Ferramentas Tecnológicas Baseadas em Inteligência Artificial na Indústria Açucareira: Uma Análise Bibliométrica e Perspectivas Futuras para Eficiência Energética

Introduction: The application of Artificial Intelligence –AI– in industrial sugar production, particularly in sensor data and systems management, is rapidly evolving towards real-time monitoring programs that offer valuable recommendations and decision-making support within the sugar industry. Methodology: This comprehensive bibliometric analysis of 125 Scopus-indexed articles highlights significant trends in the field, including surges in article production during 2017, 2018, 2021, and 2022, accounting for 34% of total publications. Results: Scientific production in this domain grew by 3.93% from 1969 to 2023. Most research (81%) originated from key countries, including Australia, Brazil, India, China, the Philippines, the United States, and France. Prominent journals played a pivotal role, representing 19% of publications. Noteworthy authors include Attard, Everingham, Meng, and Sexton, with four published articles each. Remarkably, 88% of researchers in this field are transitory. This study underscores dynamic growth in artificial intelligence applications in sugar production, emphasizing sustainability in data and systems management. Conclusions: The effective integration of these technologies holds the potential to enhance sustainability practices, optimizing efficiency and quality throughout the sugar production supply chain, thereby contributing to the attainment of Sustainable Development Goal 9. The utilization of artificial intelligence to optimize industrial sugar production represents technological innovation capable of improving the efficiency and infrastructure of the sugar industry, consequently fostering global sustainable development.Introducción: La aplicación de la Inteligencia Artificial –IA– en la producción industrial de azúcar, particularmente en la gestión de sistemas y datos de sensores, está evolucionando rápidamente hacia programas de monitoreo en tiempo real que ofrecen valiosas recomendaciones y apoyo a la toma de decisiones dentro de la industria azucarera. Metodología: Este análisis bibliométrico integral de 125 artículos indexados en Scopus destaca tendencias significativas en el campo, incluidos aumentos repentinos en la producción de artículos durante 2017, 2018, 2021 y 2022, que representan el 34% del total de publicaciones. Resultados: La producción científica en este ámbito creció un 3.93% entre 1969 y 2023. La mayor parte de la investigación (81%) se originó en países clave, incluidos Australia, Brasil, India, China, Filipinas, Estados Unidos y Francia. Las revistas destacadas desempeñaron un papel fundamental, representando el 19% de las publicaciones. Entre los autores destacables se encuentran Attard, Everingham, Meng y Sexton, con cuatro artículos publicados cada uno. Cabe destacar que el 88% de los investigadores en este campo son transitorios. Este estudio subraya el crecimiento dinámico de las aplicaciones de inteligencia artificial en la producción de azúcar, enfatizando la sostenibilidad en la gestión de datos y sistemas. Conclusiones: La integración efectiva de estas tecnologías puede mejorar las prácticas de sostenibilidad, optimizando la eficiencia y la calidad en toda la cadena de suministro de la producción de azúcar, contribuyendo al logro del Objetivo de Desarrollo Sostenible 9. Esto se debe a que el uso de inteligencia artificial para optimizar la producción industrial de azúcar representa una innovación tecnológica que puede mejorar la eficiencia y la infraestructura de la industria azucarera. Esto, a su vez, puede contribuir a lograr el desarrollo sostenible a escala global.Introdução: A aplicação da Inteligência Artificial –IA– na produção industrial de açúcar, particularmente em dados de sensores e gestão de sistemas, está a evoluir rapidamente para programas de monitorização em tempo real que oferecem recomendações valiosas e apoio à tomada de decisões na indústria açucareira. Metodologia: Esta análise bibliométrica abrangente de 125 artigos indexados pela Scopus destaca tendências significativas na área, incluindo aumentos na produção de artigos durante 2017, 2018, 2021 e 2022, representando 34% do total de publicações. Resultados: A produção científica neste domínio cresceu 3,93% entre 1969 e 2023. A maior parte da investigação (81%) teve origem em países-chave, incluindo Austrália, Brasil, Índia, China, Filipinas, Estados Unidos e França. Periódicos proeminentes desempenharam um papel fundamental, representando 19% das publicações. Autores notáveis incluem Attard, Everingham, Meng e Sexton, com quatro artigos publicados cada. Notavelmente, 88% dos investigadores nesta área são transitórios. Este estudo ressalta o crescimento dinâmico das aplicações de inteligência artificial na produção de açúcar, enfatizando a sustentabilidade na gestão de dados e sistemas. Conclusões: A integração eficaz destas tecnologias pode melhorar as práticas de sustentabilidade, otimizando a eficiência e a qualidade em toda a cadeia de abastecimento da produção de açúcar, contribuindo para a concretização do Objetivo de Desenvolvimento Sustentável 9. Isto porque a utilização da inteligência artificial para otimizar a produção industrial de açúcar representa uma inovação tecnológica que pode melhorar a eficiência e a infraestrutura da indústria açucareira. Isto, por sua vez, pode contribuir para alcançar o desenvolvimento sustentável à escala global

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

Disruptive Technologies in Agricultural Operations: A Systematic Review of AI-driven AgriTech Research

