A shadow-based method to calculate the percentage of filled rice grains

被引:29
作者
Liu, Tao [1 ]
Wu, Wei [1 ]
Chen, Wen [1 ]
Sun, Chengming [1 ]
Chen, Chen [1 ]
Wang, Rui [1 ]
Zhu, Xinkai [1 ]
Guo, Wenshan [1 ]
机构
[1] Yangzhou Univ, Coinnovat Ctr Modern Prod Technol Grain Crops, Jiangsu Key Lab Crop Genet & Physiol, Yangzhou 225009, Jiangsu, Peoples R China
关键词
Rice; Percent filled grains; Shadow features; Digital image; Computer vision; ALGORITHM; KERNELS; IMAGES;
D O I
10.1016/j.biosystemseng.2016.07.011
中图分类号
S2 [农业工程];
学科分类号
0828 ;
摘要
Rice grain filling is a critical factor that determines the grain yield. It is important to measure the percentage of filled grains (PFR) in rice production management and scientific research. Current methods to measure filled grain percentage are generally manual, which are all time-consuming and labour-intensive with subjective results. Here, we designed an image analysis-based method to measure the percentage of filled grains using four light sources to generate grains shadows from four different directions. The differences of grain shadows between filled and unfilled grains were found out. The ratio of shadow characteristics to grain characteristics distinguished filled and unfilled grains. The conveyor belt with a vibrating feeder and controlled variable-speed was used to measure batched grains. The maximum measuring speed of the conveyor belt was about 60-100 grains/s, and the proper measuring speed was about 40-50 grains/s. Support vector machine (SVM) identified the unfilled grains, and the percentage of the unfilled grains was calculated for 8 Indica and 8 Japonica rice cultivars. The average false positive rate for Indica rice was 3.85%, and the average false negative rate was 5.44%. The average false positive rate for Japonica rice was 5.11%, and the average false negative rate was 3.54%. All these results indicate that this method is reliable and can be used for fast and intelligent measurement of filled grain percentage. The method shows great potential in improving the efficiency of grains' trait evaluation in crop breeding and cultivation research. (C) 2016 IAgrE. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码:79 / 88
页数:10
相关论文
共 18 条
[1]   A simple method for fitting of bounding rectangle to closed regions [J].
Chaudhuri, D. ;
Samal, A. .
PATTERN RECOGNITION, 2007, 40 (07) :1981-1989
[2]  
Chen F. N., 2011, Proceedings 2011 International Conference on Computer Distributed Control and Intelligent Environmental Monitoring (CDCIEM 2011), P36, DOI 10.1109/CDCIEM.2011.566
[3]   Classification of broadleaf weed images using Gabor wavelets and Lie group structure of region covariance on Riemannian manifolds [J].
Chen, Yongming ;
Lin, Ping ;
He, Yong ;
Xu, Zhenghao .
BIOSYSTEMS ENGINEERING, 2011, 109 (03) :220-227
[4]   Fast discrimination and counting of filled/unfilled rice spikelets based on bi-modal imaging [J].
Duan, Lingfeng ;
Yang, Wanneng ;
Bi, Kun ;
Chen, Shangbin ;
Luo, Qingming ;
Liu, Qian .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2011, 75 (01) :196-203
[5]  
Fang JianJun Fang JianJun, 2006, Nongye Jixie Xuebao = Transactions of the Chinese Society of Agricultural Machinery, V37, P87
[6]  
Gonzalez R. C., 2009, MORPHOLOGICAL OPERAT
[7]   A novel matching algorithm for splitting touching rice kernels based on contour curvature analysis [J].
Lin, P. ;
Chen, Y. M. ;
He, Y. ;
Hu, G. W. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2014, 109 :124-133
[8]   Wheat class identification using monochrome images [J].
Manickavasagan, A. ;
Sathya, G. ;
Jayas, D. S. ;
White, N. D. G. .
JOURNAL OF CEREAL SCIENCE, 2008, 47 (03) :518-527
[9]   Evaluation of variations in the shape of grain types using principal components analysis of the elliptic Fourier descriptors [J].
Mebatsion, H. K. ;
Paliwal, J. ;
Jayas, D. S. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2012, 80 :63-70
[10]   A Fourier analysis based algorithm to separate touching kernels in digital images [J].
Mebatsion, H. K. ;
Paliwal, J. .
BIOSYSTEMS ENGINEERING, 2011, 108 (01) :66-74