |
| 【Objective】 The purpose was to provide a fast and accurate method for the identification and counting
of mature citrus in natural environment, to solve the shortage of high cost, long time and low precision caused by manual
sampling method, and to lay a foundation for automatic picking of citrus in the future. 【Method】 The RGB camera was
used to collect the image of the citrus fruit tree, and the image was converted to Lab color space. The“a”component was
used for the citrus distinguishing from the background color, and then the MATLAB software was used to count the citrus
based on Hough circle transformation method to achieve an estimate of the citrus yield. 【Result】 The image processing
method is simpler and faster than the traditional method of fruit and background separation. The recognition accuracy
rate is 94.01%. Yield estimation accuracy is 96.58%, and the average recognition time is 1.03 seconds. The algorithm
was tested on 20 images(10 trees), and the number of fruits counted by this algorithm was compared with that counted by human observation. The coefficient of determination (R2) is 0.83. 【Conclusion】 The method can realize rapid and automatic
identification and counting of fruits and has good robustness to fruit overlap and fruit occlusion. This research promotes the
application of machine learning in modern agriculture, has a high theoretical and practical significance, and facilitates the
further development of orchard smart agriculture. |
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