Visual Analysis Method Research Articles

Inner filter effect-based near-infrared fluorescent probe for detection of metronidazole on a smartphone-integrated analytical platform.

Antibiotic residues pose a serious threat to ecosystems and food safety. Developing convenient, visual, and on-site detection methods is therefore in high demand and has a practical purpose. In this work, a near-infrared (NIR) fluorescent probe with an analysis platform based on a smartphone has been constructed for quantitative and on-site detection of metronidazole (MNZ). CdTe quantum dots with NIR emission at 710 nm (QD710) were prepared by using a simple hydrothermal method and showed good properties. A spectral overlap between absorption of MNZ and excitation of QD710 resulted in an effective inner filter effect (IFE) between QD710 and MNZ. Because of the IFE, the fluorescence of QD710 decreased gradually with increasing concentrations of MNZ. Based on the fluorescence response, quantitative detection and visualization of MNZ was achieved. NIR fluorescence analysis and the special IFE between probe and target can improve sensitivity and selectivity for MNZ. Additionally, these were also utilized for quantitative detection of MNZ in real food samples and the results were reliable and satisfactory. Meanwhile, a portable visual analysis platform in a smartphone was constructed for on-site analysis of MNZ, which can be used as an alternative method for detection of MNZ residues in situations with limited instrumental conditions. Therefore, this work provides a convenient, visual, and real-time analysis method for detection of MNZ and the analysis platform shows great potential for commercialization.

Effectiveness and profitability of automation technologies in greenhouse productivity and food security

This paper examines the innovative impact of an automated system developed by the research and production company Gardens of Chechnya, which combines computer vision technologies and image data analysis methods to effectively assess plant health at the embryonic stage. Traditional visual data analysis methods have been labour-intensive and time-consuming, creating barriers to crop production and quality. The automated system developed for the company's scientific needs, based on computer vision, has excellent accuracy, allowing it to examine plants at a new level and detect even the slightest signs of disease and infection. This innovation speeds up the assessment process, reducing it from days to hours. The mobility of the system allows it to be used in various agricultural conditions, which simplifies the assessment of plant health. By making it easier to assess plant health, this innovation promises increased yields, reduced disease spread and faster results, meeting global goals for food security and sustainable agriculture.

Seismic Facies Visualization Analysis Method of SOM Corrected by Uniform Manifold Approximation and Projection

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">As a common seismic facies visualization analysis method, SOM projects the waveform or seismic attribute vectors into a two-dimensional topological plane in a nonlinear way, which can effectively and efficiently discover the topological structure of a dataset. SOM does not need to set the number of classes in prior, and has friendly visualization characteristics and excellent generalization, which are conducive to seismic facies interpretation using unlabeled data. However, due to the competitive learning used in SOM and the imbalance of data distribution in real world, the samples from majority classes are expanded on the topological plane and the minority classes are compressed. As a result, the plane cannot accurately describe the global structure of data distribution. To improve the visualization precision by modifying the topological relationship of the prototype vectors of SOM, we utilize UMAP (Uniform Manifold Approximation and Projection), a novel manifold learning technique for dimension reduction, to correct the prototype vectors generated by SOM. By combing the advantages of SOM and UMAP in the representation of data topological structure, the global relationship between seismic data samples can be properly established, and the internal relative spatial structure of majority class samples can be retained as much as possible, resulting in a more reliable classification. Meanwhile, the framework maintains the advantages of SOM in visualization. In the modeling tests and real data experiments, we have demonstrated the effectiveness and rationality of UMAP-SOM on the spatial structure representation of three-dimensional seismic data</i> .

Visual Analytics Platform for Centralized Covid-19 Digital Contact Tracing.

The Covid-19 pandemic and its dramatic worldwide impact have required global multidisciplinary actions to mitigate its effects. Mobile phone activity-based digital contact tracing (DCT) via Bluetooth Low Energy (BLE) technology has been considered a powerful pandemic monitoring tool, yet it sparked a controversial debate about privacy risks for people. In order to explore the potential benefits of a DCT system in the context of Occupational Risk Prevention, this paper presents the potential of Visual Analytics methods to summarize and extract relevant information from complex DCT data collected during a long-term experiment at our research centre. Visual tools were combined with quantitative metrics to provide insights into contact patterns among volunteers. Results showed that crucial actors such as participants acting as bridges between groups could be easily identified - ultimately allowing for making more informed management decisions aimed at containing the potential spread of a disease.

