ArticlePlants (Basel, Switzerland)2021
Automated In Situ Seed Variety Identification via Deep Learning: A Case Study in Chickpea.
Article in Plants (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 67 citations in OpenAlex.
- Machine learning approach for wheat variety identification using single-seed imaging.Scientific reports · 2026Article
- Deep learning-based seed variety classification: a case study in maize.BMC plant biology · 2025Article
- Deep learning driven, image-based phenotyping of seed processing efficiency in sainfoin (Frontiers in plant science · 2025Article
- Research Progress of Spectral Imaging Techniques in Plant Phenotype Studies.Plants (Basel, Switzerland) · 2024Review
- Detection and identification of plant leaf diseases using YOLOv4.Frontiers in plant science · 2024Article
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- A Deep Learning Image System for Classifying High Oleic Sunflower Seed Varieties.Sensors (Basel, Switzerland) · 2023Article
- Leaf Count Aided Novel Framework for Rice (Plants (Basel, Switzerland) · 2022Article
- Near-infrared hyperspectral imaging for online measurement of the viability detection of naturally aged watermelon seeds.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
- Hyperspectral Monitoring of Powdery Mildew Disease Severity in Wheat Based on Machine Learning.Frontiers in plant science · 2022Article
- A Hyperspectral Data 3D Convolutional Neural Network Classification Model for Diagnosis of Gray Mold Disease in Strawberry Leaves.Frontiers in plant science · 2022Article
- Meta-Learning for Few-Shot Plant Disease Detection.Foods (Basel, Switzerland) · 2021Article
- Citrus Huanglongbing Detection Based on Multi-Modal Feature Fusion Learning.Frontiers in plant science · 2021Article
- Detection Method of Citrus Psyllids With Field High-Definition Camera Based on Improved Cascade Region-Based Convolution Neural Networks.Frontiers in plant science · 2021Article
Corrections and comments
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Authors and funding
6 authors at 6 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
On-time seed variety recognition is critical to limit qualitative and quantitative yield loss and asynchronous crop production. The conventional method is a subjective and error-prone process, since it relies on human experts and usually requires accredited seed material. This paper presents a convolutional neural network (CNN) framework for automatic identification of chickpea varieties by using seed images in the visible spectrum (400-700 nm). Two low-cost devices were employed for image acquisition. Lighting and imaging (background, focus, angle, and camera-to-sample distance) conditions were variable. The VGG16 architecture was modified by a global average pooling layer, dense layers, a batch normalization layer, and a dropout layer. Distinguishing the intricate visual features of the diverse chickpea varieties and recognizing them according to these features was conceivable by the obtained model. A five-fold cross-validation was performed to evaluate the uncertainty and predictive efficiency of the CNN model. The modified deep learning model was able to recognize different chickpea seed varieties with an average classification accuracy of over 94%. In addition, the proposed vision-based model was very robust in seed variety identification, and independent of image acquisition device, light environment, and imaging settings. This opens the avenue for the extension into novel applications using mobile phones to acquire and process information in situ. The proposed procedure derives possibilities for deployment in the seed industry and mobile applications for fast and robust automated seed identification practices.
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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.