Abstract
The quantitative monitoring of airborne urediniospores of Puccinia striiformis f. sp. tritici (Pst) using spore trap devices in wheat fields is an important process for devising strategies early and effectively controlling wheat stripe rust. The traditional microscopic spore counting method mainly relies on naked-eye observation. Because of the great number of trapped spores, this method is labour intensive and time-consuming and has low counting efficiency, sometimes leading to huge errors; thus, an alternative method is required. In this paper, a new algorithm was proposed for the automatic detection and counting of urediniospores of Pst, based on digital image processing. First, images of urediniospores were collected using portable volumetric spore traps in an indoor simulation. Then, the urediniospores were automatically detected and counted using a series of image processing approaches, including image segmentation using the K-means clustering algorithm, image pre-processing, the identification of touching urediniospores based on their shape factor and area, and touching urediniospore contour segmentation based on concavity and contour segment merging. This automatic counting algorithm was compared with the watershed transformation algorithm. The results show that the proposed algorithm is efficient and accurate for the automatic detection and counting of trapped urediniospores. It can provide technical support for the development of online airborne urediniospore monitoring equipment.
Highlights
Wheat stripe rust, which is caused by Puccinia striiformis f. sp. tritici (Pst), is a wheat disease that is prevalent across the world, in cool and moist regions[1,2]
On the basis of simulating the wheat field environment, this study develops a new algorithm for the automatic detection and counting of trapped pathogen urediniospores of Pst
It is inevitable that overlap will arise while capturing the urediniospores of wheat stripe rust
Summary
Wheat stripe (yellow) rust, which is caused by Puccinia striiformis f. sp. tritici (Pst), is a wheat disease that is prevalent across the world, in cool and moist regions[1,2]. Li et al.[23] proposed a method based on the watershed transformation algorithm to count the urediniospores of Pst. the results appeared to incorrectly split the positions of the spores and demonstrated over-segmentation when the urediniospores had rough boundaries or a complicated touching condition. A variety of segmentation algorithms for touching objects in images, such as cells[27,28,29], fruits[30,31], and cereals[32,33,34], have been reported, and there have been some achievements in these fields These algorithms are not suitable for the segmentation and counting of spores. On the basis of simulating the wheat field environment, this study develops a new algorithm for the automatic detection and counting of trapped pathogen urediniospores of Pst. First, the urediniospores are segmented using the K-means clustering algorithm and converted into a binary image. The objective of this study is to develop a new algorithm for the automatic detection and counting of trapped pathogen spores, and to assess the algorithm’s detection accuracy by comparing the automatic counting algorithm with the watershed transformation algorithm
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