Sustainability

Oil palm is an important economic crop that is widely cultivated, especially in Southeast Asia. Thailand is one of the world’s largest producers and exporters of palm oil. Efficient management of oil palm plantations is crucial for increasing yields and minimizing agricultural losses. This study aimed to develop a smart oil palm plantation and production management system. This system utilizes Internet of Things (IoT) technology and an integrated supervised machine learning model utilizing regression analysis to monitor and control agricultural equipment within the plantation. MySQL database was used for management of sensor data. Python (version 3.9.6) programming and Google Map API were used for data analysis, spatial analysis and data visualization suite in the system. The results showed that the data from the sensors are displayed in real-time, allowing plantation managers to monitor conditions remotely and make informed adjustments as needed. The system also includes data analysis and data visualization tools for decision-making regarding production management. The model attained an accuracy of over 95%, which reflects its reliability in performing the specified prediction task. The system serves as a support tool for automating soil quality monitoring, fertilization, and field maintenance in oil palm plantations. This enhances productivity, reduces operational costs, and improves yield planning.

This study presents the development and evaluation of a Quick Response (QR) code-integrated, web-based, and GIS-supported interactive learning model designed to enhance field-based plant learning in landscape architecture education. Conducted on the Görükle Campus of Bursa Uludağ University (BUU), the research systematically inventoried 6869 individual woody plants belonging to 172 taxa, georeferenced them using GPS, and visualized the data on an interactive campus map. Unique QR codes were generated for each taxon, providing instant access to plant profiles via a web platform and the Landscape Plants mobile application. The pedagogical effectiveness of the system was evaluated through a survey administered to 158 students, yielding a high internal reliability (Cronbach’s Alpha = 0.969). The findings indicated a high level of student satisfaction and a strong positive correlation between web-based and QR code applications (r = 0.941, p ≤ 0.001). This research represents the most comprehensive campus-scale digital plant learning system in Turkey, in terms of both species diversity and individual count. It provides a scalable and sustainable smart campus model which is applicable to nature-based disciplines worldwide.

Digital transformation (DT) is reshaping cold chain operations through technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and digital twins. However, evidence remains fragmented, and a systematic synthesis focused on how these technologies affect cold chain performance, sustainability, and cost-efficiency is limited. This PRISMA-based systematic literature review analyzes 107 studies published between 2009 and 2025 to examine enabling technologies and application areas, operational and sustainability impacts, and the main adoption challenges. The reviewed evidence suggests that digitalization can improve real-time visibility, temperature control, traceability, and energy management, supporting waste reduction and improved quality assurance. Key challenges include high implementation costs and uncertain returns on investment, interoperability constraints, data governance and cybersecurity concerns, and organizational readiness gaps. The paper concludes with implications for managers and policymakers and a future research agenda emphasizing integrated multi-technology solutions, standardized sustainability assessment, and rigorous validation through pilots, testbeds, and real-world deployments to enable scalable and resilient cold chain digitalization.