International Journal of Sustainable Agricultural Management and Informatics (32 papers in press)

Regular Issues

• Innovations and green development: are a way out for the small and medium enterprises of the Western Macedonia Region  
  by Georgios Maggiridis, Fotios Chatzitheodoridis, Efstratios Loizou 
  Abstract: The study aims to assess the difficulties faced by SMEs in the region of Western Macedonia during the previous five years and to highlight their possible way out through innovations and green development. For this purpose, a questionnaire was used, which was drawn up for data collection by the SMEs of the region. According to the results of the survey, the shock of the energy transition, the economic crisis and the COVID-19 virus dramatically limited and stopped the development of SMEs in Western Macedonia. the investments in innovation and green development that significantly increasing the last few years in the area are related mainly to multinational enterprises and less to the SMEs. The role of the SMEs could be mainly supporting these large energy companies through trading activities and more significantly in the primary sector to the fields of bio and new cultivations, and bio and traditional products.
  Keywords: small and medium enterprises; innovation; green development; Western Macedonia Region; renewable energy.
  DOI: 10.1504/IJSAMI.2024.10064455
   
  
• Evaluating dimensionality reduction strategies on mixed-type datasets: a comparative analysis using Python, R and SPSS  
  by Zacharenia Kyrana, Emmanouil D. Pratsinakis, Nikolaos Papafilippou, Angelos Markos, Christos Dordas, George C. Menexes 
  Abstract: Multidimensional and multivariate datasets encompassing diverse data types provide researchers a platform to apply a range of dimensionality reduction methods. This study assessed principal components analysis, factor analysis, multiple correspondence analysis, categorical principal components analysis and factor analysis for mixed data. We examined different strategies based on input variable measurement scale selection and variable value coding. The objectives were to highlight the importance of applying different analysis strategies, ascertain the applicability of these methods to multidimensional mixed-type data, compare outcomes, and evaluate execution times from three different statistical software to identify notable computational and interpretive drawbacks. drawbacks. Significant issues included the curse of dimensionality concerning the determination of crucial dimensions, the need for increased computing power, the absence of software code for some methods and criteria, discrepancies in results calculations across software packages, and the inability of some software packages to handle numerous variables or binary-coded variables and perform parallel analysis.
  Keywords: multivariate data; principal components analysis; factor analysis; multiple correspondence analysis; categorical principal components analysis; factor analysis for mixed data; dimensionality reduction.
  DOI: 10.1504/IJSAMI.2024.10065202
   
  
• Organisation and management of beekeeping parks and the contribution of their infrastructure  
  by Georgios Kolkos, Apostolos Kantartzis, Symeon Marnasidis, Christos Charizanis, Christina Moulogianni, Garyfallos Arabatzis 
  Abstract: Honey bees play a crucial role in both natural and agricultural ecosystems worldwide by pollinating various crops. In Greece, beekeeping is predominantly nomadic and serves as a significant source of supplementary income for many. However, there’s a need for tailored land management policies to support and promote the apiculture sector, including adaptations in the National Forest Policy to facilitate beekeeping activities. While Greek legislation offers some flexibility regarding apiary placement on public lands, there’s a lack of regulations regarding designated placement sites. To address these issues, the establishment of especially demarcated beekeeping parks is proposed. These parks would not only provide beekeepers with designated spaces to increase production and lower costs but also contribute to organising beekeeping activities more effectively both nationally and internationally. Such measures are crucial for ensuring the sustainability and prosperity of beekeeping ventures in Greece and beyond.
  Keywords: beekeeping parks; forest roads; infrastructures; organisation; management; land use planning.
  
  
• Agricultural finance dynamics in Bangladesh: exploring factors influencing rice yield and farmers access to credit  
  by Rozina Yeasmin, Babor Ahmad, Shuktara Khanom, Md. Shakiul Hossain, Mohammad Main Uddin, Shahiduzzaman Selim, Anowar Hossain, Ashrafuzzaman Sohag 
  Abstract: This study examines factors influencing Bangladeshi farmers acceptance of agricultural financing and the impact of credit on rice output. Through a comprehensive survey involving 255 rice producers and utilising logistic regression and the Cobb-Douglas production function, the study identifies key determinants affecting rice yield among farmers utilising formal and informal credit, as well as those without credit. Factors such as household characteristics, land ownership, inputs (e.g., seeds and irrigation), and proximity to markets significantly influence rice productivity. Additionally, the study underscores the importance of unauthorised lending sources in facilitating higher-than-average rice yields and emphasises critical determinants - like age, education, experience, household size, and landholding - for farmers access to financing. The result also reveals that credit amount significantly influences rice production, with an average incremental positive effect of 12.60%. For formal credit holders, this effect is estimated at 14.4%, while for informal credit access, it is predicted to be 20.10%. These findings offer insights into enhancing agricultural credit accessibility and emphasise the role of informal credit sources in driving agricultural productivity.
  Keywords: agricultural credit; rice producer; productivity; formal credit; informal credit; Bangladesh.
  DOI: 10.1504/IJSAMI.2024.10065697
   