Interactive visualization of forearm internal rotation angle in Tai Chi training

Modeling the visual features of the forearm internal rotation angle during Tai Chi training is difficult, and its accurate detection is lacking. To address this issue, this paper proposes an interactive visualization analysis method based on Harris corner detection and edge contour feature extraction to visualize and adjust the internal rotation of the forearm during Tai Chi training in real time. In the proposed method, the visual images of limb movement and forearm rotation during the Tai Chi training are collected as features, and the wavelet image denoising method is used to denoise the images to improve the signal-to-noise ratio of the output. Meanwhile, the image pixel uniform traversal method is used to continuously traverse the image subblocks to extract the edge contour features during the forearm internal rotation interaction. By means of superpixel feature decomposition and grayscale information superposition, the fitting effect of the forearm internal rotation angle is fed back, and based on this, the forearm internal rotation angle is modified to realize the interactive visual analysis of the forearm internal rotation angle. Experimental results show that, compared with existing methods, the interactive visualization performance of the proposed method is better. The proposed method can fit, track, and solve the rotation angle in real time; the required analysis training images are more accurate, and the Tai Chi training data detection and dynamic analysis abilities are improved.

ВЫБОР МЕТОДА РЕКОНСТРУКЦИИ ПОЗИТРОННО-ЭМИССИОННЫХ ЭКГ-СИНХРОНИЗОВАННЫХ ИССЛЕДОВАНИЙ И ЕГО ВЛИЯНИЕ НА РАССЧИТАННЫЕ ОБЪЁМНЫЕ ПОКАЗАТЕЛИ ФУНКЦИИ МИОКАРДА ЛЕВОГО ЖЕЛУДОЧКА И ВИЗУАЛЬНОЕ КАЧЕСТВО ИЗОБРАЖЕНИЙ

Ischemic heart disease is one of the leading causes of mortality in the Russian Federation. Timely diagnosis of this condition allows you to determine the optimal treatment tactics. Positron emission tomography with 18-fluorodeoxyglucose is a method for verifying viable cardiac tissue in patients with myocardial infarction. The methods used for the reconstruction of positron emission tomograms can affect the quantitative indicators of the contractile function of the myocardium. Materials and methods. The study retrospectively included 12 patients who underwent PET/CT with 18-fluorodeoxyglucose, the results of which were reconstructed using filtered back projection methods and iterative reconstruction, with ECG synchronization and division of ECG R-R intervals into 8, 10 and 16 frames. The obtained images were evaluated visually by two radiologists, and the quantitative parameters characterizing the contractile function of the left ventricular myocardium were compared in pairs. Results. There were no significant differences in the assessment of quantitative indicators such as end-systolic and diastolic volumes, stroke volume and ejection fraction. However, the iterative reconstruction results show less data scatter than the filtered back projection reconstruction method. In visual analysis, the best quality of reconstructions by iterative methods is noted. Conclusions. Thus, the recommended method for both visual analysis and quantification is iterative reconstruction, regardless of the number of timing windows used. A spread in the number of used reconstructed fragments of the cardiac cycle in the range of 8-16, commonly used in clinical practice, can be recommended.

ДРЕВНЯЯ МЕБЕЛЬ ВЕЛИКОГО НОВГОРОДА КАК РЕЗУЛЬТАТ РЕАТРИБУЦИИ ПАМЯТНИКОВ XVI—XVII ВВ.

Currently, in the process of researching ancient domestic furniture, a separate component of objects similar in design and decor has been identified. The distinctive features of this furniture are frames of horizontal and vertical tetrahedral pillars connected at right angles, and the space between them is filled with rectangular panels with inscribed carved circles. In museum collections, these artifacts were identified in the XVI—XVII centuries without substantiating the judgment put forward or on the basis of the accepted practice of dating according to the memorial component, which currently cannot be considered exhaustive. It coincided with the dates of the owner's life or the construction of the architectural structure where these objects were found, omitting the previous periods of existence and the circumstances of creation. The complex use of visual research and art history analysis methods, applied for the first time to study these objects, allowed to identify the characteristic features of the constructive and decorative solutions, the degree of preservation and wear, repair and restoration interventions, to consider monuments in the context of the development of Russian and European style art, to justify the possibility of their creation in an earlier period, to find dated analogues from the region architecture, medieval tombstones, the results of archaeological excavations of Veliky Novgorod, furniture art of Europe. The totality of the results obtained made it possible to change the original attribution of monuments and designate it as the work of Novgorod masters of the late XIII — early XV centuries, which is not only a special case in the practice of research, but changes the accepted scientific concept of museum collections and Russian furniture art. The proposed systematic approach proves the fallacy of the thesis about the complete loss of ancient cultural heritage, about the conservatism of Russian art, which preserved pre—Mongolian traditions in the sphere of furniture creation up to the XVI—XVII centuries.

