# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 2 Related Works  
There has been a growing interest in understanding the best approaches for integrating IoT solutions within the agricultural sector. This interest can be broadly categorized into three stages: (1) Vertical studies that examine the best practices and technical guidelines for using IoT-enabling technologies such as wireless sensor networks (WSNs), gateways, communication platforms, and middleware in production farms [3; 11; 12]. (2) Horizontal studies that investigate approaches to bridge gaps among the vertical studies by discussing standardization, interoperability issues, large-scale prototyping, and system of systems approach [1]. (3) Business-driven studies that focus on integrating the entire chain of agriculture, wherein social and technical guidelines and frameworks are considered, such as farm-to-fork and traceability for food-chain-related studies [13]. IoT and AI state-of-the-art solutions in agriculture have been discussed in [14; 15]. The RPL protocol has been widely investigated in vertical studies focusing on several IoT sectors and applications (e.g., home and building automation, urban environments, and industrial applications). However, few research studies have examined how RPL behaves in other application domains, such as agriculture. For example, the authors in [16] examined the behavior of RPL for precision agriculture using a methodology similar to that used in this work; they proposed an extension model and presented preliminary network performance measurements of RPL. This study further investigates and demonstrates how the RPL behaves in two precision agriculture use cases (i.e., stationary and mobile). Many attempts have been made to build functional architectures for IoT applications suitable for plant and crop field monitoring and management [13; 17; 18]. Sensors for collecting information about the environment, soil, and water level are combined with geographical information systems (GISs). The captured parameters are stored in centralized databases using data management software. Web and mobile applications provide farmers with easy access to these databases. Several studies have proposed using IoT applications for monitoring animals, such as using sensor readings to track their health, behavior, and nutrition. These readings can be transferred to a centralized server for storage and further processing, providing valuable insights to farmers and animal trainers. They can then use this information to optimize their cost and time investments and achieve more value-added products [19; 20; 21; 22; 23; 24; 25; 26]. Similarly, various proposed IoT systems have been proposed for monitoring peat forests, such as environmental conditions and potentially managing disasters. These systems often use solar power and communicate with monitoring centers through LoRa networks. Additionally, some studies have proposed using high-resolution satellite imagery to identify changes in forest viability and detect foliar diseases. These systems aim to provide early warning systems for fires, pest control, or deforestation [27; 28; 29; 25]. In livestock farming, a wide range of factors to consider, such as wool and skin, depending on the type and number of farm animals. Studies have proposed solutions such as support systems for disease diagnosis and treatment, non-contact temperature measurement for early disease detection, and IoT monitoring systems for tracking animal behavior and health in large-scale pig farms. These systems can provide animal health recommendations to farmers in rural areas where veterinary access is difficult [30; 31; 32; 33]. This study examined the performance of wireless sensor networks (WSN) in two different agricultural scenarios: olive tree farms with fixed-position sensor nodes and a horse training stable with mobile sensor nodes. The sensors were used to monitor the physical condition of the horses, aiming to improve their health and well-being while also collecting high-precision measurements.