Application of consumer RGB-D cameras for fruit detection and localization in field: A critical review

被引:228
作者
Fu, Longsheng [1 ,3 ,4 ,5 ]
Gao, Fangfang [1 ]
Wu, Jingzhu [2 ]
Li, Rui [1 ]
Karkee, Manoj [5 ]
Zhang, Qin [5 ]
机构
[1] Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China
[2] Beijing Technol & Business Univ, Beijing Key Lab Big Data Technol Food Safety, Beijing 100048, Peoples R China
[3] Minist Agr & Rural Affairs, Key Lab Agr Internet Things, Yangling 712100, Shaanxi, Peoples R China
[4] Shaanxi Key Lab Agr Informat Percept & Intelligen, Yangling 712100, Shaanxi, Peoples R China
[5] Washington State Univ, Ctr Precis & Automated Agr Syst, Prosser, WA 99350 USA
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Structured light; Time of flight; Active infrared stereo; Infrared image; Depth image; VISION-BASED CONTROL; FASTER R-CNN; APPLE DETECTION; STRUCTURED LIGHT; CITRUS DETECTION; DEPTH FEATURES; KINECT V2; D SENSORS; SYSTEM; COLOR;
D O I
10.1016/j.compag.2020.105687
中图分类号
S [农业科学];
学科分类号
09 ;
摘要
Fruit detection and localization are essential for future agronomic management of fruit crops such as yield prediction, yield mapping and automated harvesting. However, to perform robust and efficient fruit detection and localization in orchard is a challenging task under variable illumination, low-resolutions and heavy occlusion by neighboring fruits, foliage, or branches. Therefore, researches of fruit detection and localization by getting more information of objects are essential. RGB-D (Red, Green, Blue-Depth) cameras are promising sensors and widely used in fruit detection and localization given that they provide depth information and infrared information in addition to RGB information. After presenting a discussion on the advantages and disadvantages of RGB-D cameras with different depth measurement principles and application fields, this paper reviews various types of RGB-D sensor systems and image processing methods used for fruit detection and localization in the field. Finally, major challenges for the successful application of RGB-D camera-based machine vision system, and potential future directions for the research and development in this area are discussed.
引用
收藏
页数:12
相关论文
共 96 条
[1]   Using depth cameras to extract structural parameters to assess the growth state and yield of cauliflower crops [J].
Andujar, Dionisio ;
Ribeiro, Angela ;
Fernandez-Quintanilla, Cesar ;
Dorado, Jose .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2016, 122 :67-73
[2]  
[Anonymous], 2017, T CHINESE SOC AGR MA, DOI DOI 10.6041/J.ISSN.1000-1298.2017.10.004.
[3]   Development of a sweet pepper harvesting robot [J].
Arad, Boaz ;
Balendonck, Jos ;
Barth, Ruud ;
Ben-Shahar, Ohad ;
Edan, Yael ;
Hellstrom, Thomas ;
Hemming, Jochen ;
Kurtser, Polina ;
Ringdahl, Ola ;
Tielen, Toon ;
van Tuijl, Bart .
JOURNAL OF FIELD ROBOTICS, 2020, 37 (06) :1027-1039
[4]  
Arefi A., 2013, Australian Journal of Crop Science, V7, P699
[5]   Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards [J].
Bargoti, Suchet ;
Underwood, James P. .
JOURNAL OF FIELD ROBOTICS, 2017, 34 (06) :1039-1060
[6]   Colour-agnostic shape-based 3D fruit detection for crop harvesting robots [J].
Barnea, Ehud ;
Mairon, Rotem ;
Ben-Shahar, Ohad .
BIOSYSTEMS ENGINEERING, 2016, 146 :57-70
[7]   COLOR-ENCODED STRUCTURED LIGHT FOR RAPID ACTIVE RANGING [J].
BOYER, KL ;
KAK, AC .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1987, 9 (01) :14-28
[8]   Range data acquisition using color structured lighting and stereo vision [J].
Chen, CS ;
Hung, YP ;
Chiang, CC ;
Wu, JL .
IMAGE AND VISION COMPUTING, 1997, 15 (06) :445-456
[9]  
Choi D, 2015, T ASABE, V58, P933
[10]  
Dahikar SS., 2014, Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng, V2, P683, DOI DOI 10.1016/J.INDCROP.2018.09.055.