ArticleFrontiers in plant science2021
Automatic Diagnosis of Rice Diseases Using Deep Learning.
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 21 papers.
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21 citing papers in PubMed, 172 citations in OpenAlex.
- A hybrid deep learning model with adaptive feature fusion for automated rice leaf disease detection and classification.Scientific reports · 2026Article
- RiceDetect-Net: a lightweight real-time detection framework for rice diseases.BMC plant biology · 2026Article
- Evaluation of deep learning models using explainable AI with qualitative and quantitative analysis for rice leaf disease detection.Scientific reports · 2025Article
- Deep learning-based automatic diagnosis of rice leaf diseases using ensemble CNN models.Scientific reports · 2025Article
- Rice leaf disease classification using a fusion vision approach.Scientific reports · 2025Article
- Enhancing multiclass plant disease classification using GAN-boosted vision transformer with XAI insights.Frontiers in plant science · 2025Article
- LiSA-MobileNetV2: an extremely lightweight deep learning model with Swish activation and attention mechanism for accurate rice disease classification.Frontiers in plant science · 2025Article
- Deep learning-based approaches for weed detection in crops.Frontiers in plant science · 2025Review
- Comprehensive plant health monitoring: expert-level assessment with spatio-temporal image data.Frontiers in plant science · 2025Article
- Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification.Sensors (Basel, Switzerland) · 2024Article
- Deep transfer learning with gravitational search algorithm for enhanced plant disease classification.Heliyon · 2024Article
- An Analysis of Plant Diseases Identification Based on Deep Learning Methods.The plant pathology journal · 2023Article
- A Fine-Grained Image Classification Approach for Dog Feces Using MC-SCMNet under Complex Backgrounds.Animals : an open access journal from MDPI · 2023Article
- The Genetic Mechanism of the Immune Response to the Rice False Smut (RFS) FungusPlants (Basel, Switzerland) · 2023Review
- MS-Net: a novel lightweight and precise model for plant disease identification.Frontiers in plant science · 2023Article
- A novel fine-tuned deep-learning-based multi-class classifier for severity of paddy leaf diseases.Frontiers in plant science · 2023Article
- Crop-saving with AI: latest trends in deep learning techniques for plant pathology.Frontiers in plant science · 2023Review
- Convolutional neural network in rice disease recognition: accuracy, speed and lightweight.Frontiers in plant science · 2023Review
- Orchard monitoring based on unmanned aerial vehicles and image processing by artificial neural networks: a systematic review.Frontiers in plant science · 2023Review
- A hybrid attention-enhanced DenseNet neural network model based on improved U-Net for rice leaf disease identification.Frontiers in plant science · 2022Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rice disease has serious negative effects on crop yield, and the correct diagnosis of rice diseases is the key to avoid these effects. However, the existing disease diagnosis methods for rice are neither accurate nor efficient, and special equipment is often required. In this study, an automatic diagnosis method was developed and implemented in a smartphone app. The method was developed using deep learning based on a large dataset that contained 33,026 images of six types of rice diseases: leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot. The core of the method was the Ensemble Model in which submodels were integrated. Finally, the Ensemble Model was validated using a separate set of images. Results showed that the three best submodels were DenseNet-121, SE-ResNet-50, and ResNeSt-50, in terms of several attributes, such as, learning rate, precision, recall, and disease recognition accuracy. Therefore, these three submodels were selected and integrated in the Ensemble Model. The Ensemble Model minimized confusion among the different types of disease, reducing misdiagnosis of the disease. Using the Ensemble Model to diagnose six types of rice diseases, an overall accuracy of 91% was achieved, which is considered to be reasonably good, considering the appearance similarities in some types of rice disease. The smartphone app allowed the client to use the Ensemble Model on the web server through a network, which was convenient and efficient for the field diagnosis of rice leaf blast, false smut, neck blast, sheath blight, bacterial stripe disease, and brown spot.
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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.