Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images

被引:127
作者
Fernandez-Gallego, Jose A. [1 ]
Kefauver, Shawn C. [1 ]
Aparicio Gutierrez, Nieves [2 ]
Teresa Nieto-Taladriz, Maria [3 ]
Luis Araus, Jose [1 ]
机构
[1] Univ Barcelona, Fac Biol, Dept Evolutionary Biol Ecol & Environm Sci, Plant Physiol Sect, Diagonal 643, Barcelona 08028, Spain
[2] Inst Tecnol Agr Castilla & Leon ITACyL, Ctra Burgos Km 119, Valladolid 47071, Spain
[3] Inst Nacl Invest & Tecnol Agr & Alimentaria INIA, Ctra Coruna Km 7-5, Madrid 28040, Spain
来源
PLANT METHODS | 2018年 / 14卷
关键词
Digital image processing; Ear counting; Field phenotyping; Laplacian frequency filter; Median filter; Find maxima; Wheat; ENVIRONMENTALLY ADAPTIVE SEGMENTATION; APPLE FRUITS; GRAIN-YIELD; VISION; RECOGNITION; DELINEATION; COMPONENTS; ALGORITHM; ORCHARD; STAGE;
D O I
10.1186/s13007-018-0289-4
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: The number of ears per unit ground area (ear density) is one of the main agronomic yield components in determining grain yield in wheat. A fast evaluation of this attribute may contribute to monitoring the efficiency of crop management practices, to an early prediction of grain yield or as a phenotyping trait in breeding programs. Currently the number of ears is counted manually, which is time consuming. Moreover, there is no single standardized protocol for counting the ears. An automatic ear-counting algorithm is proposed to estimate ear density under field conditions based on zenithal color digital images taken from above the crop in natural light conditions. Field trials were carried out at two sites in Spain during the 2014/2015 crop season on a set of 24 varieties of durum wheat with two growing conditions per site. The algorithm for counting uses three steps: (1) a Laplacian frequency filter chosen to remove low and high frequency elements appearing in an image, (2) a Median filter to reduce high noise still present around the ears and (3) segmentation using Find Maxima to segment local peaks and determine the ear count within the image. Results: The results demonstrate high success rate (higher than 90%) between the algorithm counts and the manual (image-based) ear counts, and precision, with a low standard deviation (around 5%). The relationships between algorithm ear counts and grain yield was also significant and greater than the correlation with manual (field-based) ear counts. In this approach, results demonstrate that automatic ear counting performed on data captured around anthesis correlated better with grain yield than with images captured at later stages when the low performance of ear counting at late grain filling stages was associated with the loss of contrast between canopy and ears. Conclusions: Developing robust, low-cost and efficient field methods to assess wheat ear density, as a major agronomic component of yield, is highly relevant for phenotyping efforts towards increases in grain yield. Although the phenological stage of measurements is important, the robust image analysis algorithm presented here appears to be amenable from aerial or other automated platforms.
引用
收藏
页数:12
相关论文
共 42 条
  • [21] CENTER WEIGHTED MEDIAN FILTERS AND THEIR APPLICATIONS TO IMAGE-ENHANCEMENT
    KO, SJ
    LEE, YH
    [J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, 1991, 38 (09): : 984 - 993
  • [22] Automated tree recognition in old growth conifer stands with high resolution digital imagery
    Leckie, DG
    Gougeon, FA
    Tims, S
    Nelson, T
    Burnett, CN
    Paradine, D
    [J]. REMOTE SENSING OF ENVIRONMENT, 2005, 94 (03) : 311 - 326
  • [23] Stand delineation and composition estimation using semi-automated individual tree crown analysis
    Leckie, DG
    Gougeon, FA
    Walsworth, N
    Paradine, D
    [J]. REMOTE SENSING OF ENVIRONMENT, 2003, 85 (03) : 355 - 369
  • [24] Identifying blueberry fruit of different growth stages using natural outdoor color images
    Li, Han
    Lee, Won Suk
    Wang, Ku
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2014, 106 : 91 - 101
  • [25] Liu Tao Liu Tao, 2014, Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery, V45, P282
  • [26] Automatic green fruit counting in orange trees using digital images
    Maldonado, Walter, Jr.
    Barbosa, Jose Carlos
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2016, 127 : 572 - 581
  • [27] Estimating mango crop yield using image analysis using fruit at 'stone hardening' stage and night time imaging
    Payne, A.
    Walsh, K.
    Subedi, P.
    Jarvis, D.
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2014, 100 : 160 - 167
  • [28] Estimation of mango crop yield using image analysis - Segmentation method
    Payne, A. B.
    Walsh, K. B.
    Subedi, P. P.
    Jarvis, D.
    [J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2013, 91 : 57 - 64
  • [29] Automated tree crown detection and delineation in high-resolution digital camera imagery of coniferous forest regeneration
    Pouliot, DA
    King, DJ
    Bell, FW
    Pitt, DG
    [J]. REMOTE SENSING OF ENVIRONMENT, 2002, 82 (2-3) : 322 - 334
  • [30] Reynolds MP., 1996, INCREASING YIELD POT, P101, DOI 10.1007/BF00989140