# AI, IoT and Remote Sensing in Precision Agriculture
###### Abstract  
The global population is projected to reach nearly 10 billion by the end of the 21 st century, posing unprecedented challenges for agricultural systems to ensure food security while maintaining sustainability [1]. Achieving a proportional increase in agricultural production to feed this growing population is one of humanity's most pressing challenges. This goal must be pursued against a backdrop of climate change, resource depletion, and increasingly frequent extreme weather events, all of which threaten the stability of global food systems [2]. To address these challenges, the integration of advanced technologies such as automation, sensors, yield monitors, the Internet of Things (IoT), drones, and robotics is essential. These tools, combined with Geographic Information Systems (GISs), artificial intelligence (AI), highly structured mathematical models, and big data analytics, form the foundation of a global \"Digital Twin\" for agriculture [3, 4]. This conceptual framework enables the creation of virtual replicas of agricultural systems, facilitating site-specific conservation and management practices that enhance productivity, income, and global sustainability [5, 6]. Spatial analysis of agricultural data plays a pivotal role in this context, allowing for precise decision making and resource optimization [7]. By leveraging these technologies, we can develop resilient agricultural systems capable of meeting future demands while minimizing environmental impacts. Satellite and aerial imagery, along with ground-based sensors and yield monitors, provide critical insights into production variability at both macro- and micro-scales. These technologies enable the processing, representation, and modeling of vast amounts of agricultural data, facilitating precision agriculture practices [8, 9]. Spatiotemporal models, in particular, offer significant advantages over traditional spatially explicit models by capturing the dynamic interplay between space and time, which is essential for understanding complex agricultural systems [10]. Hierarchical models further enhance this capability by addressing complex interactions through the specification of parameters that vary across multiple levels, often via the introduction of random effects [11]. This approach allows for more accurate predictions and tailored management strategies, ultimately improving crop yields and resource efficiency. By integrating these advanced modeling techniques with high-resolution data, we can better address the challenges of agricultural variability and sustainability in the face of global change. The spread of transboundary plant pests and diseases, caused by fungi, bacteria, or viruses, has intensified significantly in recent years, leading to substantial agricultural losses and posing a serious threat to global food security [12, 13]. These pathogens primarily spread through human activities, wind dispersal, or vector-borne mechanisms, making their control and prediction particularly challenging [14]. The spatial patterns of these outbreaks are influenced by a complex interplay of environmental, climatic, and socioeconomic factors. For instance, changes in climate--such as increased temperatures and alteredprecipitation patterns--create favorable conditions for the proliferation and spread of pests and diseases [15; 16]. Additionally, shifts in land use, agricultural practices, and human habits further complicate our understanding of these processes, making it difficult to predict and mitigate their impacts [17]. Recent studies have highlighted the importance of hotspot mapping and regional analyses to identify areas at higher risk of pest introductions, taking into account environmental, anthropogenic, and spatial factors [18]. These approaches provide valuable insights into the drivers of pest spread and help prioritize surveillance and control efforts.