Deep learning

This study explores deep-learning models for automated rice grain classification, which is essential for quality control, cost optimization, and supply chain efficiency. Testing models like ResNet, VGG, EfficientNet, and MobileNet on a dataset of 75,000 images across five rice categories, results showed that EfficientNet achieved the highest accuracy (99.67%), while MobileNet excelled in speed. The study concludes that deep-learning models offer scalable, efficient handling of large, complex data, outperforming traditional machine-learning methods.

This paper presents a new method using a Convolutional Neural Network (CNN) to estimate sea ice concentration in the Baltic Sea by combining Sentinel-1 and AMSR2 data. The CNN architecture retains different resolution inputs without losing valuable information. By using a focal loss function and skip connection, the proposed model achieves a high R2 score of 90.6%.

Presenting a novel CNN model merging SAR and AMSR2 imaging for precise maritime ice-water detection. This fusion enables automatic sea ice chart generation, outperforming uni-modal SAR architecture with 94.60% pixel-wise accuracy and an F1-score of 94.99%.

Despite significant advances in artificial intelligence and high-speed hardware, computers still do not have the same human …