Abstract Appearance of a plant is an important element in the certification of crop cultivars — there is usually a requirement that it be consistent and distinct from other cultivars. It can, therefore, also be used in the identification of samples of unknown cultivar. Appearance has traditionally been recorded using a mixture of simple measurements (for features such as length and breadth) and subjective scoring (for features such as colour and curvature). This paper describes the results of a project to study the use of automatic, objective image analysis in the discrimination of carrot cultivars. Within-year matching achieves a success rate of 64% with 15 cultivars, which improves to 85% if groups of cultivars included because of their similarity, are amalgamated. Across-year matching to a set of 47 cultivars results in 29% exact matches and 60% in the top five.
[1]
Seishi Ninomiya,et al.
Discrimination of soybean leaflet shape by neural networks with image input
,
2000
.
[2]
S. R. Draper,et al.
automated machine vision system for the morphometry of new cultivars and plant genebank accessions
,
1988
.
[3]
Wojtek J. Krzanowski,et al.
Principles of multivariate analysis : a user's perspective. oxford
,
1988
.
[4]
J. K. A. Bleasdale,et al.
An Objective Method of Recording and Comparing the Shapes of Carrot Roots
,
1963
.
[5]
L. Puzone,et al.
Image analysis in chrysanthemum DUS testing.
,
2000
.
[6]
Graham W. Horgan,et al.
The statistical analysis of plant part appearance — a review
,
2001
.