The integration of Internet of Things (IoT) and Artificial Intelligence (AI) in plant ecology and environmental monitoring is reshaping the way ecosystems are studied, managed, and conserved. This chapter explores the synergy between IoT technologies and AI models, emphasizing their potential to revolutionize ecological research and resource management. IoT-enabled devices, including sensors and remote sensing technologies, provide real-time data on environmental conditions, while AI algorithms offer powerful tools for analyzing and interpreting these vast datasets. By harnessing predictive models, machine learning, and deep learning, researchers can gain valuable insights into ecosystem dynamics, plant health, and biodiversity conservation. Furthermore, AI-driven decision support systems (DSS) allow for the optimization of resource allocation in agriculture, forestry, and land management, fostering sustainability and ecological resilience. The chapter also addresses the challenges associated with data integration, standardization, and real-time analysis in IoT-based ecosystems, while highlighting the importance of interdisciplinary approaches for enhancing environmental policy-making. As ecological systems continue to face unprecedented pressures, the combination of IoT and AI presents a transformative approach for advancing scientific understanding and enabling more effective environmental stewardship.
The intersection of Internet of Things (IoT) and Artificial Intelligence (AI) in plant ecology and environmental monitoring has brought about a paradigm shift in ecological research, enabling researchers to monitor, analyze, and manage ecosystems in real time [1]. IoT technologies, such as sensors, remote sensing platforms, and drones, collect continuous data from various environmental variables, including soil moisture, temperature, and plant health [2]. This data provides a detailed snapshot of ecosystems at various scales, from local environments to broader, landscape-level changes. However, the true potential of this data lies in its analysis and interpretation, which is where AI comes into play [3]. AI algorithms, such as machine learning and deep learning models, are capable of processing these vast datasets, identifying patterns, and generating predictive models [4]. This combination of IoT and AI enables ecologists to understand complex ecological phenomena, predict future ecological trends, and make informed decisions that can aid in ecosystem management and conservation efforts [5].
The real-time nature of IoT systems provides unparalleled insights into dynamic environmental changes, enabling researchers to monitor ecosystems continuously [6]. Traditional ecological research often relied on periodic data collection through field surveys, which could result in gaps in understanding or missed opportunities to address emerging issues [7]. IoT overcomes these limitations by offering consistent, up-to-date data, enabling researchers to detect rapid ecological changes such as plant diseases, pest outbreaks, or shifts in biodiversity [8]. The integration of AI further enhances this capability by automating the analysis of these large data streams, identifying trends, and predicting outcomes that would be difficult to discern manually [9]. For example, AI can predict how changes in soil moisture or temperature may affect plant growth, enabling proactive measures to mitigate environmental stressors and improve ecological outcomes [10].
One of the most significant applications of IoT and AI in plant ecology is their role in biodiversity monitoring and conservation [11]. Through IoT-enabled sensors and remote sensing technologies, ecologists can track the health of ecosystems, monitor species populations, and identify areas at risk of degradation [12]. AI-powered decision support systems (DSS) can then analyze this data and offer insights into the effectiveness of conservation strategies, such as reforestation efforts, wildlife protection programs, or habitat restoration initiatives [13]. These systems can also predict future biodiversity trends based on historical and current data, allowing for the identification of high-priority conservation areas or early intervention in areas at risk of habitat loss [14]. By providing a comprehensive, data-driven approach to biodiversity conservation, IoT and AI play a critical role in safeguarding ecosystems for future generations [15].
