Abstract
Wheat spike number, which could be rapidly and accurately estimated by the image processing technology, serves as the basis for crop growth monitoring and yield prediction. In this research, simple linear iterative clustering (SLIC) was performed for superpixel segmentation of the digital images of field-grown wheat. Firstly, certain characteristic color parameters were extracted and analyzed from the digital images, and the classifiers with the highest accuracy were chosen for subsequent image classification. Next, the main body of wheat spike was extracted through a series of morphological transformation and estimate was performed for each region. Backbone of the head was extracted, and the number of inflection points of backbone was detected. Then the wheat spike number was determined by combining the estimate of inflection points of backbone and the estimate for each region. Finally, the wheat spike number estimate was verified under four nitrogen fertilizer levels. The results were as follows: (1) Super green value (Eg) and normalized red green index (Dgr) were used as classification features to recognize wheat spikes, soil and leaves; (2) compared with pixel-based image processing, wheat spike recognition effect was much better after superpixel segmentation, as the main body of wheat spike extracted was more clear and morphology more intact; and (3) wheat plants had better growth under high nitrogen fertilizer level, and the accuracy of wheat spike number estimation was also the highest, which was 94.01%. The growth status was the worst under no nitrogen fertilizer application, and the accuracy of wheat spikes number estimation was also the lowest, which was only 80.8%. After excluding the no nitrogen condition, the accuracy of wheat spikes number estimation among mixed samples with more uniform growth status was up to 93.8%, which was an increase by 10.1% than before the exclusion. Wheat spikes number estimate based on superpixel segmentation and color features was a rapid and accurate method that was applicable to the field environment. However, this method was not recommended for use when the growth status of wheat was poor or of high heterogeneity. The findings provided reference for field-grown wheat yield estimate.
Highlights
Wheat has been the most important cereal crop worldwide and one of the most important food crops in China
The results showed that the classifier accuracy was 80% without nitrogen fertilizer application (N1), and medGSVM took on the highest level with the accuracy of 85.63%
Under low nitrogen fertilizer application level (N2), different classifiers varied little in accuracy, which was generally around 88%. finGSVM was the optimal classifier under this level, with accuracy reaching 90.93%
Summary
Zhou et al (2018) proposed a new algorithm that used computer vision to accurately identify wheat spikes in digital images, and adopted multi-feature optimization and a twin-support-vector-machine segmentation (TWSVMSeg) model to determine the number of spikes. After certain pre-processing, pixels on the digital images of wheat were grouped together into superpixels, and wheat spikes were recognized based on superpixel segmentation, to reduce the interference from non-relevant pixels in the process of extracting image features and to improve the recognition effect. In order to estimate wheat spike number in field environment rapidly and accurately, simple linear iterative clustering (SLIC) was applied to the digital images for superpixel segmentation, and the wheat spikes were recognized based on color features. The purpose was to provide a new reliable pathway to accurate wheat spike estimate
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