Управление цифровой социально-политической повесткой дня в онлайн-пространстве: субъекты, технологии и результаты

The aim of the study is to identify and characterize the actors, factors, technologies and results of managing the online social and political agenda and to propose models of its management to public authorities. The model of online agenda, developed by the authors, was tested on the empirical data of the ‘Constitution’ case. The empirical basis for the study of the digital agenda (March–September 2020) was 1,150 million messages on Twitter and Facebook, the news content (5,000 publications) of the major national agencies Interfax, RIA Novosti and TASS, and the accounts of 460,000 users.&#x0D; To identify technological methods of building and managing the socio-political agenda on the Internet and assess their effectiveness, the authors used methods of network, visual and linguistic analysis (content analysis, semantic analysis of hashtags in the obtained arrays of empirical data).&#x0D; As a result, conclusions were made on three strategies to manage the online agenda: the stra­tegy of engagement (expanding the range of issues covered and equal emphasis on a variety of topics to involve more participants in the discourse); the strategy of pragmatic modelling (narrowing the range of issues covered with uneven emphasis on topics, i.e. maintaining a focus only on certain concepts of issues covered); strategy of discursive substitution (complete transformation of the digital socio-political and political ‘Constitution’ agenda and its replacement with other connected topics).&#x0D; The study revealed the intersections and divergences between the discourse shaped by civil society and the official media, and, as a result, revealed the content and vectors that shape the online socio-political agenda. Obviously, the existence of divergent vectors in the digital socio-political agenda, especially in crisis and emergency situations, prevents social and political system stabilization and civil society mobilization for constructive social practices.

TreeMerge: A Visual Comparative Analysis Method for Food Classification Tree in Pesticide Residue Maximum Limit Standards

Food classification is an important part of food safety standards. In this paper, we propose a novel visual comparative analysis method for food classification trees (FCTs) in pesticide maximum residue limit (MRL) standards, called TreeMerge, to lay the foundation for a comprehensive comparison of pesticide MRL standards. First, a union tree is constructed by combining the two FCTs to be compared. Then, sunburst with an embedded chordal graph (SECG) and overlapping circular treemap (OCT), which are two new visualization solutions designed in this paper, are used to show the similarities and differences in a union tree. SECG can express the hierarchical structure and the similarity between corresponding nodes in the union tree at the same time. OCT uses an improved nested Venn diagram (overlapping circle) to express the attribute values in each layer of the union tree and uses a circle-filling layout algorithm based on the testing circle to improve the readability and space utilization of the view. Finally, a visual analysis system for comparing FCT, named FCTvis, is designed and implemented to support the exploration of the structural difference pattern of food classification in the two MRL standards and the quantity or scale of residue limits in various foods. The effectiveness of TreeMerge was verified by case studies on pesticide MRL standards in the Chinese Mainland and Chinese Hong Kong.

Prediction of γ-ray shielding performance and study of Bi/PU coated fabric

The radiation shielding simulation model of coated fabric (flexible composite) was established for the first time by SuperMC nuclear simulation software to help solve the problems of small volume, complex structure, and difficult design of flexible shielding materials, and the γ-ray shielding performance was calculated. Bismuth/polyurethane coated fabric was prepared by a coating method, and its scanning electron microscope, γ-ray shielding performance and mechanical properties were tested. The results show that the simulation accuracy was improved due to the one-to-one correspondence between the structural parameters and performance parameters of the simulation model and the actual samples. The simulation value was in good agreement with the measured value. The shielding performance and mechanical properties of fabric composites were improved after coating. Increasing the content of bismuth and coating thickness can improve the shielding performance of the coated fabric. However, when the content of bismuth was too large, or the coating was too thick, the mechanical properties were relatively decreased. The deposition of ray energy in the material was analyzed by the visual analysis method, and the influence mechanism of process parameters on shielding performance was further revealed, which provided a new theoretical reference for the design of flexible shielding materials. A shielding material design and performance prediction method based on SuperMC is proposed, which can be used for personalized customization design and performance prediction and evaluation before use. It has practical guiding significance for producing and manufacturing flexible fabric shielding materials for protective clothing and equipment.

