Coin grading is a crucial process in numismatics that determines a coin’s value on the basis of its condition. However, the traditional grading procedure is performed by human which makes it subjective and time-consuming, with different experts potentially assigning different grades to the same coin. This paper introduces a Deep Learning (DL) model for the type-invariant automatic coin grading across the full 70-points Sheldon scale. We propose a Siamese neural network based on EfficientNet encoders for dual-sided coin evaluation, which performs the analysis of both the obverse and reverse images of coins. The model is trained and evaluated on a comprehensive dataset containing 36,205 unique professionally graded samples covering all 30 grades from Poor (P)1 to Mint State (MS)70 across the multiple coin types. The proposed DL solution achieves 0.3403 accuracy and 1.52 Mean Absolute Error (MAE) along the Sheldon scale, outperforming both the Machine Learning (ML) baseline and the state-of-the-art approaches. Unlike earlier studies that were limited to fewer coin types or reduced grading scales, our work addresses the full Sheldon scale grading across the multiple coin types, providing a more robust and generalizable solution for automatic coin grading.

At present, there is a significant focus on improving agricultural productivity, which is essential both on a national and global scale. There are many factors that impact the quality and quantity of crop yields, and environmental changes such as weather conditions require careful consideration and quick action to avoid significant losses. When it comes to managing fruit orchards, large areas need to be monitored, and unmanned aerial vehicles (UAVs) are currently being used to automate and enhance the process. UAVs can capture images that can be analyzed using neural-based methods to extract useful information about the state of the trees in the orchard. However, there are some challenges that must be addressed, such as the scarcity of open-access labeled datasets and the imbalanced distribution of target classes, which include rare events or anomalous vegetation states. To tackle these issues, in this paper, a unique dataset of apple trees observations captured by UAVs has been collected and shared to the research community. This dataset includes healthy and unhealthy trees with formed and unformed crowns. Experiments were conducted using YOLOv5 neural network for object detection to evaluate its effectiveness in solving agricultural remote sensing tasks. To adjust model’s performance, we proposed a task-specific transfer learning approach that involves pre-training the model on a synthetic dataset. The synthetic dataset was generated using object-based augmentation (OBA) with the original target objects. Thereby, instead of utilizing pre-trained weights from the general domain such as COCO dataset, we use not only domain-specific UAV-derived data for pre-training but the images for the same task of apple tree health examining. The proposed approach allowed us to increase the mAP from 0.642 to 0.706 compared with the conventional approach.

A number of polyolefin plastics including Polypropylene (PP), Polyethylene (PE), Polystyrene (PS) are widespread plastics worldwide. At the same time, they are frequently recycled plastics. Still a considerable amount of waste plastics come in a mixed mode imposing technological challenges for recycling. Households cannot provide thorough separation on the early stage, especially if the plastic products are not marked properly. In this paper, we report on the analysis of two pair of plastic shavers of the similar color and shape while made of different plastics. For this reason an experimental testbed based on a Raman Spectrometer with an embedded microscope has been designed. The proposed solution enables the identification of PP and PS by the sensor system with the singular spectral range 1320 nm - 1740 nm. There is an opportunity to measure three spectral ranges simultaneously.