Sustainable development is of growing importance to the agriculture sector because the current lacking utilization of resources and energy usage, together with the pollution generated from toxic chemicals, cannot continue at present rates. Sustainability in agriculture can be achieved through using less (or no) poisonous chemicals, saving natural resources, and reducing greenhouse gas emissions. Technology applications could help farmers to use proper data in decision-making, which leads to low-input agriculture. This work focuses on the role of smart technology implementation in sustainable agriculture. The effects of smart technology implementation are analyzed by using a case study approach. The results show that the plant factory using intelligence technology enhances sustainability performance by increasing production productivity, product quality, crop per year, resource use efficiency, and food safety, as well as improving employees’ quality of life.

Modern agricultural technology management is nowadays crucial in terms of the economy and the global market, while food safety, quality control, and environmentally friendly practices should not be neglected. This review aims to give perspectives on applying big data analytic and modern technologies to increase the efficacy and effectiveness of the coffee supply chain throughout the process. It was revealed that several tools such as wireless sensor networks, cloud computing, Internet of Things (IoT), image processing, convolutional neural networks (CNN), and remote sensing could be implemented in and used to improve the coffee supply chain. Those tools could help in reducing cost as well as time for entrepreneurs and create a reliable service for the customer. It can be summarized that in the long term, these modern technologies will be able to assist coffee business management and ensure the sustainable growth for the coffee industry.

In this work, operational performance in the green supply chain management (SCM) of the Thai auto parts industry was investigated. A green supply chain performance measurement (GSPM) model was developed from the combination of various concepts including an SCM logistics scorecard, a supply chain operations reference model, a balance scorecard, and green supply chain management. The GSPM has been designed for use as a self-evaluation tool focusing on five decisive areas, or factors, and 28 sub-factors. A factor analysis was conducted using the survey results of the GSPM in order to identify significant factors that represent the green supply chain operation performance. Grouped as three major factors, namely green procurement, green transportation, and green manufacturing; reverse logistics and eco-design; and reuse and recycle of manufacturing, their significance and impact on the auto parts industry in Thailand were highlighted. Specifically, the factor of green procurement, green transportation, and green manufacturing, as major factor 1, in relation with the factor of reverse logistics and eco-design, as major factor 2, were found to have a strong positive relationship with the asset turnover ratio.

Crop yield and its prediction are crucial in agricultural production planning. This study investigates and predicts arabica coffee yield in order to match the market demand, using artificial neural networks (ANN) and multiple linear regression (MLR). Data of six variables, including areas, productivity zones, rainfalls, relative humidity, and minimum and maximum temperature, were collected for the recent 180 months between 2004 and 2018. The predicted yield of the cherry coffee crop continuously increases each year. From the dataset, it was found that the prediction accuracy of the R² and RMSE from ANN was 0.9524 and 0.0784 tons, respectively. The ANN model showed potential in determining the cherry coffee yields.

Purpose: This study aims to provide a measurement model, and the underlying constructs and items, for Logistics 4.0 in manufacturing companies. Industry 4.0 technology for logistics processes has been termed Logistics 4.0. Logistics 4.0 and its elements have seen varied conceptualizations in the literature. The literature has mainly focused on conceptual and theoretical studies, which supports the notion that Logistics 4.0 is a relatively young area of research. Refinement of constructs and building consensus perspectives and definitions is necessary for practical and theoretical advances in this area.Design/methodology/approach: Based on a detailed literature review and practitioner focus group interviews, items of Logistics 4.0 for manufacturing enterprises were further validated by using a large-scale survey with practicing experts from organizations located in Central Europe, the Northeastern United States of America and Northern Thailand. Exploratory and confirmatory factor analyses were used to define a measurement model for Logistics 4.0.Findings: Based on 239 responses the exploratory and confirmatory factor analyses resulted in nine items and three factors for the final Logistics 4.0 measurement model. It combines “the leveraging of increased organizational capabilities” (factor 1) with “the rise of interconnection and material flow transparency” (factor 2) and “the setting up of autonomization in logistics processes” (factor 3).Practical implications: Practitioners can use the proposed measurement model to assess their current level of maturity regarding the implementation of Logistics 4.0 practices. They can map the current state and derive appropriate implementation plans as well as benchmark against best practices across or between industries based on these metrics.Originality/value: Logistics 4.0 is a relatively young research area, which necessitates greater development through empirical validation. To the best of the authors knowledge, an empirically validated multidimensional construct to measure Logistics 4.0 in manufacturing companies does not exist.

Sustainable urban development is an increasingly important concept as cities around the world continue to grow and face challenges related to urbanization, including environmental degradation, social inequality, and economic instability. Chiang Mai is a rapidly growing city in Thailand that steers towards sustainability. In this work, we examine the state of sustainable urban development in Chiang Mai by analyzing various indicators, including transportation, waste management, air quality, and energy consumption. A multi-dimensional framework was used to assess the sustainability of Chiang Mai. Our analysis suggests that while Chiang Mai has made progress in some areas of sustainable urban development, such as promoting green transportation and waste reduction and reduction consumption, there are still many challenges to be addressed, particularly in air pollution, water management, transportation and energy consumption. We conclude by discussing the implications of our findings for policy makers, urban planners, and other stakeholders interested in promoting sustainable urban development. This study assesses Chiang Mai’s sustainability using a comprehensive set of 35 indicators. It was found that Chiang Mai has potential for sustainable development, but some indicators require improvement. The study presents guidelines for prioritizing sustainable development, improving air and water quality, promoting sustainable tourism, enhancing waste management, transportation management and energy consumption. This research provides alternative framework for evaluating cities and presents a valuable contribution to the field of sustainable urban development.