  
• Cultural and landscape elements in the integrated local development strategies  
  by Maria Paschalidou, Fotios Chatzitheodoridis, Achilleas Kontogeorgos, Athanasia Mavrommati 
  Abstract: The purpose of our research it is a first approach to identify how cultural landscapes are promoted, included, and developed in common agricultural policies. The major role of cultural landscape policy should be to bring social cohesion, to enhance prosperity and succeed integrated local development. The cultural policies of the rural landscape are a demanding and complex task, because of a mixture of globalisation, local history and tradition, and the current local political context. The findings show that, in terms of both output and income, the regional economy has not yet benefited from the adoption of the Pillar II policy initiatives about culture of rural landscape.
  Keywords: rural landscape; cultural heritage; regional development; culture; values; cultural landscape; rural space; Pillar II; policy impacts.
  
  
• A spatial data image processing model using unmanned aerial vehicles and RGB imagery for crop farming on small-scale subsistence farms  
  by Basuti Gerty Bolo, Irina Zlotnikova, Dimane Mpoeleng 
  Abstract: This paper presents a spatial data image processing model using unmanned aerial vehicles (UAVs) equipped with RGB cameras for enhancing agricultural productivity on small-scale subsistence farms in semi-arid regions of Botswana. The model leverages high-resolution UAV imagery (0.19 cm/pixel) to transform raw, non-spatial complex data into actionable geospatial information. It integrates machine learning algorithms, including unsupervised ISODATA and supervised support vector machine (SVM) classifiers, with proposed new RGB-based vegetation indices, to extract detailed information on crop types, land use, and coverage. The research addresses the challenges faced by smallholder farmers, such as limited access to advanced technologies and fluctuating environmental conditions. This model facilitates improved agricultural decisions and productivity by providing a cost-effective and accurate method for monitoring and managing crops through localised data capture and advanced data processing techniques. Results demonstrate an overall classification accuracy of 82.5% with the ISODATA algorithm, proving its utility in precision agriculture tailored for resource-limited settings. The study underscores the potential of integrating UAV technology with machine learning to support sustainable agriculture in developing regions.
  Keywords: unmanned aerial vehicles; UAVs; RGB imagery; small-scale subsistence farming; spatial data processing; crop monitoring; machine learning; vegetation indices; ISODATA; support vector machine; SVM; accuracy assessment; precision agriculture.
  DOI: 10.1504/IJSAMI.2025.10071557
   
  
• Hyperledger Fabric blockchain-based secured framework for agricultural IoT data  
  by Neenu Johnson, M.B. Santosh Kumar, T. Dhannia 
  Abstract: Agricultural internet of things (IoT) data is a significant digital asset and requires to be securely shared and effectively analysed to enhance productivity, optimise resource usage, achieve sustainability, and support the economic development of the nation. Agricultural IoT data gathered from various connected devices and sensors deployed in agricultural environments primarily involves data related to crop monitoring, soil health, weather patterns, and other relevant factors. The successful adoption of IoT revolutionises agrarian society and enhances the livelihood of farmers. Agricultural IoT data has an inherent sensitivity that emphasises the requirement of a secured IoT data sharing platform. A secured data sharing and storage platform for agricultural IoT data that leverages the benefits of blockchain, and Node-Red is proposed. The proposed framework facilitates the secured storage and exchange of agricultural IoT data among various agricultural stakeholders.
  Keywords: internet of things; IoT; Hyperledger Fabric; sensors; Raspberry Pi 4; Node-Red; agriculture; blockchain; smart contract.
  
  
• Integrating UTAUT3 and DM ISS 2016 theory: a proposed framework for identifying determinants and successful factors in agricultural information systems  
  by Hafni Amalia Juniarti, Alia Bihrajihant Raya, Subejo, Rahima Kaliky, Siti Andarwati 
  Abstract: The usage of agricultural information systems must be determined based on a literature review of characteristics that depend on user behaviour and the success of information systems. The methodology applied in this paper is a narrative review. The review used the search, appraisal, synthesis, and analysis (SALSA) methodology to integrate or synthesise data relevant to the review topics. Twenty-two adoption innovation theories contribute to theoretical research and are appropriately utilised. The simplest model that presumably evolves to determine behavioural intention in the adoption information system in agriculture is the Unified Theory of Acceptance and Use of Technology (UTAUT). Examining successful information systems is DeLone and McLeans as pioneers for the information system (DM ISS). The UTAUT and DM ISS research in the 2017-2023 agricultural adoption innovation period is described in related studies. The merger of UTAUT3 and DM ISS 2016 could be presented as complementary theories focusing on intention variables and successful agricultural information system usage behaviour.
  Keywords: adoption; UTAUT; DM ISS; agricultural information systems; SALSA methodology; adoption innovation; complementary theories.
  
