ArticleScientific reports2020
Interactive machine learning for soybean seed and seedling quality classification.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- SeedlingNet: A colour-based segmentation approach towards classification of plant species seedlings.MethodsX · 2026Article
- Machine learning recovers folk classification ofiScience · 2026Article
- Deep learning-based seed germination prediction using morphological traits and RGB images.BMC plant biology · 2026Article
- A crop cultivation monitoring platform for evaluating the early growth of cucumbers.Frontiers in plant science · 2026Article
- Identification and Classification of Snack-Type Watermelon (Foods (Basel, Switzerland) · 2025Article
- GABA-mediated modulation of drought stress tolerance and seed morphology during flax (Linum usitatissimum L.) germination via image analysis.Scientific reports · 2025Article
- Current State of the Art and Potential for Construction and Demolition Waste Processing: A Scoping Review of Sensor-Based Quality Monitoring and Control for In- and Online Implementation in Production Processes.Sensors (Basel, Switzerland) · 2025Review
- Machine Learning-Based Morphological Classification and Diversity Analysis of Ornamental Pumpkin Seeds.Foods (Basel, Switzerland) · 2025Article
- The Use of Imaging to Quantify the Impact of Seed Aging on Lettuce Seed Germination and Seedling Vigor.Sensors (Basel, Switzerland) · 2024Article
- Fungal identification in peanuts seeds through multispectral images: Technological advances to enhance sanitary quality.Frontiers in plant science · 2023Article
- Integrating optical imaging techniques for a novel approach to evaluate Siberian wild rye seed maturity.Frontiers in plant science · 2023Article
- Dimension Reduction of Digital Image Descriptors in Neural Identification of Damaged Malting Barley Grains.Sensors (Basel, Switzerland) · 2022Article
- Predicting the quality of soybean seeds stored in different environments and packaging using machine learning.Scientific reports · 2022Article
- A Reliable Method to Recognize Soybean Seed Maturation Stages Based on Autofluorescence-Spectral Imaging Combined With Machine Learning Algorithms.Frontiers in plant science · 2022Article
- An Approach Using Emerging Optical Technologies and Artificial Intelligence Brings New Markers to Evaluate Peanut Seed Quality.Frontiers in plant science · 2022Article
- Robust seed germination prediction using deep learning and RGB image data.Scientific reports · 2021Article
- Autofluorescence-spectral imaging as an innovative method for rapid, non-destructive and reliable assessing of soybean seed quality.Scientific reports · 2021Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of this study was to propose an approach based on interactive and traditional machine learning methods to classify soybean seeds and seedlings according to their appearance and physiological potential. In addition, we correlated the appearance of seeds to their physiological performance. Images of soybean seeds and seedlings were used to develop models using low-cost approaches and free-access software. The models developed showed high performance, with overall accuracy reaching 0.94 for seeds and seedling classification. The high precision of the models that were developed based on interactive and traditional machine learning demonstrated that the method can easily be used to classify soybean seeds according to their appearance, as well as to classify soybean seedling vigor quickly and non-subjectively. The appearance of soybean seeds is strongly correlated with their physiological performance.
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