ArticlePlant methods2023
Classification of multi-year and multi-variety pumpkin seeds using hyperspectral imaging technology and three-dimensional convolutional neural network.
Article in Plant methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Climate-robust evaluation of alfalfa seed maturity via an EMD-guided deep learning framework using multispectral imaging.Plant phenomics (Washington, D.C.) · 2026Article
- Machine Learning-Based Morphological Classification and Diversity Analysis of Ornamental Pumpkin Seeds.Foods (Basel, Switzerland) · 2025Article
- Adaptive pixel attention network for hyperspectral image classification.Scientific reports · 2024Article
- Research on the Method of Imperfect Wheat Grain Recognition Utilizing Hyperspectral Imaging Technology.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundPumpkin seeds are major oil crops with high nutritional value and high oil content. The collection and identification of different pumpkin germplasm resources play a significant role in the realization of precision breeding and variety improvement. In this research, we collected 75 species of pumpkin from the Zhejiang Province of China. 35,927 near-infrared hyperspectral images of 75 types of pumpkin seeds were used as the research object.
resultsTo realize the rapid classification of pumpkin seed varieties, position attention embedded three-dimensional convolutional neural network (PA-3DCNN) was designed based on hyperspectral image technology. The experimental results showed that PA-3DCNN had the best classification effect than other classical machine learning technology. The classification accuracy of 99.14% and 95.20% were severally reached on the training and test sets. We also demonstrated that the PA-3DCNN model performed well in next year's classification with fine-tuning and met with 94.8% accuracy.
conclusionsThe model performance improved by introducing double convolution and pooling structure and position attention module. Meanwhile, the generalization performance of the model was verified, which can be adopted for the classification of pumpkin seeds in multiple years. This study provided a new strategy and a feasible technical approach for identifying germplasm resources of pumpkin seeds.
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