  
• From consumerism to contentment! The role of minimalism in promoting well-being: a moderated mediation approach  
  by Varghese Joy, Vijay Kumar Jain, Ashwin H. Parwani, Vijay Prakash Anand, Preeti Sharma 
  Abstract: Current consumption behaviours have resulted in environmental deterioration, which endangers the underlying processes that underpin our future development and existence. There is a need to adopt new consumption habit is that are environmentally friendly and have a positive impact on consumers. As a result, the current study has proposed a framework to examine the impact of minimalism on consumer well-being using survey data from a representative Indian population. It also tests the mediating role of satisfaction and happiness and mediated moderated role of environmental knowledge. The SMARTPLS results show a favourable association between minimalism and well-being. The study supports the mediation of satisfaction and happiness in the aforementioned relationship. Marketers can capitalise on the growing trend of minimalism and it is link to well-being to attract new segments of consumers wanting a more balanced and intentional existence. This expanding customer demographic is increasingly drawn to brands that value quality over quantity, sustainability, and transparency.
  Keywords: mindfulness and self-care; holistic well-being; sustainability; sparse aesthetics; voluntary simplicity; pro-environmental consumption.
  DOI: 10.1504/IJSAMI.2025.10068937
   
  
• Utilising the potential of circularity: novel strategies for minimising food loss and waste in the circular economy  
  by ShivamKrishn Agrawal, Shrish Singh, Akhilesh Shukla, Bipin Kandpal 
  Abstract: This study examines novel strategies to tackle food loss and waste in the food industry, with a focus on resource efficiency and sustainability. It aims to promote sustainability in the food sector by applying circular economy principles. The research prioritises agri-food industry circular practice drivers using literature review and expert opinions. The methodology uses interpretive structural modelling and analytic hierarchy process to rank fourteen key drivers from a comprehensive literature survey. The findings suggest that policy factors, such as favourable legislation and accurate demand forecasting, are of paramount significance. Additional notable factors contributing to the overall progress are water recycling, the integration of renewable energy, and the use of sustainable packaging. The study emphasises the crucial importance of systemic methodologies and cooperation among stakeholders in circular food supply chains. The framework contributes by offering an organised plan for drivers, policy recommendations, and future research directions to advance circularity in agri-food systems.
  Keywords: circular economy; sustainable food packaging; food waste; water recycling; carbon footprint reduction.
  DOI: 10.1504/IJSAMI.2025.10070050
   
  
• M-Velanmai: fuzzy artificial immune network model-based mobile application for plant protection in rice and its users evaluation  
  by C. Karthikeyan, S. Pazhanivelan, S. Aravindh Kumar, Smitha S. Kumar 
  Abstract: The fuzzy artificial immune network (FAINet) model was developed for rice pest and disease identification, utilising deep learning techniques and trained on a dataset of 19,642 images collected directly from rice fields. These images were pre-processed, and transfer learning was employed to optimise model performance. The FAINet model showed a strong negative correlation between training steps and both training and validation loss (0.86 and 0.94), while a positive correlation (0.92) was observed with training accuracy. The confusion matrix confirmed the models strong performance in accurately identifying all major rice pests. FAINet achieved an overall prediction accuracy of 99.10% and a test accuracy of 98.50%, surpassing other models. Integrated into the M-Velanmai mobile app, FAINet automates the identification of rice pests, disorders, and diseases. Feedback from 510 M-Velanmai app users indicated a predominantly positive perception regarding the effectiveness of pest management advisories, highlighting FAINets ability to deliver timely and reliable solutions.
  Keywords: ‘M-Velanmai’ app; fuzzy artificial immune network; FAINet; convolutional neural network; CNN; neural network; automated pest identification; training loss; model performance; feedback; advisories; rice and evaluation.
  
  
• Sustainability benchmarking in HEIs: best practices and future trends  
  by Dimitra B. Manou, Odysseas Christou, Anastasia Blouchoutzi, Eduardo Franco, Stefano Armenia, Juan Uribe Toril, Jose Luis Ruiz Real 
  Abstract: The UN 2030 Agenda sets an ambitious framework for all countries and actors, state and non-state, and calls for them to align their development perspectives with the sustainable development goals (SDGs). Higher education institutions (HEIs) have an important role to play in fulfilling SDG 4 Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all. Target 4.7 requires mainstreaming of sustainable development in national policies, curricula, and assessment tools. This paper analyses the development and implementation of appropriate mechanisms and tools in HEIs sustainability benchmarking and identifies best practices for institutional sustainability self-assessment tools under the lens of the whole institution approach. It presents most prominent self-assessment tools for sustainability benchmarking applied by HEIs, which could lead to sustainable and energy-efficient universities. Finally, it explores future trends in higher education sustainability benchmarking. The analysis and results may offer a new perspective to academic research in this field.
  Keywords: sustainability; higher education; assessment tools; higher education institutes; HEIs; sustainability benchmarking; whole-institution approach; sustainable education; sustainable development goals; SDGs; energy-efficient universities; green universities.
  
