IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management

Abstract

The Internet of Things (IoT) has the potential to revolutionize agriculture by providing real-time data on crop and livestock conditions. This study aims to evaluate the performance scalability of wireless sensor networks (WSNs) in agriculture, specifically in two scenarios: monitoring olive tree farms and stables for horse training. The study proposes a new classification approach of IoT in agriculture based on several factors and introduces performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The study utilizes COOJA, a realistic WSN simulator, to model and simulate the performance of the 6LowPAN and Routing protocol for low-power and lossy networks (RPL) in the two farming scenarios. The simulation settings for both fixed and mobile nodes are shared, with the main difference being node mobility. The study characterizes different aspects of the performance requirements in the two farming scenarios by comparing the average power consumption, radio duty cycle, and sensor network graph connectivity degrees. A new approach is proposed to model and simulate moving animals within the COOJA simulator, adopting the random waypoint model (RWP) to represent horse movements. The results show the advantages of using the RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. The proposed framework is experimentally validated and tested through simulation, demonstrating the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. The results have several practical implications for precision agriculture by providing an efficient monitoring and management solution for agricultural and livestock farms. Overall, this study provides a comprehensive evaluation of the performance scalability of WSNs in the agriculture sector, offering a new classification approach and performance assessment metrics for stationary and mobile scenarios in 6LowPAN networks. The results demonstrate the suitability of the proposed framework for precision agriculture, providing efficient communication performance and low latency.

Internet of Things and Wireless Sensor Networks for Smart Agriculture Applications: A Survey

“…In the domain of SA, the application layer holds great significance. It supports various applications crucial for optimizing agricultural practices, enhancing yields, and promoting sustainable farming methods [97]. Farmers and analysts can easily visualize field data on their mobile devices and computers through this layer, aiding in informed decision-making.…”

“…[554]- [559] User Privacy and Consent: securing from hacking and data stolen, encompassing personal and operational data Implementing transparent data usage policies, employing robust anonymization techniques, empowering users, effective communication of the purpose of data collection. [49], [97], [560]- [564] Resilience to Attacks and Failures: failure and disrupt agricultural operations Implementing redundancy, fault tolerance mechanisms and backup systems. [33], [68], [205], [334],…”

“…SA depends significantly on the uninterrupted operation of IoT-WSNs [97], [560]. The reliability of this domain is crucial, as any compromise or failure can severely disrupt agricultural operations and impact productivity.…”

IoT-Based Agro-Toolbox for Soil Analysis and Environmental Monitoring

“…The IoT plays a crucial role in enabling smart agriculture practices and contributes to increased production efficiency by effectively measuring soil, temperature, and humidity parameters in a cost-effective manner. The development of smart IoT mobile/portable devices has revolutionized precision agriculture, by addressing the need for real-time data acquisition [ 3 ]. Traditional farming methods often rely on manual sampling or visual inspection by farmers during frequent visits, which can be time-consuming and provide limited insights into field conditions [ 4 ].…”

Internet-of-Things-Based Multiple-Sensor Monitoring System for Soil Information Diagnosis Using a Smartphone

“…Jirapond Muangprathub et al designed and developed a control system using node sensors in the crop field with data management via a smartphone and a web application; the system can send notifications through LINE API on the LINE application, and the results showed that this method was useful in agricultural applications [ 12 ]. Shadi Atalla et al proposed a new classification method for the agricultural IoT based on multiple factors and introduced performance evaluation indicators for fixed and mobile scenarios in 6LowPAN networks for precision agriculture [ 13 ]. Huan Juan et al developed a narrow band (NB) IoT-based water quality monitoring system for aquaculture ponds, using a cloud platform for data monitoring.…”