ArticleFrontiers in plant science2021
Hyperspectral Imaging Combined With Deep Transfer Learning for Rice Disease Detection.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed, 75 citations in OpenAlex.
- Limits and benefits of unsupervised transfer learning for Raman spectroscopy-based bioprocess monitoring.Bioprocess and biosystems engineering · 2026Article
- Beyond the Visual Spectrum: From RGB-Based Learning to Hyperspectral Intelligence for Plant Disease Detection-Challenges and Opportunities.Sensors (Basel, Switzerland) · 2026Review
- Integration of LLMs and VLMs in plant stress phenotyping: From trait recognition to decision support.Plant phenomics (Washington, D.C.) · 2026Review
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- Genetic enhancement of root, tuber and cereal crops via pangenomics, multi-omics integration and AI-driven prediction.Frontiers in plant science · 2026Review
- Recent advances in plant disease detection: challenges and opportunities.Plant methods · 2025Review
- Hyperspectral Imaging and Machine Learning for Diagnosing Rice Bacterial Blight Symptoms Caused byPlants (Basel, Switzerland) · 2025Article
- A review of deep learning architectures for plant disease detection.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
- A hyperspectral imaging and machine learning approach for rapid and non-invasive diagnosis of cassava bacterial blight.Frontiers in plant science · 2025Article
- Hyperspectral Imaging Combined with Deep Transfer Learning to Evaluate Flavonoids Content inInternational journal of molecular sciences · 2024Article
- Early surveillance of rice bakanae disease using deep learning and hyperspectral imaging.aBIOTECH · 2024Article
- Rice quality prediction and assessment of pesticide residue changes during storage based on Quatformer.Scientific reports · 2024Article
- DCPlant phenomics (Washington, D.C.) · 2024Article
- Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis.Plant phenomics (Washington, D.C.) · 2024Article
- ADFSNet: An Adaptive Domain Feature Separation Network for the Classification of Wheat Seed Using Hyperspectral Images.Sensors (Basel, Switzerland) · 2023Article
- A deep learning model for rapid classification of tea coal disease.Plant methods · 2023Article
- Y-Net: Identification of Typical Diseases of Corn Leaves Using a 3D-2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module.Sensors (Basel, Switzerland) · 2023Article
- Convolutional neural network in rice disease recognition: accuracy, speed and lightweight.Frontiers in plant science · 2023Review
- Quantifying physiological trait variation with automated hyperspectral imaging in rice.Frontiers in plant science · 2023Article
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4 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Various rice diseases threaten the growth of rice. It is of great importance to achieve the rapid and accurate detection of rice diseases for precise disease prevention and control. Hyperspectral imaging (HSI) was performed to detect rice leaf diseases in four different varieties of rice. Considering that it costs much time and energy to develop a classifier for each variety of rice, deep transfer learning was firstly introduced to rice disease detection across different rice varieties. Three deep transfer learning methods were adapted for 12 transfer tasks, namely, fine-tuning, deep CORrelation ALignment (CORAL), and deep domain confusion (DDC). A self-designed convolutional neural network (CNN) was set as the basic network of the deep transfer learning methods. Fine-tuning achieved the best transferable performance with an accuracy of over 88% for the test set of the target domain in the majority of transfer tasks. Deep CORAL obtained an accuracy of over 80% in four of all the transfer tasks, which was superior to that of DDC. A multi-task transfer strategy has been explored with good results, indicating the potential of both pair-wise, and multi-task transfers. A saliency map was used for the visualization of the key wavelength range captured by CNN with and without transfer learning. The results indicated that the wavelength range with and without transfer learning was overlapped to some extent. Overall, the results suggested that deep transfer learning methods could perform rice disease detection across different rice varieties. Hyperspectral imaging, in combination with the deep transfer learning method, is a promising possibility for the efficient and cost-saving field detection of rice diseases among different rice varieties.
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