  
• Does responsible consumption lead to consumer well-being? An empirical study exploring generation Z perspective using sustainable consumption theory  
  by Aditi, Akhilesh Shukla, Shivendra Singh, Shriram S. Dawkhar, Shipra Agarwal 
  Abstract: The astonishing consumption levels have a significant impact on the ecosystem and biodiversity. The rapidly diminishing resources and a deteriorating environment are forcing new consumption patterns and casting doubt on the notion that consumers are making logical choices. Therefore, the goal of the current research is to identify the factors that contribute to responsible consumption. It further examines the impact of responsible consumption on consumer well-being. The current study has taken four exogenous variables, namely: sustainable living, redefining consuming mind-set, perception of fair trade and circular consumption. The model was investigated and validated using data from 500 respondents via structural equation modelling. The most significant antecedent to responsible consumption, after redefining consumption attitude, is circular consumption. The findings of the study will help marketers to develop strategies to encourage responsible purchasing among consumers, thereby, improving their well-being and creating positive impact on society. From an environmental perspective, encouraging responsible consumption will result in less resource depletion, smaller carbon footprints, and improved sustainability practices across businesses.
  Keywords: responsible consumption; decarbonisation; circular consumption; linear economy; carbon-footprint; sustainable purchasing.
  
  
• Optimised polyculture vertical farming: harnessing IoT and machine learning for enhanced crop rotation strategies  
  by Nissi Grace A. Coronel, John Eric L. Espion, Daryll C. Maldicas, John Joshua F. Montañez 
  Abstract: This study developed a polyculture vertical system with crop rotation recommendations. The crop monitoring system was implemented using sensors to monitor soil pH, soil moisture, NPK levels, temperature, and humidity. Z-test was utilised as a statistical method to assess and evaluate the performance and effectiveness of the sensors. The developed system involved training and testing of three machine learning models that gave recommendations on the best crops to be grown. They included K-nearest neighbours (KNN), random forest, and extreme gradient boosting (XGBoost). Performance was measured on the basis of four metrics namely, Precision, Recall, F1-Score, and Accuracy with respect to the three machine learning models. In terms of the crop recommendation system, the XGBoost algorithm emerged as the top-performing model, delivering a maximum mean accuracy of 91.26%, compared to 88% and 91.16% of KNN and random forest, respectively.
  Keywords: algorithm selection; extreme gradient boosting; XGBoost; infinite agriculture; internet-of-things; K-nearest neighbours; KNN; machine learning algorithms; plant polyculture; random forest.
  DOI: 10.1504/IJSAMI.2024.10068121
   
  
• Sustainability alternatives of the post-lignite era: a case study on residents perceptions in Western Macedonia - Greece  
  by Alexandra Ioannidou, Paraskevi Boufounou, Garyfallos Arabatzis, Kanellos Toudas, Chrisovaladis Malesios 
  Abstract: This paper investigates the socio-economic effects of shutting down the public power corporation (PPC) plants in Western Macedonia, Greece. It details the areas socio-economic profile, the delignification process, and regulations for transitioning to green energy. A survey of 400 residents was analysed using descriptive statistics and statistical tests. The study evaluates the current situation and suggests pathways to sustainability, highlighting residents attitudes toward the post-lignite period. Residents, aware of the inevitable closure of PPC plants, favoured repurposing the land for agriculture and sustainable tourism. The findings emphasise the need to consider gender and age dynamics in energy and environmental policy discussions and interventions, crucial for sustainable development following the delignification process.
  Keywords: socioeconomic factors; Western Macedonia; Greece; delignification; residents’ attitudes; sustainability; alternative post-lignite land-uses.
  
