Fruits are extremely fundamental in our everyday diet. The palatable fruits are harvested, sorted, and packed for conveyance to the consumers. It needs a large number of expert resources and a long time to sort and grade the fruits from the agricultural field to the fruit markets. Automation in the agricultural field and fruit markets is a must to reduce the time as well as the dependency on the manual resource. Thus, the objective of this project is to fully automate the sorting process of handling fruits. The proposed method used an image acquisition system (camera), which acquires the images of the various selected fruits (Apple, Onion, Banana, Pepper and Tomato) used for the training data. The textural and colour features of the selected fruits were extracted and then, processed using the MATLAB software with Support vector machine (SVM) algorithm as the classifier. The fruit recognition system classified the input fruit sample by determining the similarities between the colour and gray level co-occurrence matrix values of the inputted fruits samples and the values obtained from the training datasets. The proposed method is accurate and flexible. Also, a graphical user interface was developed to be used independently of the software, the recognition rate of the system had an average accuracy of 97%.
This study examined effectiveness of mobile phone utilisation for agricultural information dissemination among tomato farmers in Ondo State, Nigeria, described their socioeconomic characteristics, assessed their knowledge, and identified constraints to mobile phones utilisation. Quantitative data was obtained from 100 tomato farmers through a three-stage sampling procedure. Chi-square analysis was used to test the relationship between respondents’ socioeconomic characteristics and effectiveness of utilisation of mobile phones. The results revealed that 94% of respondents used mobile phones, 66% utilised their mobile phones for agricultural purposes, 62% had internet-enabled services on their phones, and 51% indicated that mobile phones impacted their tomato production activities. The severe constraints faced by the respondents were "no light to charge phone" (x? = 2.75; ? = 0.87) and "network issues” (x? = 2.68; ? = 0.76). There was a significant relationship between age (?2=38.720; p< 0.05), marital status(?2=270.440;p<0.05), family size (?2=63.061;p< 0.05), secondary occupation (?2=58.080;p<0.05), level of education (?2=71.703;p< 0.05), sources of labour (?2=45.200;p< 0.05), sources of credit (?2=174.960;p< 0.05), monthly income (?2=63.900;p< 0.05) and effectiveness of mobile phone technology (?2=58.080;p< 0.05). The study recommended upgrading network infrastructure, enhancing affordability of mobile services, developing user-friendly customized applications and fostering digital literacy among the farmers
Grain trade plays a key role in global food security, but it faces a number of challenges, including climate change, geopolitical conflicts, and market volatility. These challenges have led to increasing attention on the agricultural applications of artificial intelligence (AI). AI technologies (such as predictive analytics, automated logistics, and precision agriculture) are helping to increase efficiency, reduce losses, and enhance sustainability. The aim of this article is to examine the role of AI in grain trade and its future potential.
Food waste has become a pressing global sustainability challenge, reflecting both environmental degradation and inefficient resource utilization. This study explores the relationships between attitudes, subjective norms, and perceived behavioral control (PBC) toward food waste reduction in the Kurdistan Region of Iraq, guided by the Theory of Planned Behavior (TPB). A quantitative approach was applied using an online survey of 205 respondents from Duhok City, employing a convenience sampling technique. Data were analyzed using SPSS version 26 through descriptive statistics, reliability and validity tests (Cronbach’s alpha, KMO, and Bartlett’s test), and multiple regression analysis. The results revealed that attitudes (? = 0.46, p < .001) and subjective norms (? = 0.30, p < .001) significantly influence perceived behavioral control (R² = 0.314), indicating that moral attitudes and social influence are key predictors of individuals’ perceived capacity to reduce food waste. The study confirms the applicability of the Theory of Planned Behavior in understanding food waste reduction–related perceptions in emerging economies and highlights the importance of public education, social campaigns, and household awareness programs. The findings provide valuable insights for policymakers, educators, and environmental organizations seeking to foster behavioral change and sustainability in the Kurdistan Region of Iraq.
