Proactive Measures Research Articles

The impact of artificial intelligence on regulatory compliance in the oil and gas industry

Artificial Intelligence (AI) is increasingly transforming the regulatory compliance landscape in the oil and gas industry. This paper examines the profound impact of AI on ensuring adherence to complex regulatory frameworks governing this sector. Regulatory compliance in the oil and gas industry involves adhering to a myriad of environmental, safety, and operational regulations, often posing significant challenges due to the volume and complexity of data involved. AI technologies, including machine learning, natural language processing, and predictive analytics, offer innovative solutions to these challenges. AI enhances data management and analysis by automating data collection, processing, and reporting, thereby increasing accuracy and efficiency. Predictive maintenance and risk assessment tools powered by AI can identify potential compliance issues before they arise, allowing for proactive measures. Moreover, AI-driven compliance monitoring systems enable real-time tracking of regulatory adherence, reducing the risk of non-compliance and associated penalties. Automated auditing and inspection processes further streamline compliance checks, ensuring thorough and consistent evaluations. Case studies demonstrate successful AI implementations in regulatory compliance, such as automated reporting systems in offshore drilling and predictive maintenance in pipeline management, which have resulted in improved compliance rates and reduced operational risks. However, the adoption of AI is not without challenges. Issues related to data quality and integration, cybersecurity, and regulatory acceptance pose significant hurdles. Additionally, ethical and legal considerations surrounding AI deployment must be addressed to ensure responsible use. AI holds substantial potential to revolutionize regulatory compliance in the oil and gas industry by enhancing efficiency, accuracy, and proactive risk management. As AI technologies continue to evolve, their integration into compliance processes will likely become more sophisticated, offering greater benefits and addressing current limitations. The future of regulatory compliance in the oil and gas sector will be increasingly shaped by the advancements in AI, driving both operational excellence and adherence to stringent regulatory standards.

A CAV-Lead speed advice approach considering local spatiotemporal traffic state near bottlenecks

Bottlenecks of the freeway generated especially by traffic accidents or temporary work zones contribute to significant reductions in system throughput and hinder the efficient traffic operations. It is imperative to take proactive measures to improve traffic state. With the rapid advancements in intelligent transportation, connected and autonomous vehicles (CAVs) have attracted much attention by its speculated capabilities in improving traffic safety and well-organized operational coordination. Therefore, reasonably utilizing the advantages of CAVs is possible to reduce the impact induced by bottlenecks. In this research, we propose a novel algorithm called CAV-Lead to obtain the CAV’s regulated speed under mixed CAVs and human-driven vehicles (HVs) environment to improve the overall utilization of the freeway capacity near bottlenecks. Firstly, we illustrate the basic principle of the CAV-Lead algorithm that takes both microscopic and macroscopic traffic characteristics into account. Then, based on the local spatiotemporal traffic state, the CAV-Lead algorithm is proposed to determine each CAV’s speed under mixed flow. Furthermore, a real-time simulation control framework considering the random behavior of HVs is presented. Moreover, several simulation evaluations including comparisons with basic scenarios and similar research are conducted under various CAV market penetration rates (MPRs). The results demonstrate that the CAV-Lead could improve the traffic performance, especially for the high traffic demand with certain MPRs.

Early Detection For Better Results In The Treatment Of Asymptomatic Cases Of Valvular Heart Disease

Valvular heart disease (VHD) often progresses asymptomatically, leading to delayed diagnosis and treatment, which worsens patient outcomes. Early detection of VHD in asymptomatic cases is crucial to improve treatment effectiveness and long-term prognosis. This study aims to investigate the impact of early detection on treatment outcomes in asymptomatic VHD patients. A cross-sectional study was conducted involving 200 patients diagnosed with VHD, categorized into two groups: those diagnosed early through screening and those diagnosed later after the onset of symptoms. Data were collected through medical records, echocardiograms, and patient interviews. Statistical analysis revealed that early detection significantly improved patient outcomes, with lower rates of complications, better valve repair success, and reduced mortality compared to those diagnosed later. Discussion centers on the importance of routine screenings in at-risk populations and the need for better diagnostic protocols to catch asymptomatic cases early. The conclusion highlights that early detection of asymptomatic VHD leads to better treatment outcomes, underlining the necessity of proactive screening measures to manage VHD effectively.

