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Classification of tomato ripeness in the agricultural industry using a computer vision system
Get PDFMachine vision systems (SVA) occupy an important place in the field of food and agriculture, these techniques are performed in situ, are efficient, non-invasive, time-saving and more economical than traditional techniques. Tomatoes (Solanum lycopersicum) are extensively cultivated throughout the world, are essential in the agricultural and culinary fields and are recognized for their beneficial contributions to health. Lack of knowledge about fruit maturity, proper harvesting and postharvest handling are factors responsible for large postharvest losses. Therefore, the objective of this research was the construction of a VAS that allows establishing relationships between color and maturity stage of the Chonto Roble F1 tomato. The VAS built is composed of hardware and software duly synchronized through the application of computer vision algorithms in Python 3.9 software that allow it to perform the acquisition and segmentation of the image and present the user with the color coordinates in the CIEL*a*b* system. Finally, color measurements were performed on tomato samples at different stages of ripening in the VAS and a HunterLab ColorQuest XE (EHL) spectrophotometer. The results obtained indicated that there are no significant differences in both measurement systems for L* values, the changes produced in b* and a* were statistically significant for tomato samples. The results obtained indicated the potential use of the constructed VAS for the determination of the degree of maturity of tomatoes in real time, in a non-invasive and low-cost way
Detection of diabetic retinopathy using artificial intelligence: an exploratory systematic review
Get PDFDiabetic retinopathy is a disease that can lead to vision loss and blindness in people with diabetes, so its early detection is important to prevent ocular complications. The aim of this study was to analyze the usefulness of artificial intelligence in the detection of diabetic retinopathy. For this purpose, an exploratory systematic review was performed, collecting 77 empirical articles from the Scopus, IEEE, ACM, SciELO and NIH databases. The results indicate that the most commonly used factors for the detection of diabetic retinopathy include changes in retinal vascularization, macular edema and microaneurysms. Among the most commonly applied algorithms for early detection are ResNet 101, CNN and IDx-DR. In addition, some artificial intelligence models are reported to have an accuracy ranging from 90% to 95%, although models with accuracies below 80% have also been identified. It is concluded that artificial intelligence, and in particular deep learning, has been shown to be effective in the early detection of diabetic retinopathy, facilitating timely treatment and improving clinical outcomes. However, ethical and legal concerns arise, such as privacy and security of patient data, liability in case of diagnostic errors, algorithmic bias, informed consent, and transparency in the use of artificial intelligence
Transforming the Salitre campus into a smart campus: proposal of smart initiatives for the Gerardo Barrios University of El Salvador
Get PDFThis study proposes a set of initiatives to transform the Salitre campus of the Gerardo Barrios University in San Miguel, El Salvador, into a Smart Campus inspired by Smart City concepts. Through a documentary research, a detailed diagnosis of the campus and a SWOT analysis, several proposals were defined grouped in six axes: Smart Government, Smart Environment, Smart Living, Smart Economy, Smart People and Smart Mobility. The viability of the initiatives was evaluated considering their acceptance by the authorities and stakeholders, as well as a feasibility study. The results show that the transformation of the Salitre campus into a Smart Campus may be possible and would bring multiple benefits in terms of efficiency, sustainability, innovation and quality of life for the university community. The study establishes the basis for future projects and research, promoting digital and sustainable transformation in the educational and community environment of El Salvador
Transforming Education in the World of Artificial Intelligence
Get PDFIntroduction: Artificial Intelligence (AI) and Machine Learning (ML) are key drivers of innovation and growth among all industries, and the education sector is no exemption. AI is considered as a powerful tool to facilitate new examples for technological development, instructional design and educational research that are otherwise not possible to develop in the traditional education techniques. With the development in information processing and computing techniques, artificial intelligence has been widely applied in educational practices (Artificial Intelligence in Education; AIEd), such as teaching robots, human-computer interactions intelligent tutoring systems, learning analytics dashboards and adaptive learning systems. It has the ability to maximize both teaching and learning, helping the education sector to evolve for better thus benefitting students and teachers bot
Optical character recognition system using artificial intelligence
Get PDFAbstract A technique termed optical character recognition, or OCR, is used to extract text from images. An OCR the system\u27s primary goal is to transform already present paper-based paperwork or picture data into usable papers. Character as well as word detection are the two main phases of an OCR, which is designed using many algorithms. An OCR also maintains a document\u27s structure by focusing on sentence identification, which is a more sophisticated approach. Research has demonstrated that despite the efforts of numerous scholars, no error-free Bengali OCR has been produced. This issue is addressed by developing an OCR for the Bengali language using the latest 3.03 version of the Tesseract OCR engine for Windows
Unveiling the Power of the Internet of Things: Exploring Services, Applications, and Overcoming Challenges
