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
来源：
COMPUTERS AND ELECTRONICS IN AGRICULTURE | 2020年 / 177卷
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
[81]   Size estimation of sweet onions using consumer-grade RGB-depth sensor [J].
Wang, Weilin ;
Li, Changying .
JOURNAL OF FOOD ENGINEERING, 2014, 142 :153-162
[82]   On-Tree Mango Fruit Size Estimation Using RGB-D Images [J].
Wang, Zhenglin ;
Walsh, Kerry B. ;
Verma, Brijesh .
SENSORS, 2017, 17 (12)
[83]   Using color and 3D geometry features to segment fruit point cloud and improve fruit recognition accuracy [J].
Wu, Gang ;
Li, Bin ;
Zhu, Qibing ;
Huang, Min ;
Guo, Ya .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 174
[84]  
Xiao K, 2017, COMPUT ELECTRON AGR, V133, P30, DOI [10.1016/j.compag.2016.12.002, 1]
[85]   Development and field evaluation of a strawberry harvesting robot with a cable-driven gripper [J].
Xiong, Ya ;
Peng, Cheng ;
Grimstad, Lars ;
From, Pal Johan ;
Isler, Volkan .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2019, 157 :392-402
[86]  
Yamamoto Satoshi, 2015, Environmental Control in Biology, V53, P49, DOI 10.2525/ecb.53.49
[87]  
Yang C H., 2019, Trans. Chinese Soc. Agricult. Mach., V50, P72
[88]  
Zanuttigh P., 2016, TECHNOL APP, P81, DOI [10.1007/978-3-319-30973-6., DOI 10.1007/978-3-319-30973-6]
[89]   Multi-class object detection using faster R-CNN and estimation of shaking locations for automated shake-and-catch apple harvesting [J].
Zhang, Jing ;
Karkee, Manoj ;
Zhang, Qin ;
Zhang, Xin ;
Yaqoob, Majeed ;
Fu, Longsheng ;
Wang, Shumao .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2020, 173
[90]   Branch detection for apple trees trained in fruiting wall architecture using depth features and Regions-Convolutional Neural Network (R-CNN) [J].
Zhang, Jing ;
He, Long ;
Karkee, Manoj ;
Zhang, Qin ;
Zhang, Xin ;
Gao, Zongmei .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2018, 155 :386-393
12345678910