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Building a Sustainable Development Education System for Large Organizations Based on Artificial Intelligence of Things
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Building a Sustainable Development Education System for Large Organizations Based on Artificial Intelligence of Things

Hsin-Te Wu (National Taitung University, Taiwan), Jie-Xin Li (National Taitung University, Taiwan), and Mu-Yen Chen (National Cheng Kung University, Taiwan)
Copyright: © 2024 | Pages: 19
DOI: 10.4018/JOEUC.361716

Abstract

This paper proposes building a sustainable development computing system for large organizations based on AI and IoT. The system leverages AI to calculate the carbon emissions caused by large organizations' activities and utilizes IoT devices to monitor and compute environmental coefficients. The system also employs automated devices to achieve net-zero carbon emissions. By integrating weather forecast information from meteorological agencies to understand external environmental conditions, and by consulting a knowledge database to devise appropriate response strategies, the AI system can activate relevant equipment to improve both the organization's living environment and carbon emission processes. The feasibility and practical application of this system will be demonstrated through actual simulations to enhance its viability and effectiveness.
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Introduction

Currently, agriculture is facing a significant challenge due to an aging agricultural workforce. This situation is primarily due to the low profitability of farming and the increased risk of crop losses resulting from climate change. Additionally, many countries are transitioning toward becoming developed nations, which often involves converting agricultural land into industrial use, leading to a gradual shortage of food supply. According to a report (FAO et al., 2022), in 2020, over 800 million people worldwide faced hunger. The COVID-19 pandemic exacerbated this situation, causing economic downturns in many economically deprived countries, which worsened food shortages and resulted in the highest number of hungry people in the last 20 years. Furthermore, the 2006 report by the Food and Agriculture Organization of the United Nations (Liu et al., 2018) indicated that climate change and human development activities have triggered a global food crisis. Additionally, pests and diseases contribute to the substantial damage to crops. As many agricultural countries gradually transform into industrial nations, much farmland is being converted to industrial use. With the global population expected to grow rapidly, according to UN statistics, the food crisis is expected to worsen as the population increases while food supplies diminish. Therefore, it is crucial for countries to engage in agricultural research or implement better policies to increase crop yields.

Currently, agricultural practitioners primarily rely on experience-based practices for cultivation; however, these methods are ineffective in coping with extreme climate variations, leading to crop damage. In Taiwan, where arable land is limited, and labor costs are high, approximately 70% of the food supply depends on imports to meet demand. It is imperative for Taiwan to prepare strategic responses to address the impending food crisis, and smart agriculture is seen as a critical tool in solving it.

Agriculture holds a significant position in the global economy. Data shows that 6.4% of the world's gross domestic product comes directly from the agricultural sector, highlighting not only the importance of agriculture in economic activities but also its crucial role in the global food supply chain (Introduction of the 2019 CDP (Committee for Development Policy) Report at the Ecosoc High-Level Segment, 2019). Due to the current scarcity of water resources, it is estimated that by 2050, approximately 52% of the world's population will live in water-stressed regions (Sustainable Development Goals, 2017). Agriculture, which requires substantial water resources for irrigation, necessitates the use of precision systems in smart agriculture to optimize resource usage and prevent waste. Additionally, precision systems must consider related pollution indices. For example, overuse of pesticides can lead to environmental pollution in surrounding areas; therefore, a precision system can employ drones to control the range and dosage of pesticide application.

This paper focuses on developing a hydroponic intelligent IoT system based on omnipresent AI. The proposed system utilizes affordable IoT sensors to monitor the agricultural environment and activates automated equipment based on current environmental parameters and predicted future conditions. Since each environmental factor is interrelated (e.g., a rise in temperature that affects plant growth would trigger fan equipment to reduce temperature), the precision agriculture system must account for not only plant-specific environmental factors but also the impacts of climatic conditions. The proposed system integrates sensor data and meteorological factors to estimate plant growth conditions, ensuring that plants can be maintained in an optimal growth environment.

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