Early warning and diagnostic visualization of Sclerotinia infected tomato based on hyperspectral imaging

This research explored the feasibility of early warning and diagnostic visualization of Sclerotinia infected tomato by using hyperspectral imaging technology. Healthy tomato plants and tomato plants with Sclerotinia sclerotiorum were cultivated, and hyperspectral images at 400–1000 nm were collected from healthy and infected tomato leaves at 1, 3, 5, and 7 days of incubation. After preprocessing the spectra with first derivative (FD), second derivative (SD), standard normal variant (SNV), and multiplicative scatter correction (MSC) partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) were used to construct tomato sclerotinia identification model and select the best preprocessing method. On this basis, two band screening methods, competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA), were introduced to reduce data redundancy and improve the model’s prediction accuracy. The results showed that the accuracy of the validation sets and operation speed of the CARS-PLS and CARS-SVM models were 87.88% and 1.8 s, and 87.95% and 1.78 s, respectively. The experiment was based on the SNV-CARS-SVM prediction model combined with image processing, spectral extraction, and visualization analysis methods to create diagnostic visualization software, which opens a new avenue to the implementation of online monitoring and early warning system for sclerotinia infected tomato.

Visual Analysis of Multisource Heterogeneous Data Based on Improved DPCA Algorithm

This paper proposes a multiview collaborative visual analysis system of network security based on a DPCA (clustering by fast search and find of density peaks) clustering algorithm called DPCANETVis, with network security analysis requirements for multisource heterogeneous data. Firstly, the system proposes an improved DPCA clustering algorithm based on the hierarchical relationship of mail sending and receiving to achieve the purpose of accurate classification. Secondly, a three-layer visual layout is designed to display relevant information such as data hierarchy, node relationship, behavior model, and other relevant information, by mixing a variety of interactive visual analysis methods such as tree diagram, word cloud, line diagram, subject river, and parallel coordinate. Lastly, based on the exploration of events, all suspicious nodes and their abnormal behaviors can be displayed in the system. Finally, the prototype system is used to analyze the network security log data set provided by the ChinaVis 2018, and the feasibility of the multilevel interactive visual analysis method for network security is verified through many experiments and discussions.

A Prediction and Visual Analysis Method for Graduation Destination of Undergraduates Based on LambdaMART Model

Predicting graduation destination can help students determine their learning goals in advance, help faculty optimize curriculum and provide career guidance for students. In this paper, the authors first propose a prediction algorithm for graduation destination of undergraduates based on LambdaMART, called PGDU_LM, which uses Spearman correlation coefficient to analyze the correlation between subjects and graduate destinations and extract characteristic subjects, and uses LambdaMART ranking model to calculate students' propensity scores in different graduate destinations. Second, a visual analysis method for students' course grades and graduation destinations is designed to support users to analyze student data from multiple dimensions. Finally, a prediction and visual analysis system for graduation destination of undergraduates, PGDUvis, is designed and implemented. A case study and user evaluation on this system was conducted using the academic data of students from five majors who graduated from a university during 2016-2020, and the results illustrate the effectiveness of this method.

Machine learning (deep learning) and visualization assisted ferrite content prediction in austenitic stainless steel