The management of Waste Electrical and Electronic Equipment (WEEE) remains a critical sustainability challenge across the European Union (EU). Although the WEEE Directive 2012/19/EU predates the United Nations Sustainable Development Goals (SDGs), its progressively stricter collection targets, from 45% to 65%, are closely aligned with global objectives on sustainable production and consumption. This study develops an integrated predictive framework to forecast the EU27 WEEE Collection Rate (CR) from 2022 to 2030. The framework draws on four modeling families: Statistical methods, Machine Learning (ML) algorithms, Deep Learning (DL) architectures, and selected Hybrid configurations. Data for “Waste Collected” (COL) and “Products Put on the Market” (MKT) were obtained from Eurostat, with missing values imputed through linear interpolation validated against external socioeconomic indicators. Among all models tested, Lasso and Ridge Regression achieved the most accurate forecasts for the COL and MKT datasets, respectively. Although a Hybrid model was implemented to address non-linear residual patterns, it did not outperform the standalone Lasso model, which was retained for CR estimation. The resulting forecasts reveal a consistent downward trend in the Collection Rate, remaining below the 65% target throughout the forecast horizon. This shortfall is primarily driven by an accelerating volume of EEE placed on the market that is not matched by proportional increases in WEEE collection. The findings highlight systemic gaps in current collection mechanisms and underscore the need for enhanced policy interventions. The proposed framework offers a replicable and empirically validated tool to support evidence-based planning and regulatory monitoring in alignment with EU environmental policy and the Sustainable Development Goals.
The research investigated the possibilities and effectiveness of applying decision support systems (DSS) in the planning of dispensary processes in 24 individual sheep farms operating across six regions of Azerbaijan. Thirteen sheep breeds native to Azerbaijan are bred in these farms. The purpose of the study is to develop a functional DSS model that will ensure monitoring of sheep health status, forecasting of risks, optimization of preventive measures, and rational decision-making at the farm level. Field research and empirical observations revealed that the dispensary process in most farms is still carried out through traditional methods—paper-based records and subjective observations—which complicates early diagnosis and leads to errors. In farms where DSS was applied, treatment and prevention costs decreased on average by 22.4%, livestock losses by 28.6%, while milk and meat productivity increased by 18.3% and 14.8%, respectively. The system’s ROI (Return on Investment) indicators were 62.3%, 77.3%, and 89.9% in small, medium, and large farms, respectively. The DSS model proposed within the study includes several key functional blocks: data collection (from sensor and manual sources), an analytical engine (statistical and AI-based analysis), a risk assessment module, and a user interface (mobile application, web panel, reporting mechanisms). As a scientific novelty, this research presents the complex integration of decision support systems into dispensary processes in sheep farming, which is significant both theoretically and practically.
Digitalisation is the most significant shaping factor of today's economy, creating new opportunities and challenges, especially for small and medium-sized enterprises (SMEs). This study aims to explore the extent and structural differences in how SMEs in the European Union use cloud computing and artificial intelligence (AI) services, and how these two technologies are interconnected in terms of digital maturity and competitiveness. The research is based on Eurostat data from 2018 to 2024, applying a descriptive statistical approach. According to the results, cloud computing is the foundation for the digitalisation of SMEs: in 2024, 41.7% of small companies, 59% of medium-sized companies, and 77.6% of large companies utilised cloud services. However, the use of AI is more limited, with 11.2% of small companies, 21% of medium-sized companies, and 41.2% of large companies having adopted AI-based solutions. The data indicate a strong complementary relationship between the two technologies: cloud infrastructure provides the data and computational conditions necessary for AI to operate. At the same time, AI enhances the business value of cloud use by evaluating and automating data. At the sectoral level, the information and communication technology (ICT) sector, along with other knowledge-intensive services, is at the forefront of both technologies. At the same time, the manufacturing and construction industries continue to lag significantly. At the regional level, the high adoption rates in Northern and Western European countries stand in stark contrast to those in Eastern and Southern Europe, where technological infrastructure, digital competencies, and investment opportunities are more limited. The study concludes that cloud and AI technologies are not only complementary but also form the basis of digital development for SMEs. The success of digitalisation is also a result of organisational and cultural adaptation. The long-term competitiveness of SMEs depends on their ability to integrate these technologies into their operations with a strategic approach and conscious innovation.
