Object recognition algorithm of tomato harvesting robot using non-color coding approach

被引：0

作者：

Zhao Y. ^{[1
]}

Gong L. ^{[1
]}

Zhou B. ^{[1
]}

Huang Y. ^{[1
]}

Niu Q. ^{[2
]}

Liu C. ^{[1
]}

机构：

[1] School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai

[2] School of Agriculture and Biology, Shanghai Jiao Tong University, Shanghai

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2016年 / 47卷 / 07期

关键词：

AdaBoost; Haar-like feature; Harvesting robot; Non-color coding; Object recognition; Tomato;

D O I：

10.6041/j.issn.1000-1298.2016.07.001

中图分类号：

学科分类号：

摘要：

In order to detect the ripe tomato in unstructured environment for robotic harvesting, a tomato recognition algorithm using non-color coding approach was developed. The proposed algorithm was consist of offline training and online recognition. In the process of offline training, a strong classifier was obtained using AdaBoost algorithm with Haar-like features. The Haar-like feature is a kind of non-color coding feature which can be extracted by integral figure calculation. In the online recognition process, the tomato object was detected by using the strong classifier which was obtained in the offline training process. Two couples of comparative tests were conducted to study the influence of the types of Haar-like features and training times on the performance of the proposed algorithm. The results showed that the C-style Haar-like features and 20000 training times were the optimal parameters for the size of training set. The results of online recognition tests indicated that about 93.3% ripe tomatoes existing in the testing samples set were successfully detected. The proposed tomato recognition approach was also successfully applied in the unstructured environment with various disturbances such as occluded, overlapping, and varying illumination, which indicated that the proposed tomato recognition algorithm was self-adaptive and robust. It was available to be applied in the vision recognition system for a harvesting robot. © 2016, Chinese Society of Agricultural Machinery. All right reserved.

引用

页码：1 / 7

页数：6

共 21 条

[1]

Li P.L., Lee S.H., Hsu H.Y., Review on fruit harvesting method for potential use of automatic fruit harvesting systems, Procedia Engineering, 23, pp. 351-366, (2011)

[2]

Parrish E.A., Goksel J.A.K., Pictorial pattern recognition applied to fruit harvesting, Transactions of the ASAE, 20, 5, pp. 822-827, (1977)

[3]

Slaughter D.C., Harrell R.C., Discriminating fruit for robotic harvest using color in natural outdoor scenes, Transactions of the ASAE, 32, 2, pp. 757-763, (1989)

[4]

Arefi A., Motlagh A.M., Development of an expert system based on wavelet transform and artificial neural networks for the ripe tomato harvesting robot, Australian Journal Crop Science, 7, 5, pp. 699-705, (2013)

[5]

Kurtulmus F., Lee W.S., Vardar A., Green citrus detection using 'eigenfruit', color and circular Gabor texture features under natural outdoor conditions, Computers and Electronics in Agriculture, 78, 2, pp. 140-149, (2011)

[6]

Ji W., Zhao D., Cheng F., Et al., Automatic recognition vision system guided for apple harvesting robot, Computers & Electrical Engineering, 38, 5, pp. 1186-1195, (2012)

[7]

Kurtulmus F., Lee W.S., Vardar A., Immature peach detection in colour images acquired in natural illumination conditions using statistic classifiers and neural network, Precision Agriculture, 15, 1, pp. 57-79, (2014)

[8]

Song Y., Glasbey C.A., Horgan W.G., Et al., Automatic fruit recognition and counting from multiple images, Biosystems Engineering, 118, 1, pp. 203-215, (2014)

[9]

Gabriel G., Stanley B., Jose C., Et al., Olive fruits recognition using neural networks, First International Conference on Information Technology and Quantitative Management (ITQM2013), Procedia Computer Science, 17, pp. 412-419, (2013)

[10]

Ma X., Liu G., Zhou W., Et al., Apple recognition based on fuzzy neural network and quantum genetic algorithm, Transactions of the Chinese Society for Agricultural Machinery, 44, 12, pp. 227-232, (2013)

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