Anek Putthidech

Rice cultivation in climate-sensitive regions necessitates adaptive irrigation and nutrient management strategies to enhance resource utilization efficiency and mitigate operational uncertainty. This study investigated the operational feasibility of an Internet of Things (IoT)-based monitoring and recommendation system for real-time soil moisture and nutrient-related operational monitoring in large-scale rice farming environments in Thailand. An integrated IoT-assisted monitoring and recommendation framework comprising sensing, communication, analytics, and recommendation components was developed and evaluated under practical field-deployment conditions. The system incorporated soil moisture monitoring and nutrient-related operational sensing, cloud-based data processing, machine learning-assisted prediction, and mobile notification services to support irrigation and fertilizer management. A comparative evaluation between conventional and IoT-assisted management conditions revealed lower irrigation water use (947.38 vs. 7638.38 m³/ha), reduced fertilizer utilization (41.40 vs. 347.56 kg/ha), and lower production costs (4230.88 vs. 30,664.69 THB/ha) under IoT-assisted conditions. Average profit also increased from 2357.68 to 23,920.00 THB/ha. User evaluation indicated high overall satisfaction (mean = 4.28/5.00). The findings suggest that integrating IoT-based sensing, machine learning-assisted prediction, and optimization-driven recommendation workflows within a unified field-deployment framework may improve adaptive irrigation management, resource-allocation efficiency, and operational decision support under climate-sensitive rice cultivation environments.

Rice farming in Central Thailand relies heavily on controlled irrigation, making efficient water management critical for sustainable production. This study examines how production input costs influence irrigation water demand, measured as irrigation water volume per unit area (m3∙ha−1), using panel data from 215 rice farmers in Sena District, Phra Nakhon Si Ayutthaya Province, covering the period 2016–2023. The dataset comprises 1,720 farm-year observations across approximately 7.58 km2 of irrigated area. A log-linear Cobb–Douglas model estimated using panel regression techniques is applied to quantify the elasticities of irrigation water demand with respect to production input costs. Because all variables are expressed in logarithmic form, the estimated coefficients are directly interpretable as elasticities. The results indicate that fertiliser (β = 0.210), pesticide (β = 0.165), machinery (β = 0.302), and fuel costs (β = 0.128) have positive and statistically significant effects on irrigation water demand per rai, whereas seed and labour costs are not statistically significant. For example, a 1% increase in fertiliser expenditure is associated with a 0.21% increase in irrigation water demand per rai, holding other factors constant. The model explains a substantial share of the variation in irrigation water demand (R2 = 0.63), indicating strong explanatory power. The findings highlight that chemical input use and mechanisation are key drivers of irrigation intensity in rice farming. These results underscore the importance of integrated water–input management strategies, including improved nutrient scheduling, energy- efficient pumping technologies, and data-driven irrigation planning, to enhance water-use efficiency and support sustainable rice production in Thailand.

Higher education is increasingly integrating artificial intelligence (AI) and sustainability-oriented learning, aligning with the United Nations Sustainable Development Goals (SDGs). Nonetheless, there is limited empirical evidence explaining how cognitive processes, AI-supported learning (AI-SL), and SDG awareness (SDGA) jointly relate to learner engagement (ENG) and learning outcomes (LO) across academic disciplines. This study examines the associations among working memory (WM), metacognition (MET), reasoning ability (REA), AI-SL, SDGA, ENG, and LO in higher education. Survey data were collected from undergraduate students across nine campuses of a public university system in Thailand. Multi-group structural equation modeling (MG-SEM) was employed to compare students enrolled in STEM and social science programs. Measurement model evaluation indicated satisfactory reliability and convergent and discriminant validity, with partial scalar invariance supported across groups, enabling meaningful structural comparisons. The results showed that ENG was strongly associated with LO in both groups. MET emerged as the strongest cognitive correlate of ENG overall, while REA was more strongly associated with ENG among STEM students, and SDGA showed a stronger association among social science students. AI-SL was positively associated with ENG and LO, with discipline-specific differences in the strength of the effect. The model explained substantial variance in ENG (R² = 0.48) and LO (R² = 0.52). These findings emphasize the value of integrating cognitive processes (WM, MET, and REA), AI-SL, and sustainability-oriented content within discipline-sensitive instructional designs in higher education.

