ArticlePlants (Basel, Switzerland)2021
Image-Based Wheat Fungi Diseases Identification by Deep Learning.
Article in Plants (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 127 citations in OpenAlex.
- Efficient deep learning framework for arecanut disease detection using graph neural network and Bat algorithm.Scientific reports · 2026Article
- Transfer learning models for wheat ear detection on multi-source dataset.Scientific reports · 2025Article
- Novel dual-input stream-based hybrid approach for wheat leaf disease classification using edge-aware features.Scientific reports · 2025Article
- Hyperspectral image analysis for classification of multiple infections in wheat.Plant methods · 2025Article
- LDSL framework: a lightweight dual-stream learning framework for wheat disease detection.Plant methods · 2025Article
- An improved YOLOv8n model for in-field detection of pests and diseases in pakchoi.Frontiers in plant science · 2025Article
- Regional feature purification contrastive learning for wheat biotic stress detection.Frontiers in plant science · 2025Article
- An intelligent identification for pest and disease detection in wheat leaf based on environmental data using multimodal data fusion.Frontiers in plant science · 2025Article
- Integrating deep learning for visual question answering in Agricultural Disease Diagnostics: Case Study of Wheat Rust.Scientific reports · 2024Article
- Deep Learning-Based Classification of Macrofungi: Comparative Analysis of Advanced Models for Accurate Fungi Identification.Sensors (Basel, Switzerland) · 2024Article
- MnasNet-SimAM: An Improved Deep Learning Model for the Identification of Common Wheat Diseases in Complex Real-Field Environments.Plants (Basel, Switzerland) · 2024Article
- SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images.Plant methods · 2024Article
- Recognition of Wheat Leaf Diseases Using Lightweight Convolutional Neural Networks against Complex Backgrounds.Life (Basel, Switzerland) · 2023Article
- An Interpretable High-Accuracy Method for Rice Disease Detection Based on Multisource Data and Transfer Learning.Plants (Basel, Switzerland) · 2023Article
- An advanced deep learning models-based plant disease detection: A review of recent research.Frontiers in plant science · 2023Review
- A robust and light-weight transfer learning-based architecture for accurate detection of leaf diseases across multiple plants using less amount of images.Frontiers in plant science · 2023Article
- Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset.Plant phenomics (Washington, D.C.) · 2023Article
- A Review on Multiscale-Deep-Learning Applications.Sensors (Basel, Switzerland) · 2022Review
- Medicinal Chrysanthemum Detection under Complex Environments Using the MC-LCNN Model.Plants (Basel, Switzerland) · 2022Article
- Investigating Explanatory Factors of Machine Learning Models for Plant Classification.Plants (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale; thus, one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images, with the possibility of obtaining them in field conditions, using mobile devices. In this work, we propose a method for the recognition of five fungal diseases of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately and in case of multiple diseases, with the possibility of identifying the stage of plant development. A set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was performed by the type of disease. More than 80% of the images in the dataset correspond to single disease labels (including seedlings), more than 12% are represented by healthy plants, and 6% of the images labeled are represented by multiple diseases. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The disease-recognition algorithm is based on the convolutional neural network with the EfficientNet architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented as a bot on the Telegram platform, which allows users to assess plants by lesions in the field conditions.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.