ArticleFrontiers in plant science2020
Identification of Bacterial Blight Resistant Rice Seeds Using Terahertz Imaging and Hyperspectral Imaging Combined With Convolutional Neural Network.
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 24 papers.
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24 citing papers in PubMed, 73 citations in OpenAlex.
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- XooNet: a high-throughput UAV-based approach for field screening of bacterial blight-resistant germplasm in wild rice.Frontiers in plant science · 2026Article
- Enhanced rice leaf disease classification via contour-driven segmentation and optimized deep transfer learning architectures.PloS one · 2026Article
- GCT-BCLN: a bidirectional closed-loop network for nondestructive detection of rice seed vigor using hyperspectral imaging.Frontiers in plant science · 2026Article
- Refining Structural Analysis of Proteins: Automated Methods to Measure Transition Dipole Strength of Single Residues.The journal of physical chemistry. B · 2025Article
- Distinguishing different proteins based on terahertz spectra by visual geometry group 16 neural network.iScience · 2025Article
- A hyperspectral imaging and machine learning approach for rapid and non-invasive diagnosis of cassava bacterial blight.Frontiers in plant science · 2025Article
- Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping.Plant phenomics (Washington, D.C.) · 2023Article
- Rice seed vigor detection based on near-infrared hyperspectral imaging and deep transfer learning.Frontiers in plant science · 2023Article
- Convolutional neural network in rice disease recognition: accuracy, speed and lightweight.Frontiers in plant science · 2023Review
- Identification of Defective Maize Seeds Using Hyperspectral Imaging Combined with Deep Learning.Foods (Basel, Switzerland) · 2022Article
- Applying spectral fractal dimension index to predict the SPAD value of rice leaves under bacterial blight disease stress.Plant methods · 2022Article
- Visible and near-infrared spectroscopy and deep learning application for the qualitative and quantitative investigation of nitrogen status in cotton leaves.Frontiers in plant science · 2022Article
- Rapid and Non-destructive Classification of New and Aged Maize Seeds Using Hyperspectral Image and Chemometric Methods.Frontiers in plant science · 2022Article
- Detecting Asymptomatic Infections of Rice Bacterial Leaf Blight Using Hyperspectral Imaging and 3-Dimensional Convolutional Neural Network With Spectral Dilated Convolution.Frontiers in plant science · 2022Article
- Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification.Frontiers in plant science · 2022Article
- Maize disease detection based on spectral recovery from RGB images.Frontiers in plant science · 2022Article
- Spectral Preprocessing Combined with Deep Transfer Learning to Evaluate Chlorophyll Content in Cotton Leaves.Plant phenomics (Washington, D.C.) · 2022Article
- Rice Seed Purity Identification Technology Using Hyperspectral Image with LASSO Logistic Regression Model.Sensors (Basel, Switzerland) · 2021Article
- Application of Laser-Induced Breakdown Spectroscopy Coupled With Spectral Matrix and Convolutional Neural Network for Identifying Geographical Origins ofFrontiers in artificial intelligence · 2021Article
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Because bacterial blight (BB) disease seriously affects the yield and quality of rice, breeding BB resistant rice is an important priority for plant breeders but the process is time-consuming. The feasibility of using terahertz imaging technology and near-infrared hyperspectral imaging technology to identify BB resistant seeds has therefore been studied. The two-dimensional (2D) spectral images and one-dimensional (1D) spectra provided by both imaging methods were used to build discriminant models based on a deep learning method, the convolutional neural network (CNN), and traditional machine learning methods, support vector machine (SVM), random forest (RF), and partial least squares discriminant analysis (PLS-DA). The highest classification accuracy was achieved by the discriminate model based on CNN using the terahertz absorption spectra. Confusion matrixes were pictured to show the identification details. The t-distributed stochastic neighbor embedding (t-SNE) method was used to visualize the process of CNN data processing. Terahertz imaging technology combined with CNN has great potential to quickly identify BB resistant rice seeds and is more accurate than using near-infrared hyperspectral imaging.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.