Despite the great possibilities of modern neural network architectures concerning the problems of object detection and recognition, the output of such models is the local (pixel) coordinates of objects bounding boxes in the image and their predicted classes. However, in several practical tasks, it is necessary to obtain more complete information about the object from the image. In particular, for robotic apple picking, it is necessary to clearly understand where and how much to move the grabber. To determine the real position of the apple relative to the source of image registration, it is proposed to use the Intel Real Sense depth camera and aggregate information from its depth and brightness channels. The apples detection is carried out using the YOLOv3 architecture; then, based on the distance to the object and its localization in the image, the relative distances are calculated for all coordinates. In this case, to determine the coordinates of apples, a transition to a symmetric coordinate system takes place by means of simple linear transformations. Estimating the position in a symmetric coordinate system allows estimating not only the magnitude of the shift but also the location of the object relative to the camera. The proposed approach makes it possible to obtain position estimates with high accuracy. The approximate root mean square error is 7–12 mm, depending on the range and axis. As for precision and recall metrics, the first is 100% and the second is 90%.

The article studies the performance of a trained neural network SSD_MobileNet_V2_COCO. It is proposed to use the OpenVINO Toolkit to increase network performance. Performance evaluation is calculated by the reciprocal of the frame processing time, which characterizes the number of frames processed per second. Dataset COCO (by Microsoft) was used as the source dataset. In this case, 200 images were selected from this dataset, and during processing all images were reduced to the same sizes 300x300. Studies shown that the use of OpenVINO has increased the performance of the neural network SSD_MobileNet_V2_COCO by 130 times on average. At the same time, in contrast to starting a network using TensorFlow only, the variance of network performance using OpenVINO is significantly increased. However, the use of such an accelerator remains appropriate on Intel processors.

Problems of synthesis of doubly stochastic autoregressive models are considered that can be used for the formation of images close to real signals in their properties. A mechanism is proposed for the...

Modern deep learning systems make it possible to develop increasingly intelligent solutions in various fields of science and technology. The electronics of single board computers facilitate the control of various robotic solutions. At the same time, the implementation of such tasks does not require a large amount of resources. However, deep learning models still require a high level of computing power. Thus, the effective control of an intelligent robot manipulator is possible when a computationally complex deep learning model on GPU graphics devices and a mechanics control unit on a single-board computer work together. In this regard, the study is devoted to the development of a computer vision model for estimation of the coordinates of objects of interest, as well as the subsequent recalculation of coordinates relative to the control of the manipulator to form a control action. In addition, in the simulation environment, a reinforcement learning model was developed to determine the optimal path for picking apples from 2D images. The detection efficiency on the test images was 92%, and in the laboratory it was possible to achieve 100% detection of apples. In addition, an algorithm has been trained that provides adequate guidance to apples located at a distance of 1 m along the Z axis. Thus, the original neural network used to recognize apples was trained using a big image dataset, algorithms for estimating the coordinates of apples were developed and investigated, and the use of reinforcement learning was suggested to optimize the picking policy.

Recognizing and classifying localized lesions on apple fruit surfaces during automated sorting is critical for improving product quality and increasing the sustainability of fruit production. This study is aimed at developing sustainable methods for fruit sorting by applying hyperspectral analysis and machine learning to improve product quality and reduce losses. The employed hyperspectral technologies and machine learning algorithms enable the rapid and accurate detection of defects on the surface of fruits, enhancing product quality and reducing the number of rejects, thereby contributing to the sustainability of agriculture. This study seeks to advance commercial fruit quality control by comparing hyperspectral image classification algorithms to detect apple lesions caused by pathogens, including sunburn, scab, and rot, on three apple varieties: Honeycrisp, Gala, and Jonagold. The lesions were confirmed independently using expert judgment, real-time PCR, and 3D fluorimetry, providing a high accuracy of ground truth data and allowing conclusions to be drawn on ways to improve the sustainability and safety of the agrocenosis in which the fruits are grown. Hyperspectral imaging combined with mathematical analysis revealed that Venturia inaequalis is the main pathogen responsible for scab, while Botrytis cinerea and Penicillium expansum are the main causes of rot. This comparative study is important because it provides a detailed analysis of the performance of both supervised and unsupervised classification methods for hyperspectral imagery, which is essential for the development of reliable automated grading systems. Support Vector Machines (SVM) proved to be the most accurate, with the highest average adjusted Rand Index (ARI) scores for sunscald (0.789), scab (0.818), and rot (0.854), making it the preferred approach for classifying apple lesions during grading. K-Means performed well for scab (0.786) and rot (0.84) classes, but showed limitations with lower metrics for other lesion types. A design and technological scheme of an optical system for identifying micro- and macro-damage to fruit tissues is proposed, and the dependence of the percentage of apple damage on the rotation frequency of the sorting line rollers is obtained. The optimal values for the rotation frequency of the rollers, at which the damage to apples is less than 5%, are up to 6 Hz. The results of this study confirm the high potential of hyperspectral data for the non-invasive recognition and classification of apple diseases in automated sorting systems with an accuracy comparable to that of human experts. These results provide valuable insights into the optimization of machine learning algorithms for agricultural applications, contributing to the development of more efficient and accurate fruit quality control systems, improved production sustainability, and the long-term storage of fruits.

The text considers methods for estimating the parameters of autoregressive models of images. Special attention is given to estimating the internal autoregressions and its parameters in doubly...