Abstract
Spike is one of the crop yield organs in wheat plants. Determination of the phenological stages, including heading time point (HTP), and area of spike from non-invasive phenotyping images provides the necessary information for the inference of growth-related traits. The algorithm previously developed by Qiongyan et al. for spike detection in 2-D images turns out to be less accurate when applied to the European cultivars that produce many more leaves. Therefore, we here present an improved and extended method where (i) wavelet amplitude is used as an input to the Laws texture energy-based neural network instead of original grayscale images and (ii) non-spike structures (e.g., leaves) are subsequently suppressed by combining the result of the neural network prediction with a Frangi-filtered image. Using this two-step approach, a 98.6% overall accuracy of neural network segmentation based on direct comparison with ground-truth data could be achieved. Moreover, the comparative error rate in spike HTP detection and growth correlation among the ground truth, the algorithm developed by Qiongyan et al., and the proposed algorithm are discussed in this paper. The proposed algorithm was also capable of significantly reducing the error rate of the HTP detection by 75% and improving the accuracy of spike area estimation by 50% in comparison with the Qionagyan et al. method. With these algorithmic improvements, HTP detection on a diverse set of 369 plants was performed in a high-throughput manner. This analysis demonstrated that the HTP of 104 plants (comprises of 57 genotypes) with lower biomass and tillering range (e.g., earlier-heading types) were correctly determined. However, fine-tuning or extension of the developed method is required for high biomass plants where spike emerges within green bushes. In conclusion, our proposed method allows significantly more reliable results for HTP detection and spike growth analysis to be achieved in application to European cultivars with earlier-heading types.
Highlights
Wheat is one of the major crop species in the world, with 762 million tons of grain produced annually (FAOSTAT 2018) and providing ≥ 20% of the world’s calorie and protein demand (Braun et al, 2010)
The results of this study are presented in two sections dedicated to (i) detection of the time point of spike emergence and (ii) spike growth analysis from RGB images acquired using visible light cameras during an experiment with diverse winter wheat varieties
We present an improved method for wheat spike detection in a test data set with 369 plants from 202 diverse winter wheat varieties corresponding to mainly high-yielding Central
Summary
Wheat is one of the major crop species in the world, with 762 million tons of grain produced annually (FAOSTAT 2018) and providing ≥ 20% of the world’s calorie and protein demand (Braun et al, 2010). The increasing world population and climate change are major threats to sustainable crop production (Tester and Langridge, 2010). Information-based plant breeding and precision agriculture are fundamental for identifying suitable wheat varieties to increase wheat productivity and production. One of the important components in both crop breeding and precision agriculture is the monitoring of plant developmental growth stages to apply informeddecision-based treatments in field or greenhouse experiments. Phenology influences grain yield components both directly and indirectly (Snape et al, 2001; Zhang et al, 2009), and in this aspect, quantitative assessment of crop phenology plays an important role in precision phenotyping as a quantifier of crop performance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