  
• Financing the green transition in the Western Balkans  
  by Bojana Olgić Draženović 
  Abstract: The green transition in Croatia, Slovenia and the Western Balkans (WB) is crucial for sustainable development and aims to reconcile economic growth and environmental protection. This paper analyses the development of green and sustainable financial markets in these regions, focusing on the issuance of various financial instruments. Croatia and Slovenia, as EU members, have progressive decarbonisation policies and legal frameworks that support green finance, while the WB struggle with challenges such as low energy efficiency and dependence on coal. Despite progress, these countries are lagging behind the EU in green transition efforts. The study highlights the important role of sustainable finance in the implementation of the European Green Agenda and emphasises the need for improved policies and awareness in the WB to promote green growth. This comprehensive analysis provides an understanding of the green transition efforts and financial strategies in these regions.
  Keywords: green agenda; sustainable financial instruments; green finance; decarbonisation; ESG; Croatia; Slovenia.
  DOI: 10.1504/IJSAMI.2025.10071707
   
  
• Developing sustainable agricultural management and information and communications technologies (2015-2024)  
  by Juan Uribe-Toril, Jaime De Pablo Valenciano, Juan Milán-García 
  Abstract: International Journal of Sustainable Agricultural Management and Informatics is a journal in the field of agricultural and biological sciences, as well as business, management and economics, founded in 2015. The journal celebrates its 10th anniversary. The aim of this research is to present a complete bibliometric overview of the journal and to highlight the state of the art of sustainable agriculture as an interdisciplinary field of knowledge. Scientific information databases were used to analyse 216 documents. The most influential countries, the leading and most outstanding authors and the most significant articles published in IJSAMI were studied. A complete keyword concurrence network with graphical visualisation and cluster analysis is used to identify the main trends and emerging issues to be addressed in the coming decade. Food safety, climate change, traceability and the Internet of Things, among others, are the main trends in agricultural research over the last decade.
  Keywords: agricultural; management; sustainability; IJSAMI; bibliometric; sustainability; informatics; state-of-arts; Scopus; web of science; WoS; trends.
  
  
• Exploring the relationship between locational preferences of industry and urban macroform: The case of Bursa-Turkey  
  by N. Aydan Sat, Cigdem Varol, Cansu Guller Yanar, Duygu Cayan, Emine Seyda Satilmis, Deniz Yard?m 
  Abstract: Urban agglomeration is the engine of national development, and the industrial sector is considered an essential tool in developing cities on a national and regional scale. Mainly due to the acceleration of sustainability discussions after the 2000s and the recent "green industrial transformation" approach, there is a need to develop different innovative perspectives on the industrial sector. From this point of view, this study aims to evaluate the relationship between industry and urban agglomeration from a more analytical and innovative perspective in Bursa - Turkey case. Following the introduction, a brief literature review on industrial locational preferences and urban agglomeration is conducted in the second section. The third section is focused on Bursa's industrial development and urban agglomeration process. The fourth section concentrates on developing an innovative model focusing on the relationship between industrial location preferences and urban macroform. The study is completed with the results and evaluation section.
  Keywords: industry; locational preferences; urban macroform; Bursa; Turkey.
  
  
• A comprehensive assessment of macro and micro nutrients of soil based on LSTM  
  by Rishiraj Negi, Santosh Kumar, Ankur Choudhary, Chandra Prakash 
  Abstract: The soil nutrients loss is caused by continuous crop usage, and soil erosion. Due to which the fertility of the soil decreases. Nutrient knowledge becomes crucial for efficient crop management and sustainable farming. To predict the vital elements and nutrients present in soil, such as pH level, electrical conductivity (EC), organic carbon (OC), nitrogen (N), phosphorus (P), potassium (K), magnesium (Mg), sulphur (S), zinc (Zn), iron (Fe), and copper (Cu), we have developed an LSTM-based soil nutrients prediction system. This system takes the location data (latitude and longitude) as an input. The actual and predicted dataset is derived from the real soil testing data integrated with ArcGIS. To obtain actual soil test data out of the whole dataset, an equidistant sampling is employed. Using assessment measures including mean error (ME), mean absolute error (MAE), root mean square error (RMSE), and R-squared, the developed models performance has been assessed.
  Keywords: soil nutrients prediction system; SNPS; soil nutrients; equidistance method; long short-term memory; LSTM; nonlinear spatial data.
  DOI: 10.1504/IJSAMI.2025.10070185
   
  
• Digital twins in agriculture for enhancing productivity and sustainability: a comprehensive review  
  by Dadabada Pradeep Kumar 
  Abstract: The application of digital twins (DTs) improves agricultural productivity and sustainability. These virtual replicas of physical systems facilitate testing complex agricultural processes, optimising resource utilisation, and enabling intelligent automation without disrupting live systems. This paper thoroughly examines the transformative potential of DTs in agriculture, particularly emphasising their ability to improve productivity and sustainability by comprehensively reviewing the literature. The review conducted from 2017 to 2024 indicates that DTs are being utilised in monitoring crops, farms, and machinery, the management of livestock, urban and controlled environment farming, agri-product design and machinery management, and the agri-supply value chain.
  Keywords: smart agriculture; digital twins; comprehensive review; productivity; sustainability.
  
