ArticleFrontiers in plant science2020
Automated Spike Detection in Diverse European Wheat Plants Using Textural Features and the Frangi Filter in 2D Greenhouse Images.
Article in Frontiers in plant science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 19 citations in OpenAlex.
- Wheat spike and spikelet detection and counting from high-resolution digital imagery using YOLO with Oriented Bounding Boxes.Scientific reports · 2026Article
- Beyond high-throughput: leveraging plant phenotyping to improve understanding and prediction of plant growth through process-based models.The New phytologist · 2026Review
- Transfer learning models for wheat ear detection on multi-source dataset.Scientific reports · 2025Article
- High-Throughput Spike Detection in Greenhouse Cultivated Grain Crops with Attention Mechanisms-Based Deep Learning Models.Plant phenomics (Washington, D.C.) · 2024Article
- Feature diffusion reconstruction mechanism network for crop spike head detection.Frontiers in plant science · 2024Article
- Automated extraction of pod phenotype data from micro-computed tomography.Frontiers in plant science · 2023Article
- Image-based classification of wheat spikes by glume pubescence using convolutional neural networks.Frontiers in plant science · 2023Article
- Wheat Ear Segmentation Based on a Multisensor System and Superpixel Classification.Plant phenomics (Washington, D.C.) · 2022Article
- Dynamic Color Transform Networks for Wheat Head Detection.Plant phenomics (Washington, D.C.) · 2022Article
- SlypNet: Spikelet-based yield prediction of wheat using advanced plant phenotyping and computer vision techniques.Frontiers in plant science · 2022Article
- Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods.Sensors (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors at 1 institution in 1 country.
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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.
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