Influence of Professional Learning Communities (PLCs) on Teacher Professional Development (TPD) in Basic Schools in the Tamale Metropolis

Purpose: This study aimed to examine the influence of professional learning communities (PLCs) on teacher professional development in basic schools in the Tamale Metropolis. Methodology: The study employed a qualitative research approach. A case study design was employed, and 20 teachers, head teachers, and school improvement support officers (SISO), were interviewed. This study used a self-constructed semi-structured interview (SSI) guide to collect data. Thematic analysis was used to analyze the data. Findings: The study revealed that professional learning communities significantly enhance skills, knowledge, and collaborative learning in teachers’ professional development. Unique contribution to theory, practice, and policy: The study recommends proactive measures, urging educational authorities, particularly the Ghana Education Service, to enhance awareness and understanding of the need for organizing professional learning communities among basic school teachers in the Tamale metropolis.

Assessment of the Transportation and Mobility Impacts of Santa Rosa Bridge Rehabilitation on Student Commuters

This study assessed the transportation and mobility impacts of the Santa Rosa Bridge rehabilitation on student commuters, focusing on how this infrastructure project affects their daily travel experiences. The study employs a descriptive-comparative methodology, gathering data from 117 student commuters through a researcher-made questionnaire distributed online. Results reveal a significant increase in travel time, with more students experiencing delays of 1-2 hours or more. Additionally, the rehabilitation has led to decreased satisfaction with available transportation options and overall commuting experiences, as evidenced by a decline in weighted mean scores across all transportation and mobility constructs. The study concludes that the rehabilitation project has adversely affected student commuters, disrupting their routines and reducing the reliability of their commutes. These findings highlights the need for proactive measures, such as alternative transportation options or temporary shuttle services, to mitigate the impact on student mobility during infrastructure projects.

The reporting quality of meta-epidemiological studies needs substantial improvement: a research on research study

BackgroundMeta-epidemiological research plays a vital role in providing empirical evidence needed to develop methodological manuals and tools, but the reporting quality has not been comprehensively assessed, and the influence of reporting guidelines remains unclear. The current study aims to evaluate the reporting quality of meta-epidemiological studies, assess the impact of reporting guidelines, and identify factors influencing reporting quality.MethodsWe searched PubMed and Embase for meta-epidemiological studies. The reporting quality of these studies was assessed for adherence to established reporting guidelines. Two researchers independently screened the studies and assessed the quality of the included studies. Time-series segmented linear regression was used to evaluate changes in reporting quality over time, while beta-regression analysis was performed to identify factors significantly associated with reporting quality.ResultsWe initially identified 1720 articles, of which 125 meta-epidemiological studies met the inclusion criteria. Of these, 65 (52%) had low reporting quality, 60 (48%) had moderate quality, and none achieved high quality. Of the 24 items derived from established reporting guidelines, 4 had poor adherence, 13 had moderate adherence, and 7 had high adherences. High journal impact factor (≥ 10) (OR = 1.42, 95% CI: 1.13, 1.80; P = 0.003) and protocol registration (OR = 1.70, 95% CI: 1.30, 2.22; P < 0.001) were significantly associated with better reporting quality. The publication of the reporting guideline did not significantly increase the mean reporting quality score (− 0.53, 95% CI: − 3.37, 2.31; P = 0.67) or the trend (− 0.38, 95% CI: − 1.02, 0.26; P = 0.20).ConclusionsOur analysis showed suboptimal reporting quality in meta-epidemiological studies, with no improvement post-2017 guidelines. This potential shortcoming could hinder stakeholders’ ability to draw reliable conclusions from these studies. While preregistration could reduce reporting bias, its adoption remains low. Registration platforms could consider creating tailored types for meta-epidemiological research, and journals need to adopt more proactive measures to enforce reporting standards.

A patient report scale research to access the symptom burden in patients with IgA nephropathy