YesThe evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of Agricultural Technology (AgriTech) with applications of Artificial Intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations

Bulanık bilişsel haritalama yöntemiyle kurumsallaşma düzeyiniin analizi

06.03.2018 tarihli ve 30352 sayılı Resmi Gazetede yayımlanan “Yükseköğretim Kanunu İle Bazı Kanun Ve Kanun Hükmünde Kararnamelerde Değişiklik Yapılması Hakkında Kanun” ile 18.06.2018 tarihli “Lisansüstü Tezlerin Elektronik Ortamda Toplanması, Düzenlenmesi ve Erişime Açılmasına İlişkin Yönerge” gereğince tam metin erişime açılmıştır.Kurumsallaşma, yöneticilere veya çalışanlarına bağlı olmaksızın, bir şirket veya organizasyonun tüm süreçlerini sistematik ve şeffaf bir şekilde yürütmesi anlamına gelmektedir. Bu durum, kurumun misyonunu, vizyonunu, temel değerlerini, politikalarını ve stratejik amaçlarını, çalışanlar için temel değerleri ve stratejik hedefleri, kurumun yapısına ve kültürüne entegre etmeyi amaçlayan eylem planlarına dönüştüren ticari faaliyetlerle başarılabilir. Organizasyonlar, kurumsallaşma sürecini başarıyla yönetemediklerinde, genellikle uzun vadeli bir yaşam döngüsü sürdüremezler. Sonuç olarak, kurumsallaşma seviyesinin değerlendirilmesi ve geliştirilmesi için faydalı araçlar bulmak, kurumların üzerinde durması gereken bir konudur. Literatürde kurumsallaşmayla ilgili pek çok çalışma olmasına rağmen, birçoğu kurumsallaşma eğilimini ileriye dönük olarak incelememektedir. Bu bağlamda, organizasyonun kurumsallaşma eğilimi, uygun ölçütler kullanarak incelenmesi için araştırmada odak noktasıdır. Bu çalışmada, öncelikle organizasyonların kurumsallaşma eğilimini etkileyen konseptlerinin belirlendiği yeni bir yaklaşım önerilmiştir. Ardından, konseptler arasındaki etkileşimlerin ağırlıkları, uzman görüşleri alınarak hesaplanmıştır. Son olarak, kurumsallaşma konusundaki en etkili konseptler Bulanık Bilişsel Haritalar (BBH) prosedürleri kullanılarak ortaya çıkarılmıştır. Bu yaklaşım aynı zamanda hangi konsept(ler)'in kurumsallaşmanın gelişimi açısından önceliklendirileceğini belirlemek için bir bakış açısı da sağlamaktadır.Institutionalization implies conducting the whole processes of a company or organization systematically, notwithstanding the managers or employees of the institutions. This consequence could be achieved by business operations which convert the institution's mission, vision, core values, policies, and strategic aims into action plans for its people as the aim of integrating core values and strategic objectives with the institution's structure and culture. When an institutions could not manage the institutionalization process successfully, generally a long-term life cycle may not be sustained. Thus, to find useful means of evaluating and developing the institutionalization level is a big challenge that organizations must contend with. Though there are many papers about the institutionalization in the literature, many of them do not present the institutionalization tendency on a going-forward basis. In this context, institutionalization tendency of an organization is a focus of investigation to examine organization using suitable criteria. In this paper, a novel approach is proposed in which the concepts affecting institutionalization tendency of organizations are determined first. Then, the weights of the interactions among the concepts are calculated by obtaining expert opinions. Finally, the most affecting concept on the institutionalization is revealed using Fuzzy Cognitive Maps (FCMs) procedures. The approach also provides an insight to determine which concept(s) to be prioritized in terms of institutionalization development

Knowledge Representation in Digital Agriculture: A Step Towards Standardised Model

In recent years, data science has evolved significantly. Data analysis and mining processes become routines in all sectors of the economy where datasets are available. Vast data repositories have been collected, curated, stored, and used for extracting knowledge. And this is becoming commonplace. Subsequently, we extract a large amount of knowledge, either directly from the data or through experts in the given domain. The challenge now is how to exploit all this large amount of knowledge that is previously known for efficient decision-making processes. Until recently, much of the knowledge gained through a number of years of research is stored in static knowledge bases or ontologies, while more diverse and dynamic knowledge acquired from data mining studies is not centrally and consistently managed. In this research, we propose a novel model called ontology-based knowledge map to represent and store the results (knowledge) of data mining in crop farming to build, maintain, and enrich the process of knowledge discovery. The proposed model consists of six main sets: concepts, attributes, relations, transformations, instances, and states. This model is dynamic and facilitates the access, updates, and exploitation of the knowledge at any time. This paper also proposes an architecture for handling this knowledge-based model. The system architecture includes knowledge modelling, extraction, assessment, publishing, and exploitation. This system has been implemented and used in agriculture for crop management and monitoring. It is proven to be very effective and promising for its extension to other domains

FORECASTING CLIMATE AND LAND USE CHANGE IMPACTS ON ECOSYSTEM SERVICES IN HAWAIʻI THROUGH INTEGRATION OF HYDROLOGICAL AND PARTICIPATORY MODELS

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2018

Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible