Using computer vision to monitor germination time course of sunflower (Helianthus annuus L.) seeds

Summary A computer-controlled system was used to monitor the germination time course of sunflower seeds. The system integrates a Jacobsen table, a controlled lighting environment, a video camera and a computer. Software was developed to control image recording and image analysis. The algorithm retained for detecting and counting the germinated seeds was validated in two ways: 1. First, human operators read images independently and the variability between readers was compared to the computer reading. 2. Then, computer counts were compared to counts of germination carried out directly on the Jacobsen table. The whole equipment (automatic shooting and algorithm connected to the germination system) was tested to plot germination time courses of three sunflower seed lots at 20°C. Detailed germination curves were obtained allowing a perfect fitting in a probit model and a comparison of the seed lots. These results confirm the high potential of artificial vision in quality evaluation of seeds.

[1]  A. Clement,et al.  Unsupervised classification of pixels in color images by hierarchical analysis of bi-dimensional histograms , 2002, IEEE International Conference on Systems, Man and Cybernetics.

[2]  Kent J. Bradford,et al.  A hydrothermal time model explains the cardinal temperatures for seed germination , 2002 .

[3]  W. EckvanJ.,et al.  The application of image analysis in monitoring the imbibition process of white cabbage (Brassica oleracea L.) seeds , 2000, Seed Science Research.

[4]  Dominique Bertrand,et al.  Comparison of multilayer perceptron and probabilistic neural networks in artificial vision. Application to the discrimination of seeds , 1997 .

[5]  A. Trewavas,et al.  Sensitivity Thresholds and Variable Time Scales in Plant Hormone Action , 1994, Plant physiology.

[6]  A. Tarquis,et al.  A population-based threshold model describing the relationship between germination rates and seed deterioration , 1993 .

[7]  M. Scott Howarth,et al.  Measurement of seedling growth rate by machine vision , 1993, Other Conferences.

[8]  Luc Vincent,et al.  Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  E. H. Roberts,et al.  The Influence of Temperature on Seed Germination Rate in Grain Legumes I. A COMPARISON OF CHICKPEA, LENTIL, SOYABEAN AND COWPEA AT CONSTANT TEMPERATURES , 1986 .

[10]  R. J. Gummerson The Effect of Constant Temperatures and Osmotic Potentials on the Germination of Sugar Beet , 1986 .

[11]  J. L. Monteith,et al.  Time, Temperature and Germination of Pearl Millet (Pennisetum typhoides S. & H.) II. ALTERNATING TEMPERATURE , 1982 .

[12]  T. Orchard Estimating the parameters of plant seedling emergence , 1977 .

[13]  K. Fujimura,et al.  A system for automated seed vigour assessment , 2001 .

[14]  D. Demilly,et al.  Carrot seeds grading using a vision system , 2001 .

[15]  M. Kruse The effect of moisture content on linear dimensions in cereal seeds measured by image analysis. , 2000 .

[16]  R. V. D. Schoor,et al.  Automatic determination of germination of seeds , 2000 .

[17]  S. R. Draper,et al.  The measurement of new characters for cultivar identification in wheat using machine vision , 1986 .

[18]  Michael J. Black,et al.  Seeds: Germination, Structure, and Composition , 1985 .