The strategic role of Information Systems (IS) in supply chain competitiveness (SCC) has undergone a profound, yet inadequately synthesized evolution. This narrative review addresses this gap by proposing a 3-era framework that charts the coevolution of IS and SCC, revealing a fundamental paradigm shift in competitive logic. The analysis identifies: (1) the Internal Efficiency Era, where IS established cost leadership through automation and intra-firm integration (e.g., ERP and MRP); (2) the Inter-Organizational Coordination Era, where digital connectivity redefined advantage as dyadic reliability, enabling practices like VMI and CPFR (e.g., via EDI, RFID; (3) the Network-Wide Intelligence Era, where converging technologies (e.g., AI, IoT, and blockchain) are fostering ecosystem-wide resilience and adaptive decision-making. The review contributes a coherent conceptual framework that argues IS has progressively reconfigured the source of competitive advantage. This synthesis provides scholars and practitioners with a robust lens for understanding the historical progression and future trajectory of digitally driven supply chains.
This article aims to start the development of language models for agronomic advisory service. This is done by evaluating various n-gram language models with different smoothers: Modified Kneser-Ney, Add-k, and Absolute. The models were constructed using an earlier collected dataset on Norwegian agriculture. They were adapted to provide practical agronomic advice on integrated pest management. Model performance was measured using perplexity. The Add-k (k=0.1) scored perplexities of 1920, 7800, 13600, and 16200 for 2-, 3-, 4-, and 5-grams, respectively. In this study, the Modified Kneser-Ney (D=0.8, D=0.8, and D=0.8) performed best, achieving perplexities of 494, 363, 344 and 339 for the same orders. When expressing the best performing model, the Modified Kneser-Ney model (trigram) could predict sentences such as “Meadowgrass is a grass that grows in more or less dense lawns”. However, before being practically useful, the models need further development.
Efforts to expand irrigated agriculture faces challenge of managing scarce water resources amid rising food demand and impacts of climate change especially in ASAL districts. The objectives of this study were; to monitor spatial-temporal changes in performance of irrigation production system and to relate agronomic causes of variation in productivity. The open access remotely sensed WaPOR database was applied to assess chronology of land and water productivity of maize crop. Decadal data was bulk downloaded from FAO WaPOR portal delimited to the boundary of Galana Kulalu irrigation scheme in Kilifi, Kenya for four cropping seasons, 2018 to 2022. Soil chemical properties were analyzed for hot/cold spots in the scheme. The 2018-2019 season recorded highest net primary productivity (NPP) and actual evapotranspiration (AETI) and was high in center pivots located southern side of the scheme. Irrigation equity was rated fair but deteriorated to poor rating in the 2021-2022 season. Crop water productivity ranged between 0.43 and 1.07 Kg/m3. Poorly performing center pivots were characterized by significantly high soil moisture content (p< 0.05), highly alkaline soil pH 8.6 – 8.9 and high exchangeable sodium (ES) levels. Strong negative correlation existed between water productivity and exchangeable sodium levels. Critical remedial measures are needed to restore soil health, improve efficiency of water consumption and crop yield performance.
This study employs bibliometric analysis to explore research trends, challenges, and innovations in aligning agricultural development with the Sustainable Development Goals (SDGs). Based on Scopus-indexed literature from 2016 to 2024, Citation Network Analysis (CNA) and Co-Occurrence Network Analysis reveal key thematic areas including SDG integration, sustainability trade-offs, food security, the water-energy-food nexus, and climate adaptation. The findings highlight critical regional disparities, governance gaps, and economic constraints that hinder effective implementation of sustainable agricultural strategies, particularly in developing regions such as Africa, India, and Bangladesh. Although technologies like precision agriculture, remote sensing, and smart irrigation offer promising solutions, their uptake remains limited due to infrastructural and financial barriers. The study underscores the need for inclusive policy frameworks, multi-stakeholder collaboration, and targeted investment in sustainable technologies. A noted limitation is the exclusive use of the Scopus database, which may overlook relevant research indexed elsewhere. These insights aim to inform policymakers, researchers, and practitioners in advancing SDG-aligned agricultural transformation through evidence-based strategies and global cooperation.
Precision farming has developed as a pivotal advancement in modern agriculture, tackling key challenges associated to economic, environmental and social sustainability. This article explores the implementation of precision farming technologies (PFTs) across developed and developing countries, focusing on the driving factors and challenges. A Mediated Causal Conceptual Framework Model is developed to analyse the factors influencing farmer’s decisions on the adoption of precision farming technologies across national contexts. Based on a comparative approach and supported by empirical evidence from several studies, the research emphasizes how variations in farm sizes, degree of digitalization, capital availability, and the role of policy frameworks influence the adoption. The findings indicate that networking, government support, and training packages are fundamental in increasing awareness and willingness to adopt PATs. Likewise, high costs, limited information, and struggle to adjust continue to be substantial barriers in both advanced and emerging economies.