Patients diagnosed with IgA nephropathy (IgAN) commonly experience a substantial burden of symptoms encompassing both physical and psychological aspects. Presently, there's a dearth of standardized assessment tools to effectively gauge the extent of symptom burden in IgAN patients. Therefore, this study aims to devise an IgAN Symptom Assessment Tool that enables a comprehensive evaluation of patient symptom burden and their self-perceived severity. Employing a prospective observational design, this study conducted a survey among patients diagnosed with IgAN at a hospital in China. The research team formulated an IgAN Symptom Burden Assessment Scale and administered a questionnaire to gauge patient symptom burden. Severity assessment was conducted on a 5-point Likert scale, with higher scores indicating a more pronounced burden of symptoms. The finalized scale comprised 14 distinct symptom items, and the questionnaire survey garnered responses from 200 patients, achieving a 100% response rate. Statistical analysis unveiled that nearly all patients regarded these symptoms as prevalent and significantly impactful on their daily lives, resulting in a considerable burden. Notably, mild oliguria, moderate nasal congestion, bitter taste , throat discomfort, alongside severe manifestations such as muscle weakness, fatigue, and foamy urine, were frequently reported by patients. The findings underscore that a substantial proportion of IgAN patients grapple with a significant burden of symptoms, emphasizing the imperative for healthcare providers to prioritize symptom management and implement proactive measures to alleviate these challenges. This study presents an innovative tool tailored for evaluating symptom burden specifically in IgAN patients. Subsequent research should center on validating this tool within larger patient cohorts to optimize the efficacy of symptom management in this demographic.

Using Geovisualizations to Educate the Public About Environmental Health Hazards: What Works and Why.

Informing the public about environmental risks to health is crucial for raising awareness around hazards, and promoting actions that minimize exposures. Geographic visualizations-geovisualizations-have become an increasingly common way to disseminate web-based information about environmental hazards, displaying spatial variations in exposures and health outcomes using a map. Unfortunately, ineffective geovisualizations can result in inaccurate inferences about a hazard, leading to misguided actions or policies. In this narrative review, we discuss key considerations for the use of geovisualizations to promote environmental health literacy. Many conventional geovisualizations used for hazard education and risk communication fail to consider how people process visual information. Design choices that prompt viewers to think and feel, leveraging processes such as individual attention, memory, and emotion, could promote improved comprehension and decision making around environmental health risks using geovisualizations. Based on the studies reviewed, we recommend six strategies for designing effective, evidence-based geovisualizations, synthesizing evidence from the cognitive sciences, cartography, and environmental health. These strategies include: Displaying only key data, tailoring and testing geovisualizations with the desired audience, using salient cues, leveraging emotion, aiding pattern recognition, and limiting visual distractions. Geovisualizations offer a promising avenue for advancing public awareness and fostering proactive measures in addressing complex environmental health challenges. This review highlights how incorporating evidence-based design principles into geovisualizations could promote environmental health literacy. More experimental research evaluating geovisualizations, using interdisciplinary approaches, is needed.

A study on ecological sustainable cities based on LCA-emergy-carbon footprint and geographic information system (GIS) approach

ABSTRACT Sustainable cities, as an effective response to global climate aberrations, have increasingly garnered the attention of researchers. However, due to the wide range of interpretations and applications associated with sustainable cities, diverse methodologies can yield starkly contrasting results. This paper adopts ecological and low-carbon perspectives to evaluate and discuss urban sustainability. The core of this investigation involves quantitatively measuring a city’s sustainability based on the lifecycle metrics of emergy and carbon footprint. The findings reveal that Yangzhou, recognized as a model ecological city by authorities, exhibits an emergy sustainability index below the standard benchmark (ESI = 0.02 < 1). GIS mapping reveals that the city’s largest ecological service system is situated in the northwest water region. Both the emergy and carbon footprint analyses indicate that urbanized areas exert a detrimental influence on the city’s overall sustainability. Additionally, the administrative authorities in Yangzhou have implemented proactive measures to transition the city towards sustainability. Currently, the impacts of clean energy adoption and urban wetland restoration are particularly pronounced. The application of lifecycle emergy and carbon footprint methods offers a holistic approach for urban managers, enabling sustainable urban planning that harmoniously integrates ecological emergy and low-carbon considerations. This approach is instrumental in the realization of sustainable cities.

Deep learning for blood glucose level prediction: How well do models generalize across different data sets?