This study explores the interconnections between new farming practices, smart agricultural technology, mathematical skills, data-driven decision-making, and community learning in areas commonly affected by drought. Using a statistical method known as Structural Equation Modeling (SEM) and data from 320 farmers, the study explores how new farming ideas encourage smart practices that improve math skills. It also demonstrates how smart farming creates an environment where data helps inform decision-making, which benefits community learning. The results indicate that New Theory Agriculture (NT) encourages Smart Agriculture (SA) engagement, thereby facilitating both Mathematical Literacy (ML) and Data-Driven Decision-Making (DD). Engagement in SA is closely linked to improvements in ML, which, in turn, strengthen DD abilities. ML plays a central role by serving as a bridge between SA and DD, which, in turn, directly affects Community Learning Outcomes (CL). The findings show that NT fosters community-level outcomes by first building SA and ML, both of which shape DD and ultimately enhance CL, clarifying the sequence of concept connections. The findings reveal that implementing NT and smart technology in agriculture systematically enhances farmers’ resource management and the evolution of mathematical and data skills beyond formal education. The research demonstrates how cognitive skills, technological participation, and collective learning are linked within the community: NT leads to SA engagement, which develops ML, enables DD, and produces CL. The study discusses implications for community education, digital agriculture policy, and rural capacity development, suggesting that future longitudinal or experimental studies could clarify how these connections change over time.

Artificial intelligence (AI) can manage tourism by optimizing, personalizing the experience, and enhancing user interactions. This research presents the Ayutthaya tourism platform independent model (ATPiM), an intelligent tourism application that integrates a domain-specific language (DSL) designed for chatbot development with machine learning algorithms that generate personalized recommendations based on user preferences, historical data, and real-time contextual influences. This pre-experimental design measures performance on parameters such as response time, recommendation accuracy, and system latency. The outcomes indicate that the mean time taken to respond to a user's query was 2.3 seconds, with 88.5% recommendation accuracy, and no latency. The AI-based recommendation system achieved 89.7% accuracy at destinations, 87.2% at accommodations, 90.3% at itineraries, and 85.6% at activities, with corresponding recalls of 85.4%, 83.5%, 88.1%, and 80.2% respectively. Although these results are promising, a 6.2% error rate for the advanced search, along with data security are some of the remaining issues. The findings reveal that the development of new user-centric and sustainable solutions for tourism, which leverage state-of-the-art natural language processing approaches, can enhance data security and provide additional new technologies, such as augmented reality (AR) and blockchain, for use in tourism.

This research evaluates how students’ perceptions of artificial intelligence (AI)-based learning tools affect calculus education by examining the middle factors between cognitive and emotional student responses. Research built upon cognitive load theory and social cognitive theory evaluates how students perceive AI adaptability, feedback accuracy, trust, and pedagogical value because these perceptions alter their learning processes as well as their academic outcomes. University professors from nine Rajamangala Universities of Technology in Thailand provided data through structured questionnaires to the research study. The researchers used partial least squares structural equation modeling (PLS-SEM) to evaluate their proposed theoretical connections. Results show that student perceptions regarding AI-based learning directly affect cognitive-emotional responses, and these responses powerfully determine academic achievements. The predictive model detects 75.7% of learning outcome variance in student performance (R² = 0.757), which demonstrates robust model specification and documentation. Research revealed that emotional and cognitive outcomes serve to bridge AI-based learning perceptions and educational outcomes, thereby demonstrating their importance in the learning achievement process. The research emphasizes the need to build AI learning platforms that enhance students’ engagement alongside their resilience and confidence because these elements drive academic achievement in calculus and STEM subjects.