ArticleFrontiers in plant science2024
Efficient residual network using hyperspectral images for corn variety identification.
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 6 papers.
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Who cites it
6 citing papers in PubMed.
- VIS-NIR-SWIR Proximal Spectroradiometry Coupled with Machine Learning and Deep Learning for Ornamental Plant Identification and Classification.ACS omega · 2026Article
- Residual Derivative-Guided Spectral Fusion Module for Few-Shot Classification of Soybean Seed Varieties Using Hyperspectral Imaging.Foods (Basel, Switzerland) · 2026Article
- MobileNet-WDD: a lightweight deep learning image classification method for identifying defects in wheat grains.Frontiers in plant science · 2026Article
- Variety Identification of Corn Seeds Based on Hyperspectral Imaging and Residual Mamba 1D CNN.Foods (Basel, Switzerland) · 2025Article
- Variety Identification of Corn Seeds Based on Hyperspectral Imaging and Convolutional Neural Network.Foods (Basel, Switzerland) · 2025Article
- LWheatNet: a lightweight convolutional neural network with mixed attention mechanism for wheat seed classification.Frontiers in plant science · 2024Article
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Authors and funding
7 authors.
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Abstract
Corn seeds are an essential element in agricultural production, and accurate identification of their varieties and quality is crucial for planting management, variety improvement, and agricultural product quality control. However, more than traditional manual classification methods are needed to meet the needs of intelligent agriculture. With the rapid development of deep learning methods in the computer field, we propose an efficient residual network named ERNet to identify hyperspectral corn seeds. First, we use linear discriminant analysis to perform dimensionality reduction processing on hyperspectral corn seed images so that the images can be smoothly input into the network. Second, we use effective residual blocks to extract fine-grained features from images. Lastly, we detect and categorize the hyperspectral corn seed images using the classifier softmax. ERNet performs exceptionally well compared to other deep learning techniques and conventional methods. With 98.36% accuracy rate, the result is a valuable reference for classification studies, including hyperspectral corn seed pictures.
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