There should be two blank (10-point) lines before and after the abstract. The adoption of cloud computing in Syria is shaped by a complex set of socio-technical factors, exacerbated by the nation’s prolonged conflict and fragile economic landscape. Utilizing the Technology–Organization–Environment (TOE) framework, this study systematically investigates the key drivers and impediments influ-encing cloud computing uptake among Syrian enterprises. This study employs a qualitative systematic review based on the TOE framework to identify and classify adoption factors. Through an integrative review of aca-demic literature, regional case studies, and sector-specific data, the analysis identifies several enabling factors, including cost efficiency, scalability, and operational flexibility. However, the diffusion of cloud technologies remains constrained by infrastructural deficiencies, heightened security and privacy concerns, regulatory uncer-tainty, and limited organizational readiness. The findings underscore that successful adoption in such contexts requires a strategic alignment between technological capability, organizational capacity, and a supportive policy environment. Implications are presented for policymakers, industry stakeholders, and future research initiatives focused on advancing digital transformation in post-conflict economies.
The rapid advancement of Artificial Intelligence (AI) technologies has revolutionized various aspects of business operations. This study aims to define the scope of AI applications in businesses through a comprehensive literature review and bibliometric analysis. Using a structured search protocol for data collection, a comprehensive search was conducted across key academic databases to identify relevant literature on AI in business. The gathered data were then analyzed using VOSviewer and Bibliometrix tools to identify key themes, trends, and author groupings within the research field. Results indicate a diverse range of AI applications, including process optimization, customer relationship management, and predictive analytics, showcasing AI's transformative potential in business contexts. The bibliometric analysis further reveals the evolution of research focus over time, highlighting emerging themes and gaps in the literature. This review proposes a structured framework for future research in AI applications in businesses, advocating for a more integrated and strategic approach to AI deployment to maximize its benefits. By identifying the current state of research and future directions, this study provides a valuable roadmap for researchers and practitioners seeking to leverage AI for business innovation and efficiency.
Cyberattacks on critical infrastructures have escalated in frequency and complexity over the past decade, posing systemic risks to national security, public safety, and economic stability. This study analyzes cybersecurity risks across key infrastructure sectors including energy, healthcare, finance, transportation, and others, drawing on threat intelligence from the U.S. Cybersecurity and Infrastructure Security Agency (CISA), the EU Agency for Cybersecurity (ENISA), and industry sources such as IBM X-Force and Verizon DBIR. We compare U.S. and EU regulatory approaches, identifying strengths and limitations in frameworks like the Network and Information Systems 2 Directive (NIS2 Directive) and the National Isntitute of Standards and Technology (NIST) Cybersecurity Framework. Our findings show that while the EU favors centralized and mandatory compliance, the U.S. has leaned toward voluntary and sector-specific standards, although this is gradually changing. We also examine the most frequently targeted sectors, highlight trends in attack types such as ransomware, and discuss how smaller organizations, particularly SMEs and minority-owned businesses, often serve as vulnerable entry points for attackers. The results emphasize the urgent need for integrated and forward-looking cybersecurity strategies that combine regulation, collaboration, and continuous adaptation to an evolving threat landscape.
This study examined Scopus-indexed articles in Agricultural and Biological Sciences from Scopus narrow subject fields, focusing on the relationship between citations and readership among Indian publications. The study retrieved 15,700 articles from the Scopus database, of which 83.25% with Digital Object Identifiers (DOI) were suitable for Mendeley readership. 99.96% of DOI articles matched the Mendeley API to measure readership. 94.39% of DOI articles had at least one reader, whereas 84.66% had at least one citation. The "Food Science" field had the highest citation and readership (18.51% and 19.72%, respectively) among all Agricultural and Biological Science narrow subject fields. The "Forestry" fields had the highest mean and standard deviation (SD) values for citation (18.27 and 29.78) and readership (49.05 and 69.39). Data skewness varied among subject fields, with "Food Science" and "Soil Science" showing the highest and lowest for both citations and readership. The geometric mean was similar to the mean value for the "Food Science" field for citation and readership variables. The mean normalization log-transforms citation score revealed that the "Plant Science" field had a higher value of 1.000124 with a group indicator for readership. Almost all narrow subject fields demonstrated strong positive correlations between citations and readership, ranging from 0.716 to 0.847.
