## 1 Introduction  
Maize holds a crucial position in the global cereal markets, with its demand steadily rising, especially for animal feed purposes. Major importers of maize include Mexico, the European Union, Japan, Egypt, and Vietnam. Notably, Thailand also holds significance as an importer of maize, primarily driven by the demands of the food industry [1]. The productivity of maize production poses challenges due to several factors such as water availability, seed quality, weather conditions, pests, diseases, soil management, and fertilizer nutrient content. Additionally, plant diseases [2] such as downy mildew and common rust, as well as pests such as corn armyworm [3; 4; 5] can significantly affect maize yields, leading to global supply and price fluctuations. Accurately predicting grain yield is crucial for improving crop productivity [6]. This can be accomplished through various methods, including statistical modeling, machine learning (ML) algorithms, and crop simulation models. These prediction approaches rely on historical data related to crop yields, weather patterns, soil quality, and management practices [7]. However, there is a notable research gap in the field of crop health prediction. Existing studies primarily focus on optimizing algorithmic accuracy [8] while overlooking the need for real-time applications. Therefore, there is an urgent need to develop a solution that allows for real-time monitoring and detection of factors that directly affect crop health throughout its life cycle [9]. Addressing this research gap would greatly benefit precision agriculture and support informed decision making for farmers and stakeholders in the agricultural sector [10]. Environmental remote sensing is of paramount importance in precision agriculture, offering a wide array of technologies for long-term monitoring and management of agricultural resources. Proximal remote sensing, which acquires high-resolution data from close distances, provides valuable insights into crop health, soil conditions, and ecosystem dynamics, which are indispensable for precision agriculture and informed environmental decision making [11; 12]. Remote sensing technologies prove particularly advantageous in addressing water management challenges in irrigation systems, particularly in areas affected by climate change, as they enable efficient data collection across extensive areas [13; 14]. Additionally, the research conducted by [15; 16] underscores the significance of utilizing remote sensing big data and Artificial Intelligence (AI) in achieving agricultural precision. The integration of Internet of Things (IoT) technologies enhances long-range monitoring, addressing critical aspects of precision agriculture such as soil conditions, water usage, and irrigation practices [13; 17]. Utilizing diverse sensors for data analysis plays a pivotal role in precision agriculture, providing valuable insights into crop management [18; 19]. The integration of unmanned aerial vehicles (UAVs) with remote charging capabilities offers a promising solution for wide-area sensing on large-scale farms [20]. Furthermore, the utilization of wireless sensors based on Narrowband IoT (NB-IoT) enables real-time and accurate data collection, empowering farmers with information to optimize crop growth [21]. These advancements contribute to intelligent and sustainable crop production systems, improving profitability, productivity, and environmental protection [22; 23]. This research endeavors to address a critical gap in the field of crop health prediction by presenting a groundbreaking approach that leverages the combined power of the IoT and AI [24; 25]. The innovative system proposed in this study stands as a pioneering solution that has the potential to revolutionize the agricultural landscape. At its core, this research aims to achieve the following primary objectives:  
1. Continuous crop monitoring: Develop a comprehensive and continuous monitoring system that provides real-time insights into a diverse range of environmental and crop-specific parameters.