  
• Integrating IoT and AI: a system architecture for hydroponic based controlled environment agriculture  
  by Woshan Sirimal, Mohamed Siraj, Rehana Anjum, Muhammad Raisuddin, Raja Rani Titti 
  Abstract: Controlled environment agriculture (CEA) utilises controlled spaces to cultivate fruits, vegetables, herbs, and other plants. It enables precise regulation of temperature, humidity, light intensity, CO2 levels, and nutrient delivery. While CEA enhances yield potential, it involves complex operations and high energy consumption. To address these challenges, this article presents an IoT-based system for monitoring and controlling growth factors. The system integrates eleven IoT devices, including sensors, actuators, cameras, and an energy meter, within a hydroponic environment. These devices communicate with a central Edge device for data processing. Sensors collect real-time data and publish MQTT telemetry messages, which IoT devices process to trigger actions. The Edge device, using Docker-based software modules, analyzes data and controls actuators. Selected results are sent to Microsoft Azure for further processing. A Node-RED dashboard allows manual and AI-driven control. Evaluated over one month, the system demonstrates its capability in managing CEA operations effectively.
  Keywords: IoT; controlled environment agriculture; CEA; Node-RED; Azure; agriculture; hydroponic.
  DOI: 10.1504/IJSAMI.2025.10070177
   
  
• A novel mobile application for soil health management using generative AI  
  by German Cuaya-Simbro, Ciro Canales Castillo, Emilio Raymundo Morales Maldonado, Karina Gutiérrez Fragoso 
  Abstract: We present a novel mobile application that provides automated soil care recommendations to farmers using generative artificial intelligence (GAI). The digital tool offers personalised recommendations to help farmers preserve or improve soil health, promote sustainable agriculture, and tackle issues like soil degradation caused by unsustainable practices. The application facilitates the input of qualitative users data about their agricultural soil without needing to know the technical terms quality of their soil, which is then processed using OpenAI GPT API to generate suggestions to care for or recover it. This approach leverages advanced natural language processing and machine learning to provide fast and practical advice, including crop selection and soil management improvements. Finally, we present a qualitative and quantitative comparison between expert recommendations and those generated by the GAI, with a highest similarity score of 75.31%.
  Keywords: natural language processing; NLP; automatic recommendations; soil assessment; sustainable agriculture.
  DOI: 10.1504/IJSAMI.2025.10070355
   
  
• The challenges and opportunities of adopting digital platforms for localised resources sharing in agriculture: issues, perceptions and influential factors  
  by Hayat Lionboui, Congduc Pham, Fouad Elame, El Haj El Maadoudi, Seham M.A. El Gamal, Véronique Henry, Tarik Benabdelouahab 
  Abstract: This research delves into agricultural stakeholders attitudes towards resource sharing, particularly focusing on digital platforms for localised resource sharing (DPLRS) in both regular and crisis scenarios. It showcases varied perspectives among farmers regarding DPLRS, with some acknowledging their potential benefits in enhancing agricultural processes and market exploration, while others remain skeptical or disinterested, leading to limited adoption. Barriers to adoption include lack of familiarity, trust issues, pricing concerns, operational hurdles, and limited internet access. The research underscores the significance of education level and professional affiliations in influencing adoption rates. Additionally, it notes that younger farmers and farm size also play a role in adoption decisions. Overall, the findings stress the importance of awareness, training, and institutional support in promoting DPLRS adoption, urging for comprehensive strategies and supportive policies to drive widespread adoption of digital innovations in agriculture.
  Keywords: digital platforms; resources sharing; agricultural sector; agricultural stakeholders; policy maker; adoption; resilience.
  
  
• Outlining the veterinary students' attitudes towards entrepreneurial intentions using the categorical regression  
  by Athanasios Batzios, Vagis Samathrakis, Alexandros Theodoridis, Georgia Koutouzidou, Alexandros Kakouris 
  Abstract: In this study the attitudes of veterinary students on issues of multithematic variables reflecting their entrepreneurial intentions are investigated. Using a specially designed questionnaire, a survey was carried out and 105 veterinary students were asked to indicate their agreement on certain issues. The data collected were analysed through categorical regression and the relationships between the multithematic variables of interest and specific students characteristics such as age, familys residence, parents occupation, etc. were estimated. From the estimated models, it is concluded that familys residence, respondents age and parents profession consist criteria that significantly define the students attitudes on elements both of the internal and external environment that would make it difficult for them to start a business. Overall, the findings of the study could be utilised for a targeted enhancement of the entrepreneurial intentions of veterinary students and their training and education on issues related to the labour market and entrepreneurship.
  Keywords: Veterinary students’ attitudes; categorical regression; career prospects; entrepreneurial intentions; Greece.
  
