Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with the important agronomic traits, such as yield, non-brittle spike, and easy threshing. Biologists and breeders are interested in the spike characteristics, such as length, number of spikelets per spike, number of kernels per spike, size and shape of kernels, and their weight per spike as well as spike shape and density, presence or lack of awns, and spike and awn color. Spikes differ in their shape, size, density, awnedness, color, and so on. The spike shape is controlled by a set of genes; their study will make it possible to purposefully create new cultivars with improved yield characteristics, easier threshing, and resistance to environmental factors.
Currently, the spike characteristics in most studies are assessed by an expert via a visual analysis and measurements, which is rather time-consuming; the more so as the modern experiments involve tens of thousands of plants. Correspondingly, automation of this laborious and time-consuming process is relevant for the science and breeding. The efficiency of plant phenotyping can be increased by technologies of digital image analysis.
A method for wheat spike morphometry utilizing 2D image analysis:
The spike is captured on a blue background (‘table’ protocol) or is vertically fixed with a clip holder (‘clip’ protocol). The holder allows spikes to be fixed at different angles relative to the spike axis.
Each image contains a ColorChecker Mini Classic target (https://xritephoto.com/camera) for color correction. This correction allows for avoiding color shifts in the images, which result from differences in the lighting conditions. Another advantage in using the color scale is its standard size (68 × 108 mm), allowing for assessment of image scale.
The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Section model, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning.
The implementation of the wheat ear recognition method is available at: http://wheatdb.org/werecognizer
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