The ferrite content of 202 austenitic stainless-steel welds was predicted based on Machine Learning (ML), and the data set was analysed by manifold learning, feature engineering, and other methods. Multiple ML methods such as K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Ensemble Learning (EL)、Multiple model Merging (MM), and Artificial Neural Network (ANN) performed algorithm model building and model tuning on the data set and evaluated the prediction performance of the model. The ML visualization and ML interpretation tools are used to demonstrate in detail the comparison of the prediction performance of ferrite number (FN) of various ML models and the effect of welding parameters on FN prediction. SVR, Gradient Boost, Xgboost, Catboost, Voting, Stacking, and MLP regression models demonstrate precision prediction and generalization. The coefficient of determination ( R 2 ) of the MLP model was 0.956 on the test set, which showed generalization and robustness. The visual data analysis and ML sample tracking methods are used to optimize the model. An ANN model with high prediction accuracy was proposed, and the R 2 of the test set was 0.972. This paper reveals the comprehensive application of ML-assisted material design and data sample visual tracking in the field of materials. • PyTorch deep learning framework to improve prediction accuracy. • Welding parameters are used to analyse welding parameters’ effect on ferrite content in welds. • 13 kinds of algorithms are selected for modelling and training. • Visualization tools explain the model prediction and feature importance. • The determination coefficient of the neural network model is 0.972.

An Interesting Small Molecule Fluorescent Probe for A Narrow Range (12.5~13) pH and DMSO Detection

Aims: The development of a sensitive and visual analytical method for detecting and monitoring the change of pH has always attracted great interest because it plays a significant role in chemical reactions, environmental monitoring, and biological systems. Methods: In this paper, a simple condensation reaction from readily available substrates 2-hydroxy-1- naphthylaldehyde and malononitrile has been carried out to produce probe L with good aggregationinduced luminescence, and its chemical structure was confirmed by 1H NMR, 13C NMR and ESI-MS. Objective: There is a conjugated system containing two electron-withdrawing cyano groups and an electron- donating hydroxyl group in probe L's molecular structure, exhibiting obvious fluorescence intensity and visible color change through intramolecular charge transfer (ICT) under different pH conditions. Results: Probe L assumes excellent fluorescence intensity in a DMF/H2O (1:9, v:v) mixed solvent system, and exhibits good reversibility in a very narrow range of pH values (pH = 12.5~13). In addition, probe L can also selectively identify solvent DMSO by the induction of fluorescence quenching. Conclusion: Probe L can be used for the analysis and visual detection to pH change in DMF/H2O system, and it may also be used as the indicator for the detection and monitoring of trace amounts of DMSO.

Bibliometric and visual analysis of transcranial direct current stimulation in the web of science database from 2000 to 2022 via CiteSpace.

This study aimed to evaluate the current research hotspots and development tendency of Transcranial Direct Current Stimulation (tDCS) in the field of neurobiology from a bibliometric perspective by providing visualized information to scientists and clinicians. Publications related to tDCS published between 2000 and 2022 were retrieved from the Web of Science Core Collection (WOSCC) on May 5, 2022. Bibliometric features including the number of publications and citations, citation frequency, H-index, journal impact factors, and journal citation reports were summarized using Microsoft Office Excel. Co-authorship, citation, co-citation, and co-occurrence analyses among countries, institutions, authors, co-authors, journals, publications, references, and keywords were analyzed and visualized using CiteSpace (version 6.1.R3). A total of 4,756 publications on tDCS fulfilled the criteria we designed and then were extracted from the WOSCC. The United States (1,190 publications, 25.02%) and Harvard University (185 publications, 3.89%) were the leading contributors among all the countries and institutions, respectively. NITSCHE MA and FREGNI F, two key researchers, have made great achievements in tDCS. Brain Stimulation (306 publications) had the highest number of publications relevant to tDCS and the highest number of citations (4,042 times). In terms of potential hotspots, we observed through reference co-citation analysis timeline viewer related to tDCS that "depression"#0, "Sensorimotor network"#10, "working memory"#11, and "Transcranial magnetic stimulation"#9 might be the future research hotspots, while keywords with the strong burst and still ongoing were "intensity" (2018-2022), "impairment" (2020-2022), "efficacy" (2020-2022), and "guideline" (2020-2022). This was the first-ever study of peer-reviewed publications relative to tDCS using several scientometric and visual analytic methods to quantitatively and qualitatively reveal the current research status and trends in the field of tDCS. Through the bibliometric method, we gained an in-depth understanding of the current research status and development trend on tDCS. Our research and analysis results might provide some practical sources for academic scholars and clinicians.