Deep learning-based models for predicting blood glucose levels in diabetic patients can facilitate proactive measures to prevent critical events and are essential for closed-loop control therapy systems. However, selecting appropriate models from the literature may not always yield conclusive results, as the choice could be influenced by biases or misleading evaluations stemming from different methodologies, datasets, and preprocessing techniques. This study aims to compare and comprehensively analyze the performance of various deep learning models across diverse datasets to assess their applicability and generalizability across a broader spectrum of scenarios. Commonly used deep learning models for blood glucose level forecasting, such as feed-forward neural network, convolutional neural network, long short-term memory network (LSTM), temporal convolutional neural network, and self-attention network (SAN), are considered in this study. To evaluate the generalization capabilities of each model, four datasets of varying sizes, encompassing samples from different age groups and conditions, are utilized. Performance metrics include Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), and Coefficient of Determination (CoD) for analytical asssessment, Clarke Error Grid (CEG) for clinical assessments, Kolmogorov-Smirnov (KS) test for statistical analysis, and generalization ability evaluations to obtain both coarse and granular insights. The experimental findings indicate that the LSTM model demonstrates superior performance with the lowest root mean square error and highest generalization capability among all other models, closely followed by SAN. The ability of LSTM and SAN to capture long-term dependencies in blood glucose data and their correlations with various influencing factors and events contribute to their enhanced performance. Despite the lower predictive performance, the FFN was able to capture patterns and trends in the data, suggesting its applicability in forecasting future direction. Moreover, this study helps in identifying the optimal model based on specific objectives, whether prioritizing generalization or accuracy.

The Convergence of Artificial Intelligence and Privacy: Navigating Innovation with Ethical Considerations

This article explores the complex relationship between artificial intelligence (AI) and privacy. While acknowledging AI's potential benefits, the authors emphasize the ethical implications of its data-driven nature. The article begins by outlining the privacy risks inherent in AI systems, including data breaches, surveillance, and the potential for bias and discrimination. It then delves into ethical considerations surrounding AI development, such as transparency, accountability, and the need to prioritize human values. Various frameworks for balancing innovation with privacy protection are discussed, including Privacy by Design principles and the General Data Protection Regulation (GDPR). It also examine case studies of privacy violations in AI systems, highlighting the real-world consequences of inadequate safeguards. Looking towards the future, the article identifies advancements in privacy-preserving AI technologies as a crucial area of research. It concludes by advocating for a comprehensive approach to AI governance that combines technological innovation with ethical and regulatory strategies, by stressing the importance of proactive measures to mitigate privacy risks and ensure that AI technologies are developed and deployed in a manner that respects.

Enhancing supply chain resilience through risk mitigation strategies: Evidence from smallholder red chili supply chains in East Java Indonesia

The cultivation of red chili in East Java, Indonesia, has significant economic and social impacts, necessitating proactive supply chain measures. This research aimed to identify priority risk agents, develop effective risk mitigation, and enhance supply chain resilience using the SCOR model, House of Risk, Interpretative Structural Modelling (ISM), and synthesis analysis. Examining 238 respondents—including farmers, collectors, wholesalers, retailers, home-agroindustries, and experts—the findings highlight farmers’ critical role in supply chain resilience despite risks from crop failures, weather fluctuations, and pest infestations. Simultaneous planting led to market oversupply and price drops, but accurate pricing information facilitated quick market adaptation. Wholesalers influenced pricing dynamics and income levels, impacting farmers directly. To improve resilience, three main strategies were developed through ten key elements: proactive strategies (real-time SCM tracking, Weather Early Warning Systems, risk management team formation, and training), resistance strategies (partnerships, chili stock reserves, storage and drying technologies, GAP implementation, post-harvest management, agricultural insurance, and Fair Profit Sharing Agreements), and recovery and growth strategies (flexible distribution channels and customizable distribution centers). Furthermore, the study delves into the mediating and moderating effects between variables within the model. This research not only addresses a knowledge gap but also provides stakeholders with evidence to consider new strategies to enhance red chili supply resilience.

Application of machine learning algorithms in predicting carotid artery plaques using routine health assessments.

Cardiovascular diseases (CVD) constitute a grave global health challenge, engendering significant socio-economic repercussions. Carotid artery plaques (CAP) are critical determinants of CVD risk, and proactive screening can substantially mitigate the frequency of cardiovascular incidents. However, the unequal distribution of medical resources precludes many patients from accessing carotid ultrasound diagnostics. Machine learning (ML) offers an effective screening alternative, delivering accurate predictions without the need for advanced diagnostic equipment. This study aimed to construct ML models that utilize routine health assessments and blood biomarkers to forecast the onset of CAP. In this study, seven ML models, including LightGBM, LR, multi-layer perceptron (MLP), NBM, RF, SVM, and XGBoost, were used to construct the prediction model, and their performance in predicting the risk of CAP was compared. Data on health checkups and biochemical indicators were collected from 19,751 participants at the Beijing MJ Health Screening Center for model training and validation. Of these, 6,381 were diagnosed with CAP using carotid ultrasonography. In this study, 21 indicators were selected. The performance of the models was evaluated using the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and area under the curve (AUC) value. Among the seven ML models, the light gradient boosting machine (LightGBM) had the highest AUC value (85.4%). Moreover, age, systolic blood pressure (SBP), gender, low-density lipoprotein cholesterol (LDL-C), and total cholesterol (CHOL) were the top five predictors of carotid plaque formation. This study demonstrated the feasibility of predicting carotid plaque risk using ML algorithms. ML offers effective tools for improving public health monitoring and risk assessment, with the potential to improve primary care and community health by identifying high-risk individuals and enabling proactive healthcare measures and resource optimization.

