ArticleFrontiers in plant science2021
Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.
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 10 papers.
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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.
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Leveraging generative adversarial networks and SE-UNet for high-precision tobacco leaf image segmentation and blend uniformity detection.Scientific reports · 2026Article
- Investigating performance and key factors for real-world deployment of grain image classification using convolutional neural networks.Scientific reports · 2026Article
- Deep Learning Models for Detection and Severity Assessment of Cercospora Leaf Spot (Plants (Basel, Switzerland) · 2025Article
- An Analysis of Plant Diseases Identification Based on Deep Learning Methods.The plant pathology journal · 2023Article
- Recent advances in plant disease severity assessment using convolutional neural networks.Scientific reports · 2023Article
- Overlapped tobacco shred image segmentation and area computation using an improved Mask RCNN network and COT algorithm.Frontiers in plant science · 2023Article
- Efficient Noninvasive FHB Estimation using RGB Images from a Novel Multiyear, Multirater Dataset.Plant phenomics (Washington, D.C.) · 2023Article
- Monitoring tar spot disease in corn at different canopy and temporal levels using aerial multispectral imaging and machine learning.Frontiers in plant science · 2022Article
- Tobacco shred varieties classification using Multi-Scale-X-ResNet network and machine vision.Frontiers in plant science · 2022Article
- Wheat Spike Blast Image Classification Using Deep Convolutional Neural Networks.Frontiers in plant science · 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
8 authors at 1 institution in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Wheat blast is a threat to global wheat production, and limited blast-resistant cultivars are available. The current estimations of wheat spike blast severity rely on human assessments, but this technique could have limitations. Reliable visual disease estimations paired with Red Green Blue (RGB) images of wheat spike blast can be used to train deep convolutional neural networks (CNN) for disease severity (DS) classification. Inter-rater agreement analysis was used to measure the reliability of who collected and classified data obtained under controlled conditions. We then trained CNN models to classify wheat spike blast severity. Inter-rater agreement analysis showed high accuracy and low bias before model training. Results showed that the CNN models trained provide a promising approach to classify images in the three wheat blast severity categories. However, the models trained on non-matured and matured spikes images showing the highest precision, recall, and F1 score when classifying the images. The high classification accuracy could serve as a basis to facilitate wheat spike blast phenotyping in the future.
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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.