특수교육 분야 언어 네트워크 분석 연구의 동향

Objectives This study tried to explore the direction of language network analysis-related research in the field of special education in the future by analyzing the trends of studies using language network analysis methods in the field of special education in Korea.&#x0D; Methods For this purpose, 28 studies using language network analysis in the domestic special education field were analyzed from 2007 to June 2022. Based on previous research, the analysis criteria were set as ‘year of publication and published journal, research topic, analysis tool, visualization analysis method, and analysis index’.&#x0D; Results As a result of the study, it was found that the largest number of studies were conducted in ‘Special Education Rehabilitation Science Research’ and ‘Intellectual Disability Research’. was found to have been carried out. In addition, most of the research was conducted on the subject of research trend and knowledge structure analysis, and it was found that the majority of collected texts were articles published in academic journals. As a network analysis tool, ‘UCINET’ was used the most, and the most studies that visualized the results through a network map were the most. In all 28 papers to be analyzed, the centrality scale was used as an analysis index, and methods such as clustering and ego network analysis were also used.&#x0D; Conclusions These results have significance in providing a direction for conducting language network research in the field of special education in the future, and the need for continuous expansion of qualitatively more diverse topics, as well as network analysis tools, visualization presentation methods, and analysis suitable for research purposes and topics. The necessity of presenting the research results abundantly using indicators was suggested.

Determination of β-glucosidase activity using single-particle enumeration with Au@CeO2 nanoparticles.

Avisible and sensitive assay for the quantitative detection of β-glucosidase (β-glu) activity based on Au@CeO2 core-shell nanoparticles (Au@CeO2 NPs)is described. As a hydrolytic enzyme, β-glu can promote the hydrolysis of β-arbutin to hydroquinone (HQ), which can trigger the decomposition of theCeO2 shell. With the single-particle enumeration (SPE) strategy coupled withdark field optical microscopy (DFM), an obvious color alteration of single Au@CeO2 NPs during theetching process can be observed in real-time. By statistically calculating the number of the etched nanoparticles, the β-glu activity level can be quantified accurately. This assay displays a broad linear range from 0.5 to 50mU⋅mL-1 and low detection limit of 0.12mU⋅mL-1. In addition, this method was successfully used to determine β-glu in real samples and acquires satisfactory recoveries in the range of 97.1-102.0%. This study provides a visualization analysis method for β-glu, which may be helpful for monitoring other targets in the future.

Methods of visual analysis in the design of the stray light protection of optical devices

We provide an analysis of the existing methods for calculating parasitic illumination, methods for its analysis, and visualization. To search for and analyze the sources of stray light in optical devices, a software model of the beam propagation criterion in an optical device is proposed, which makes it possible to select beams that satisfy a given criterion. We consider the possibility of using the method of bidirectional progressive stochastic ray tracing with inverse photon maps to calculate the stray illumination of an image and analyze the causes of its occurrence. To analyze the sources of stray illumination on the radiation detector, it was proposed to use progressive photon maps, which calculated the caustic and secondary components of the illumination and fixed the image illumination on a static regular mesh attached to the surfaces of the optical device. Special visualization tools allow the display of this illumination over the image of an optical device and determination of the elements of the device that have the greatest impact on the level of stray illumination of the image. We present examples of calculation, analysis, and visualization of parasitic illumination of a number of optical systems of lenses with real mechanical structures.

Approach of AI-Based Automatic Climate Control in White Button Mushroom Growing Hall

Automatic climate management enables us to reduce repetitive work and share knowledge of different experts. An artificial intelligence-based layer to manage climate in white button mushroom growing hall was presented in this article. It combines visual data, climate data collected by sensors, and technologists’ actions taken to manage climate in the mushroom growing hall. The layer employs visual data analysis methods (morphological analysis, Fourier analysis, convolutional neural networks) to extract indicators, such as the percentage of mycelium coverage and number of pins of different size per area unit. These indicators are used to generate time series that represent the dynamics of the mushroom growing process. The incorporation of time synchronized indicators obtained from visual data with monitored climate indicators and technologists’ actions allows for the application of a supervised learning decision making model to automatically define necessary climate changes. Whereas managed climate parameters and visual indicators depend on the mushroom production stage, three different models were created to correspond the incubation, shock, and fruiting stage of the mushroom production process (using decision trees, K-nearest neighbors’ method). An analysis of the results showed that trends of the selected visual indicators remain similar during different cultivations. Thus, the created decision-making models allow for the definition of the majority of the cases in which the climate change or transition between the growing stages is needed.