Get PDFThe Internet of Things (IoT) has transcended its futuristic perception and become an omnipresent reality. Its pervasive nature encompasses devices, sensors, clouds, big data, and business interactions. This revolutionary concept amalgamates traditional embedded systems with wireless microsensors, automation-driven control systems, and other elements to establish a vast infrastructure. The integration of wireless communication, micro electro mechanical devices, and the Internet has given rise to novel IoT applications. The IoT is essentially a network of interconnected objects accessible through the Internet, each object uniquely identifiable. The advent of IPv6, superseding IPv4, plays a pivotal role in expanding the address space for IoT development. The primary objective of IoT applications is to imbue objects with intelligence, eliminating the need for human intervention. However, the proliferation of smart nodes and the exponential data generated by each node present new challenges pertaining to data privacy, scalability, security, manageability, and other critical issues, which we delve into in this comprehensive exploratio
Benefits and challenges of artificial intelligence in the Colombian health system
Get PDFThis study explored the impact of artificial intelligence (AI) on the Colombian healthcare system, focusing on its potential to improve diagnosis, treatment, and resource management, the methodology included a literature review and case study analysis in rural and urban areas, findings revealed that AI can enhance the accuracy and speed of clinical decision-making, address the lack of specialist access in remote areas, and personalize medical treatments. However, significant challenges were also identified, such as insufficient technological infrastructure, the need for adequate health personnel training, and ethical and data protection concerns. It was concluded that to maximize the benefits of AI and minimize its risks, careful planning, adequate investments in infrastructure and continuous staff training, as well as robust ethical and legal regulation, are essential. Additionally, the importance of designing AI implementation policies that consider and address existing inequalities in access to healthcare services was emphasize
Enhancing Agricultural Resilience in Malawi: The Impact of Simple Irrigation Adoption and AI-Driven Solutions on Smallholder Farmers in Kamudidi
Get PDFAgriculture is the backbone of Malawi’s economy, yet smallholder farmers face significant challenges due to erratic rainfall, water scarcity, and inefficient irrigation practices. This study examines the impact of simple irrigation adoption on maize productivity and household income among smallholder farmers in Kamudidi, Malawi. Using Propensity Score Matching (PSM), we compare farmers who adopt simple irrigation with those who rely on traditional rain-fed agriculture. The results show that irrigation adapters produce, on average, 244.21 more kilograms of maize and experience a 6562.79 Malawian Kwacha increase in household total expenditure compared to non-adopters. These findings underscore the role of irrigation in improving food security and economic stability. Furthermore, the study explores the potential of Artificial Intelligence (AI) to optimize irrigation practices through predictive analytics, weather forecasting, and smart water management. While AI-driven solutions can enhance decision-making and resource allocation, challenges such as limited digital literacy, infrastructure constraints, and financial barriers hinder widespread adoption. The study highlights the need for targeted policies, including access to affordable credit, farmer training programs, and investment in digital infrastructure, to facilitate both irrigation and AI adoption. Overall, the research provides valuable insights into how simple irrigation and AI-driven solutions can enhance agricultural resilience. Policymakers and development agencies should prioritize interventions that improve irrigation access and integrate AI to support smallholder farmers, ultimately fostering sustainable agricultural growth and rural development in Malawi
Development of an Image Recognition System Based on Neural Networks for the Classification of Plant Species in the Amazon Rainforest, Peru, 2024
Get PDFIntroduction: The recognition and classification of plant species in the Amazon Rainforest is crucial for biodiversity conservation and ecological research. This study presents the development of an image recognition system based on neural networks for the classification of plant species in the Amazon Rainforest, Peru, 2024.Objective: Create an efficient model that can identify and classify various plant species from images, thus improving current methods of cataloging and studying Amazonian flora.Methodology: The methodology includes collecting a large dataset of plant images, followed by rigorous preprocessing to normalize and augment the data. A convolutional neural network (CNN) was designed and trained using advanced machine learning techniques, and its performance was evaluated using metrics such as precision, recall and F1-score.Results: The results show that the developed model achieves an accuracy of 92%, surpassing traditional methods and some previous models in the literature. This high precision suggests that the system can be a valuable tool for researchers and conservationists in the Amazon Rainforest.Conclusion: This study demonstrates the effectiveness of neural networks in the classification of plant species and highlights their potential to contribute significantly to the conservation and study of biodiversity in the Amazon region
The principle of transparency and Electronic Government in Cuba
Get PDFThe Electronic Government includes better ways of interaction between Government, companies and society through Information Technology and Communications. Access to visible, available and quality public information quickly and expeditiously improves the perception of the functioning of the institutions in pursuit of Cuba\u27s efforts to advance in the digital transformation. The effectiveness of the principle of transparency from the constitutional mandate obliges the Public Administration to generate updated, reliable and accessible information according to the activity carried out. It is a prerequisite for good administration that involves citizens in decision-making processes as well as balancing their position vis-à-vis the State. It promotes good governance derived from the establishment and management of effective, efficient and accountable institutions.