ArticleFood science & nutrition2024
Maize seeds forecasting with hybrid directional and bi-directional long short-term memory models.
Article in Food science & nutrition, 2024. 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.
- Detection of Frost-Damaged Maize Kernels for Food Quality Screening Using Hyperspectral Imaging and 2D-COS-Assisted Deep Learning.Food science & nutrition · 2026Article
- Grading evaluation of haploid fertility restoration traits based on inception-ResNet in maize.Plant phenomics (Washington, D.C.) · 2025Article
- Real-Time and Fully Automated Robotic Stacking System with Deep Learning-Based Visual Perception.Sensors (Basel, Switzerland) · 2025Article
- Maize seeds forecasting with hybrid directional and bi-directional long short-term memory models.Food science & nutrition · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purity of the seeds is one of the important factors that increase the yield. For this reason, the classification of maize cultivars constitutes a significant problem. Within the scope of this study, six different classification models were designed to solve this problem. A special dataset was created to be used in the models designed for the study. The dataset contains a total of 14,469 images in four classes. Images belong to four different maize types, BT6470, CALIPOS, ES_ARMANDI, and HIVA, taken from the BIOTEK company. AlexNet and ResNet50 architectures, with the transfer learning method, were used in the models created for the image classification. In order to improve the classification success, LSTM (Directional Long Short-Term Memory) and BiLSTM (Bi-directional Long Short-Term Memory) algorithms and AlexNet and ResNet50 architectures were hybridized. As a result of the classifications, the highest classification success was obtained from the ResNet50+BiLSTM model with 98.10%.
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Registered trials
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