MA Zhihong，GONG Liang，LIN Ke，MAO Yuhan，WU Wei，LIU Chengliang . Estimation of Panicle Seed Number Based on Panicle Geometric Pattern Recognition[J]. Journal of Shanghai Jiaotong University, 2019 , 53(2) : 239 -246 . DOI: 10.16183/j.cnki.jsjtu.2019.02.016

［1］REBOLLEDO M C, PEA A L, JORGE D, et al. Combining image analysis, genome wide association studies and different field trials to reveal stable gene-tic regions related to panicle architecture and the number of spikelets per panicle in rice［J］. Frontiers in Plant Science, 2016, 7: 1384. ［2］WENG X, WANG L, WANG J, et al. Grain number, plant height, and heading date7 is a central re-gulator of growth, development, and stress response［J］. Plant Physiology, 2014, 164(2): 735. ［3］CROWELL S, FALCAO A X, SHAH A, et al. High-resolution inflorescence phenotyping using a novel image-analysis pipeline, panorama［J］. Plant Physiology, 2014, 165(2): 479-495. ［4］HUANG C L, YANG W N, DUAN L F, et al. Rice panicle length measuring system based on dual-camera imaging［J］. Computers & Electronics in Agriculture, 2013, 98: 158-165. ［5］GUO W, FUKATSU T, NINOMIYA S. Automated characterization of flowering dynamics in rice using field-acquired time-series rgb images［J］. Plant me-thods, 2015, 11(1): 7. ［6］MA Z, MAO Y, GONG L, et al. Smartphone-based visual measurement and portable instrumentation for crop seed phenotyping［J］. Ifac Papersonline, 2016, 49(16): 259-264. ［7］GRANIER C, AGUIRREZABAL L, CHENV K, et al. PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensilivity to water deficit［J］. New Phytologist, 2006, 169(3): 623-635. ［8］ADAGALE S S, PAWAR S S. Image segmentation using PCNN and template matching for blood cell counting［C］//IEEE International Conference on Computational Intelligence and Computing Research. Madurai: IEEE, 2013: 1-5. ［9］李小龙, 马占鸿, 孙振宇, 等.基于图像处理的小麦条锈病菌夏孢子模拟捕捉的自动计数［J］. 农业工程学报, 2013, 29(2): 199-206. LI Xiaolong, MA Zhanhong, SUN Zhenyu, et al. Automatic counting for trapped urediospores of Puccinia striiformis f. sp. tritici based on image processing［J］. Transactions of the Chinese Society of Agricultural Engineering, 2013, 29(2): 199-206. ［10］ALTAM F, ADAM H, ANJOS A D, et al. P-TRAP: A panicle trait phenotyping tool［J］. Bmc Plant Biology, 2013, 13(1): 122. ［11］许杰, 周子力, 潘大宇, 等. 水稻穗部籽粒参数快速无损获取方法研究［J］. 计算机工程与应用, 2017, 53(8): 203-208. XU Jie, ZHOU Zili, PAN Dayu, et al. Study on fast and nondestructive method of rice grain parameters acquisition of rice panicle［J］. Computer Engineering and Applications, 2017, 53(8): 203-208. ［12］赵三琴, 李毅念, 丁为民, 等.稻穗结构图像特征与籽粒数相关关系分析［J］. 农业机械学报, 2014, 45(12): 323-328. ZHAO Sanqin, LI Yinian, DING Weimin, et al. Relative analysis between image characteristics of panicle structure and spikelet number［J］. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(12): 323-328. ［13］SCHARR H, MINERVINI M, KLUKAS C, et al. Leaf segmentation in plant phenotyping: A collation study［J］. Machine Vision & Applications, 2016, 27(4): 1-22. ［14］NAVARRO P J, FERNANDO P, JULIA W, et al. Machine learning and computer vision system for phenotype data acquisition and analysis in plants［J］. Sensors, 2016, 16(5): 641. ［15］NASERI M, HEIDARI S, GHEIBI R, et al. A novel quantum binary images thinning algorithm: A quantum version of the Hilditch’s algorithm［J］. Optik-International Journal for Light and Electron Optics, 2017, 131: 678-686. ［16］MAJI S, BERG A C, MALIK J. Efficient classification for additive kernel svms［J］. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2013, 35(1): 66-77. ［17］YUAN M S, YANG Z J, HUANG G Z, et al. Feature selection by maximizing correlation information for integrated high-dimensional protein data［J］. Pattern Recognition Letters, 2017, 92: 17-24.