# IoT-Enabled Precision Agriculture: Developing an Ecosystem for Optimized Crop Management
## 6 Practical and Research Implications  
The proposed framework for integrating IoT systems in precision agriculture provides a comprehensive solution for the effective planning and implementation of IoT systems in precision agriculture applications. The proposed system effectively combines an array of sensor measurements, communication technologies, middleware, and applications to provide automated decision-making support. The experimental results obtained through simulation demonstrate the suitability of the proposed framework for both fixed and mobile scenarios, providing efficient communication performance and low latency. Additionally, the results show the advantages of using RPL protocol for routing in mobile and fixed sensor networks, which supports dynamic topologies and improves the overall network performance. This research has several practical implications for precision agriculture. First, the proposed framework provides an efficient solution for monitoring and managing agricultural and livestock farms. The system can be used to collect essential information from the fields, such as plant health and soil moisture levels, and transmit it to a specific application for analysis and decision-making. This can help farmers improve crop yields, reduce water usage, and prevent crop damage. Furthermore, automated machine learning can also be used to model the sensor data to predict the condition and needs of the crops. Additionally, this research has important research implications. The proposed framework is a promising solution for precision agriculture and can be further enhanced by incorporating more advanced technologies and techniques to improve its performance and scalability. The experimental results obtained through simulation provide a foundation for further research on the performance of IoT systems in precision agriculture. Moreover, the proposed framework can be extended to other application domains that require efficient data collection and management, such as smart cities and industrial IoT. A limitation of this methodology was the use of only simulation to validate the framework; hence, the results may not be exactly the same as those obtained in a real-world setting. However, using simulation as a validation method for the proposed framework in precision agriculture applications offers several advantages, such as the ability to realistically compare the networking performance indices of both fixed and mobile nodes using the same simulation settings. Additionally, the simulation results can be easily replicated and verified, providing a high level of confidence in the validity of the results. One potential area for future improvement in this study is the integration of automated machine learning (AutoML) techniques to model the sensor data collected by the IoT  
Figure 9: Instantaneous Power Consumption for nodes = [1, 15, 31, 40]. system. By applying AutoML algorithms, the proposed framework can automatically identify patterns and anomalies in the sensor data and predict the condition and needs of the crops. This can help farmers make more accurate and timely decisions about crop management, such as when to water, fertilize, or apply pesticides. Additionally, by automating the modeling process, the proposed framework can provide more accurate predictions with less human intervention, reducing the time and resources required for manual data analysis. Overall, the integration of AutoML techniques can help to improve the performance and scalability of the proposed framework, making it even more useful for precision agriculture applications. Future improvements include testing the proposed framework in a real-world setting to validate the simulation results, as well as investigating the use of more advanced technologies and techniques, such as machine learning, to improve the performance and scalability of the proposed framework.