  
• Harnessing blockchain technology for agricultural supply chain innovation: a pathway to sustainability and efficiency in the Industry 5.0 era  
  by Henry Karyamsetty, Saurav Negi, Mohammad Sultan Ahmad Ansari, Shantanu Trivedi 
  Abstract: The global population is projected to 10 billion by 2050, driving an estimated 98% increase in food demand. Simultaneously, agriculture faces growing challenges from climate change, global warming, and food security. This study examines how blockchain technology can revolutionise the agricultural supply chain within the industry 5.0 framework. By leveraging secondary data from published research, this paper analyses how blockchain can improve agricultural productivity and streamline supply chain operations. The findings suggest that blockchains ability to enhance transparency, reduce food waste, prevent fraud, increase crop production, and improve supply chain traceability is driving its adoption in the food and agriculture industry. Additionally, blockchain enables secure transactions, customisable crop insurance, and risk management tools directly benefiting small-scale farmers by eliminating intermediaries and improving market access. This study emphasises the role of blockchain to guide agrarian economies toward a more transparent, efficient, and resilient agricultural supply chain for achieving long-term sustainability.
  Keywords: blockchain technology; agricultural supply chain; Industry 4.0; sustainability; supply chain innovation.
  DOI: 10.1504/IJSAMI.2025.10070987
   
  
• Accurate identification of the tomato leaf disease using convolutional neural network and human learning optimisation-based model  
  by Nidhi Goyal, Sumit Kumar, Mukesh Saraswat 
  Abstract: Advancements in machine learning and computer vision technologies are being used to detect crop diseases, improving crop production and reducing labour-intensive. Accuracy is one of the prominent issues for these technologies due to variety of crops and its diseases. Hence, the aim of this work is to propose a new model based on the shallow CNN and human learning optimisation (HLO) based technique for detecting the tomato leaf disease more accurately. The effectiveness of the proposed CNN architecture is evaluated using plant village dataset. A wide variety of performance measures are adopted for assessing the performance of the proposed architecture and simulation results are compared with several DL variants. The results demonstrated that proposed CNN architecture achieves higher accuracy (0.9824), precision (0.9724), recall (0.9772) and F1-score (0.9747) rates compared to other methods and techniques. Hence, proposed architecture can enable precise and accurate detection of tomato diseases based on tomato leaves.
  Keywords: convolutional neural network; CNN; human learning optimisation; tomato disease; crop sustainability.
  DOI: 10.1504/IJSAMI.2025.10071353
   
  
• Eco-innovation and economic performance of agricultural co-operatives: the moderating role of government support and green human capital  
  by Huong Lan Pham, Huong Thu Nguyen, Ha Thanh Nguyen, Hung Vu Nguyen 
  Abstract: In the agricultural sector, prior research on the relationship between eco-innovation and economic performance has focused on either smallholders or investor-owned firms. Empirical research that examines such relationship in agricultural cooperatives is limited. This study seeks to fill in this gap by investigating the impact of eco-innovation in agricultural cooperatives on economic performance and the boundary conditions of both external and internal factors on this relationship. The results reveal that the impact of eco-innovation on economic performance is positive and significant. Moreover, the moderating effect of government support as external factor and green human capital as internal factor on the relationship between eco-innovation and economic performance are also confirmed. This study, thus, suggests that agricultural cooperatives should invest in eco-innovation to enhance their economic performance and take efforts to leverage their green human capital. The government, on the other hand, should take actions to actively provide technical, finance and marketing supports so that agricultural cooperatives can capitalise on eco-innovation opportunities to generate sustainable economic benefits.
  Keywords: eco-innovation; economic performance; government support; green human capital; agricultural co-operatives; Vietnam.
  DOI: 10.1504/IJSAMI.2025.10071354
   
  
• AI-driven mobile agriculture ICT-based leaf disease detection for sustainable farm management  
  by Swarna Prabha Jena, Krishnakant Chaubey, Ajay Kumar Moodadla, P. Srinivasa Rao, Vikrant Shokeen, Bijay Kumar Paikaray, Sujata Chakravarty 
  Abstract: This study offers a novel approach to farm management by using the Mobile Agriculture Revolution. Our solution provides farmers instantaneous leaf disease assessment with cellular IoT technology and Android smartphones. A machine learning model trained on various leaf image datasets to accurately identify numerous crop diseases on the fingertip. Developing an intuitive and user-friendly Android application for investigating leaf diseases, data visualisation, and image capture, emphasising usability, efficacy, and accuracy as determined by field testing and user feedback. Farmers can swiftly identify and classify leaf diseases using this innovative method, allowing for prompt crop protection decision making. This paper introduces the Mobile Agriculture Revolution, which increases the productivity of farming operations and supports proactive, sustainable agricultural management in the digital era. By providing quick and precise diagnosis, this smartphone app can assist farmers in making well-informed decisions about managing illnesses, which can enhance crop productivity, lower losses, and improve food security.
  Keywords: global system for mobile communications; GSM; cellular IoT; agriculture; sustainability; mobile app.
  DOI: 10.1504/IJSAMI.2025.10071513
   