This systematic literature review explores the role of Development Financial Institutions (DFIs) in advancing sustainable agricultural development and rural economic growth. Based on an analysis of 73 articles from the Scopus database, the study highlights DFIs’ key contributions, including the provision of financial support, promotion of technological modernisation, and development of rural infrastructure. It also examines the strategies DFIs employ to foster innovation, enhance productivity, and support climate-resilient agricultural practices. Despite their significant roles, DFIs encounter various challenges, such as limited institutional capacity, inadequate financial resources, and high risks associated with agricultural lending. The review suggests strengthening institutional frameworks, fostering collaboration among governments, DFIs, and the private sector, and designing financial products tailored to the specific needs of the agricultural sector. These findings offer valuable insights for policymakers, researchers, and financial institutions seeking to enhance the effectiveness of DFIs in promoting sustainable agriculture and rural development.
Mitigating and addressing climate change is one of the main issues that concern the global community. The rapid increase in the world population as well as human activities has led to the pollution of the natural environment and the depletion of natural resources. The global community is now aware of the situation and green practices for sustainability are being implemented. Technology has now infiltrated all areas of our daily lives. The use of mobile devices has increased dramatically around the world, and they have become devices for all uses. Applications have been developed that are called “smart applications”, because they can respond to real conditions, process complex situations and act instantly. They facilitate the daily lives of citizens and promote energy saving. A multitude of “smart applications” have been developed with which citizens can calculate their ecological footprint and adapt their behavior with the aim of reducing it. This research is an approach to document and describe smart applications in the global market. The use of such applications can be a sustainable alternative in terms of saving valuable environmental resources and capital, while simultaneously reducing the total annual greenhouse gas emissions and environmental impacts.
A B S T R A C T Water being the most perilous abiotic stress to crop growth, the performance of irrigation systems in the largely arid and semi-arid Kenya is critical in increasing agricultural production. The study aimed at improving the efficiency of irrigation systems for rice in the Mwea Irrigation Scheme during the major growing season from August to December 2022. The study used datasets which include: Vegetation Health Index (VHI), Leaf Area Index (LAI), climate data, crop data, soil data. Random Forest Algorithm was used to fuse VHI and LAI to obtain the crop water content and using the evapotranspiration calculated based on Penman Monteith Algorithm using Climate data, crop coefficient KC values at the different growth stages of rice is obtained. The CROPWAT model was used to integrate the datasets to estimate irrigation water requirements at the different growth stages and develop an irrigation schedule. From the results, the spectral indices ranged from 0-1with values near zero indicating adequate crop evapotranspiration, good crop condition implying no water stress while higher values close to 1 may indicate crop water stress. The temporal fluctuation in estimated CWC-derived Kc values showed moisture stress events during the season where reduced CWC caused a corresponding drop in Kc values. From the calculation of irrigation water demand, irrigation water increases gradually and reaches its peak at the mid/grain filling stage and decreases in the late stage. From the irrigation schedule developed, schedule based on daily monitoring of soil moisture balance is more robust in contrast with a schedule based on regular time interval period which may lead to over-irrigation or under-irrigation. A validation approach for the applicability of CROPWAT in Mwea Irrigation Scheme should however be developed.
Artificial Intelligence has broad implications for humanity, and therefore vital for educational institutions to equip learners with the skills to navigate careers. The paper synthesises research on traditional and new tools for decision-making and presents implications that the future of AI holds for leaders and developed economies. The overall objective is to draw a comparison between traditional decision making and AI decision making in organisations. A total of 200 managers participated in the quantitative study. The results show that traditional decision making was preferred over AI decision making as it scored a higher mean value. The results of a multiple regression test also revealed that with the increase of the perceived threat of AI, managers’ willingness to use AI increased. Given the circumstances that learning AI skills could advance a manager’s career, then it led them to use AI. The implications of these results inform employers’ strategic plans to implement learning and development initiatives while also informing governments about their impact on policymaking. Human resources traditionally used compensation and rewards to motivate employees in a change process. However, improving career prospects through the adoption of AI can create a favourable environment where the organisation can reap the benefits of AI and employees’ development.