Developing resilience strategies amid supply chain risks in the automotive industry: A stakeholder theory perspective

AbstractManaging automotive supply chain risks contributes to achieving sustainability goals, including ensuring business continuity, maintaining production and delivery schedules, and reducing environmental impact. Therefore, this study aims to investigate the risks confronting the supply chain in the automotive industry through the moderating effect of reactive and proactive resilience strategies. The stakeholder theory perspective was used in this study to examine supply chain risk in the automotive industry. A quantitative method was employed, resulting in 157 valid responses. The results reveal that demand risk had the most significant effect on auto dealer performance, followed by logistics/transportation, production, and supply risk. Notably, proactive resilience strategies were found to significantly impact the relationship between risk and performance. The findings underscore the importance of proactive measures in mitigating the performance impacts of supply chain risk. This study contributes a theoretical framework by identifying four major supply chain risks and seven proactive and 12 reactive strategies. In addition, the study was conducted through the lens of stakeholder theory, emphasizing the importance of suppliers, manufacturers, and dealers cooperating to establish a reliable supply chain.

From Deception to Redemption: Analyzing Volkswagen’s Reputational Journey Post-Dieselgate

The Volkswagen emissions scandal, or "Dieselgate," inflicted a severe blow to the company's reputation and financial standing. This research paper explores Volkswagen's journey from deception to redemption, analyzing its strategies to rebuild trust and restore its brand image. The study employs a mixed-method approach, incorporating qualitative analysis of media coverage and corporate communications alongside a quantitative assessment of stock prices and consumer sentiment data. Findings reveal a complex reputational trajectory, starting with initial denial, progressing to a reluctant acceptance of responsibility, and culminating in proactive measures to regain stakeholder confidence. While Volkswagen has made notable strides in its recovery, the lingering impact of the scandal underscores the formidable challenge of repairing a tarnished reputation. In conclusion, this research provides valuable insights into crisis management, corporate social responsibility, and the dynamics of reputational recovery in the digital age, highlighting the importance of swift action, transparency, and a genuine commitment to change.

The Contribution of Dairy Bedding and Silage to the Dissemination of Genes Coding for Antimicrobial Resistance: A Narrative Review.

Antimicrobial resistance (AMR) is a concern in the dairy industry. Recent studies have indicated that bedding serves as a reservoir for antimicrobial-resistant bacteria and antimicrobial-resistance genes (ARGs), while silage has been proposed as another possible source. The impact of AMR in dairy farming can be significant, resulting in decreased productivity and economic losses for farmers. Several studies have highlighted the safety implications of AMR bacteria and genes in bedding and silage, emphasizing the need for further research on how housing, bedding, and silage management affect AMR in farm environments. Exposure to sub-lethal concentrations of antibiotics, such as those from contaminated bedding and silage, can prompt bacteria to develop resistance mechanisms. Thus, even if antimicrobial usage is diminished, ARGs may be maintained in the dairy farm environment. By implementing proactive measures to tackle AMR in dairy farming, we can take steps to preserve the health and productivity of dairy cattle while also protecting public health. This involves addressing the prudent use of antibiotics during production and promoting animal welfare, hygiene, and management practices in bedding and farm environments to minimize the risk of AMR development and spread. This narrative review compiles the growing research, positioning the contribution of bedding and silage to the prevalence and dissemination of AMR, which can elicit insights for researchers and policymakers.

