ArticlePlant methods2022
Rice bacterial blight resistant cultivar selection based on visible/near-infrared spectrum and deep learning.
Article in Plant methods, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- High-Throughput Phenotyping: Status and Applications in Rice Breeding.Plants (Basel, Switzerland) · 2026Review
- Advances in molecular breeding of medicinal plants.Molecular horticulture · 2026Review
- Current Progress and Future Trends of Genomics-Based Techniques for Food Adulteration Identification.Foods (Basel, Switzerland) · 2025Review
- Hyperspectral Imaging and Machine Learning for Diagnosing Rice Bacterial Blight Symptoms Caused byPlants (Basel, Switzerland) · 2025Article
- Bactericidal bissulfone BScience China. Life sciences · 2024Article
- DCPlant phenomics (Washington, D.C.) · 2024Article
- Spectral Reflectance Indexes Reveal Differences in the Physiological Status ofPlants (Basel, Switzerland) · 2023Article
- Concise Cascade Methods for Transgenic Rice Seed Discrimination using Spectral Phenotyping.Plant phenomics (Washington, D.C.) · 2023Article
- A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning.Plant methods · 2022Article
- Evaluation of rice bacterial blight severity from lab to field with hyperspectral imaging technique.Frontiers in plant science · 2022Article
- Combination of spectral index and transfer learning strategy for glyphosate-resistant cultivar identification.Frontiers in plant science · 2022Article
- Novel Vegetation Indices to Identify Broccoli Plants Infected WithFrontiers in plant science · 2022Article
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7 authors.
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Abstract
backgroundRice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes.
resultsIn this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS-NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization.
conclusionsThis study illustrated that informative VIS-NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.
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