  
• Enhancing the quality of agricultural products using machine learning-based intelligent grading detection  
  by Swapnil Rajendra Desai, Manuj Joshi, Pradip Suresh Mane 
  Abstract: Detecting fruit freshness is vital in agriculture, requiring an accurate system to reduce labour costs associated with discarding spoiled produce. Although some modern machine learning techniques have shown success in fruit image classification, still some challenges persist like blurred fresh and rotten fruit images and irrelevant features. To address this, this research proposed a novel method based on stacked generalisation based extreme gradient boosting tree (SGXGBoost) with improved Water wheel plant algorithm (IWPA) for classifying fresh and rotten fruit images. Initially, leveraging with coherence shock filtering and discrete wavelet transform for image enhancement and feature extraction. The marine predators algorithm optimally selects features and finally proposed approach classifying fresh and rotten fruit images as healthy or diseased. Evaluated on fruits fresh and rotten for classification dataset and fruits 360 dataset, achieves 99.9% and 99.98% accuracy correspondingly, which highlights the effectiveness of proposed approach over other approaches in fruit freshness detection.
  Keywords: fresh and rotten fruits; ranking; stacked generalisation; agricultural products; machine learning; ML.
  DOI: 10.1504/IJSAMI.2025.10071628
   
  
• A real-time detection framework for blast fungal in rice using improved meta-heuristic algorithm for FCM and vision transformer-based deep learning  
  by Vidhya Machakandhan, Dahlia Sam, V.N. Rajavarman 
  Abstract: Most Asian countries are dependent on rice because it is a significant staple food crop and hence large quantities of rice are grown every year. A novel deep learning-based real-time rice blast fungal detection approach is suggested in this research work. By employing the internet of things (IoT), this work detects the rice blast fungal disease more effectively. The gathered images are subjected to the segmentation process, where adaptive fuzzy C-means clustering (AFCM) is employed for segmenting the abnormalities. Here, the improved running city game optimiser (IRCGO) is used for tuning the AFCM network parameters. The segmented images are applied to the classification process, where the vision transformer (ViT)-based adaptive EfficienNetB7 (ViT-AENB7) is employed. The same IRCGO is utilised to optimise the network parameters. The numerical analysis is conducted on the proposed rice blast fungal detection approach and compared with previously developed techniques to ensure the effectiveness of the system.
  Keywords: rice blast fungal disease; real-time detection; abnormality segmentation; adaptive fuzzy C-means clustering; improved running city game optimiser; vision transformer-based adaptive EfficienNetB7; ViT-AENB7.
  DOI: 10.1504/IJSAMI.2025.10071660
   
  
• The evolution of animal husbandry in Greece: a regional analysis of livestock farms.  
  by Christina Moulogianni 
  Abstract: Animal husbandry is an integral part of the Greek primary sector, shaping the agricultural landscape and contributing significantly to the national economy. The Greek economy and especially primary production went through a difficult period with the financial and economic crisis in the last decade. This paper attempts to analyse the effects of the financial crisis in Greece in the livestock sector. For this purpose, the data from the agriculture and livestock censuses concerning the number of livestock holdings and the number of animals per holding are analysed. At the same time, the geographical distribution of livestock farming in Greece is presented. The findings show that financial crisis had a substantial impact on the sustainability of livestock farms. The number of animals has decreased dramatically, with the majority of farms abandoning livestock activities. The results are useful for policy makers especially of rural development programs.
  Keywords: livestock holdings; regional analysis; sustainability; animal husbandry; Greece.
  DOI: 10.1504/IJSAMI.2025.10071738
   
  
• Engaging generation Z: digital marketing strategies for branding new technological products  
  by Dimitrios Theocharis, Georgios Tsekouropoulos, Nikolaos Papadopoulos, Greta Hoxha 
  Abstract: This study examines how branding influences generation Zs purchase intention for newly launched agricultural technology products. Given gen Zs digital engagement and preference for authenticity, sustainability, and innovation, branding plays a crucial role in shaping their purchasing decisions. The study assesses key branding elements in the adoption of smart farming technologies and agricultural informatics, including IoT monitoring, AI analytics, and automated farm management. A quantitative approach was used, with a structured questionnaire administered to 302 participants. Statistical analyses reveal that online brand experience, knowledge, and image are the strongest predictors of purchase intention, while trust and loyalty have a lesser impact. Brand awareness and engagement correlate with purchase intention but do not independently drive purchases. Businesses should enhance digital brand experiences and transparency to attract gen Z consumers. Future research should explore brandings long-term effects and the role of emerging technologies in strengthening consumer engagement.
  Keywords: gen Z; digital marketing; consumer behaviour; purchase intention; agriculture technological products.
  DOI: 10.1504/IJSAMI.2025.10072463