ArticleFrontiers in plant science2024
A maize seed variety identification method based on improving deep residual convolutional network.
Article in Frontiers in plant science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Mung bean seed classification based on multimodal features and Kepler-optimized stacking ensemble learning model.PloS one · 2026Article
- MobileNet-WDD: a lightweight deep learning image classification method for identifying defects in wheat grains.Frontiers in plant science · 2026Article
- Non-destructive identification of closely related japonica rice seeds using hyperspectral imaging combined with HHO-optimized XGBoost.Frontiers in plant science · 2026Article
- Deep learning-based seed variety classification: a case study in maize.BMC plant biology · 2025Article
- Design and Evaluation of an Automated Ultraviolet-C Irradiation System for Maize Seed Disinfection and Monitoring.Sensors (Basel, Switzerland) · 2025Article
- Classification of maize seed hyperspectral images based on variable-depth convolutional kernels.Frontiers in plant science · 2025Article
- DualCMNet: a lightweight dual-branch network for maize variety identification based on multi-modal feature fusion.Frontiers in plant science · 2025Article
- Identification of maize kernel varieties based on interpretable ensemble algorithms.Frontiers in plant science · 2025Article
- Rapid and accurate classification of mung bean seeds based on HPMobileNet.Frontiers in plant science · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Seed quality and safety are related to national food security, and seed variety purity is an essential indicator in seed quality detection. This study established a maize seed dataset comprising 5877 images of six different types and proposed a maize seed recognition model based on an improved ResNet50 framework. Firstly, we introduced the ResStage structure in the early stage of the original model, which facilitated the network's learning process and enabled more efficient information propagation across the network layers. Meanwhile, in the later residual blocks of the model, we introduced both the efficient channel attention (ECA) mechanism and depthwise separable (DS) convolution, which reduced the model's parameter cost and enabled the capturing of more precise and detailed features. Finally, a Swish-PReLU mixed activation function was introduced globally to improve the overall predictive power of the model. The results showed that our model achieved an impressive accuracy of 91.23% in corn seed classification, surpassing other related models. Compared with the original model, our model improved the accuracy by 7.07%, reduced the loss value by 0.19, and decreased the number of parameters by 40%. The research suggested that this method can efficiently classify corn seeds, holding significant value in seed variety identification.
Indexed as
Identifiers
What Socratic 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 Socratic graph.