INFORMATION SECURITY AND THE DEVELOPMENT OF EDUCATIONAL ENVIRONMENT OF THE 21ST CENTURY

In the 21st century, information security has emerged as a critical concern for educational environments. This paper delves into the definition and scope of information security, highlighting its key principles and best practices. It underscores the relevance of robust information security measures for educational institutions, which face unique threats such as cybersecurity risks, data privacy issues, and security breaches. By examining real-world examples, the paper illustrates these threats and their potential impact. Strategies for enhancing information security in educational settings are discussed, including implementing advanced cybersecurity protocols, the importance of training and awareness programs, and the benefits of collaboration with industry experts and government agencies. The paper also explores the positive impact of information security on educational development, such as creating a safe learning environment, fostering innovation and technology integration, and maintaining the confidentiality and integrity of educational data. The conclusion emphasizes the critical need for proactive information security measures in educational institutions and considers future advancements and strategies to address emerging threats.

Mercury Screening for At-Risk Populations

This article discusses the risks associated with mercury exposure, particularly focusing on at-risk populations in Canada, such as those living in remote areas, individuals living near toxic spills, and those who rely on fish consumption year-round. It underlines the detrimental health effects of mercury exposure, including liver and kidney damage, neurological changes, and developmental issues. The lack of awareness and screening for elevated mercury levels is identified as a significant issue along with the need for greater education regarding possible exposures to mercury. A screening pathway is proposed to aid primary care providers in identifying individuals at risk of elevated mercury levels, with the aim of preventing long-term health complications. Overall, the paper highlights the importance of proactive measures to mitigate mercury exposure and safeguard public health.

Eating disorder risk among Australian youth starting a diet in the community

Dieting is a potent risk factor for eating disorder (ED) symptoms and development, which typically occur in late adolescence. However, as diets are often motivated by body image concerns (another core ED risk factor), dieters may already carry heightened ED risk. Thus, the current study aimed to document ED risk among young people starting a diet in the community. Young people (16–25 years) starting or intending to start a self-initiated diet (N = 727) provided data via a screener questionnaire, assessing containing sociodemographic factors, past and current ED symptoms and behaviours. Over a third (36.9%) screened using a validated instrument were found to be at-risk of a current ED, with 10% above the clinical cut-off. Consistent with this finding, over 10% of the sample self-reported experiencing a lifetime ED, while nearly a quarter reported symptoms consistent with an ED diagnosis with no reported formal diagnosis. Findings suggest a high level of ED risk among young people starting a diet in the community and point to the need for more proactive measures targeted at this cohort (e.g., screening, monitoring). Further education on the risks of dieting and encouragement for help-seeking in young people is indicated.

A crop disease severity index derived from transfer learning and feature fusion using enhanced OPTICS algorithm

The adoption of automated methods for the identification and assessment of tomato-related disorders is highly sought-after in the agriculture sector. Using this technology is crucial for reducing wasteful spending, increasing the efficiency of treatments, and ultimately growing more resilient crops by reducing losses in agricultural output and maximising the effectiveness of these processes. An automated method has been suggested for accurately identifying and classifying diseases using a single photograph. The described method for disease detection in tomato plants makes use of a computer vision-based technique. Image processing, ML, and deep learning are just a few of the methods that this strategy uses. The goal of this approach is to prevent tomato crops from being damaged by various illnesses by reducing the need of conventional procedures. Bacterial spot, early blight, late blight, leaf mould, spider mites, target spot, spotted spider mite, mosaic virus, and yellow leaf curl are all examples of these illnesses. The following ten diseases frequently strike tomato crops in India. By utilising picture segmentation in combination with the Enhanced OPTICS algorithm (EOPTICSA), the affected area of the tomato plant may be precisely detected and defined after image pre-processing procedures have been used. It may be necessary to look for certain visual signs in order to diagnose the previously mentioned illnesses. The primary goal of this study was to evaluate the efficacy of the EOPTICSA method for detecting diseases in plant leaves. To eliminate the geometric features associated with colour, texture, and leaf arrangement in the provided plant pictures, image segmentation and edge detection methods are employed. Using these methods allows us to achieve our goal. Various efficacy measures are used to assess and provide a technique recommendation. This research shows that when performance metrics are used to implement these strategies, the suggested strategy outperforms the current methods in terms of accuracy, precision, and F1-score. The process of detecting sickness involves several consecutive steps. Capturing images, segmenting them, detecting edges, and determining the infection’s severity are all steps in this process. To accomplish the goal of recognising and categorising different types of diseases that might impact tomato plants, the method of transfer learning is employed. As soon as the problem is identified, it is recommended to take proactive measures to help individuals and organisations involved in agriculture address the effects of these disorders using appropriate measures.