# Design of an Integrated Remote and Ground Sensing Monitor System for Assessing Farmland Quality
## 3 Data Transmission Optimization Routing Protocol
### Data Transmission Scheduling
The combination of the time-varying characteristics obtained from long-term WSN sensing data and the spatially-varying characteristics obtained from high spatial resolution remote sensing images can lead to the development of accurate spatiotemporal models for the variables of interest in these applications. ## 5 Conclusions

This study addressed the shortcomings of conventional UAV-WSN systems for conducting long-term farmland quality monitoring by designing a monitoring system that facilitates the integrated and simultaneous collection of high-resolution remote sensing and long-term ground sensing data based on a newly proposed DTORP. In contrast to existing UAV-WSN systems that plan UAV flight paths according to the locations of communication nodes in the WSN, the proposed system conducts flight planning independently of the node positions by considering every sensor node in a cluster to be a potential communication node that can transmit data to the sink node of the UAV. The proposed DTORP selects the communication node within a cluster of the WSN to maximize the quantity of data that can be efficiently transmitted based on an efficient communication length algorithm. In addition, the proposed protocol combines the scheduling algorithm and routing algorithm employed for conducting data transmission between the nodes in a cluster over three distance scales, including small, medium, and long distances, in order to reduce the energy consumption incurred during the data transmission process. The performance of the improved UAV-WSN system employing the DTORP was assessed for a WSN composed of a single cluster using Monte Carlo simulations for different spatial resolution images captured by a UAV, based on comparisons with the performance obtained for a conventional UAV-WSN system employing the LEACH protocol. The simulation results demonstrated that (1) the proposed system had better overall performance for all three performance indicators than the conventional system, (2) that the DTORP facilitated the transmission of a much greater total volume of data than the LEACH protocol, (3) that the DTORP utilized sensor node energy for data transmission with much greater efficiency than the LEACH protocol, (4) and that the DTORP provided a much larger maximum revisit period than the LEACH protocol. While the proposed UAV-WSN system has achieved several improvements compared with conventional UAV-WSN systems, further studies should be conducted. In the future, we will conduct more research on improved UAV-WSN systems based on DTORP to increase the volume of transmitted data and reduce energy consumption and apply it for the monitoring of the dynamics of farmland quality using a mobile farmland quality monitoring laboratory. Future efforts could be directed in various directions. Firstly, a sleep strategy for sensor node clusters could be introduced to further reduce energy consumption. Secondly, a redundant backup or storage algorithm must be introduced, because sensor nodes may breakdown and data may be lost prior to revisitation. Finally, we note that actual UAV flight paths do not always conform to the straight lines formulated in the planning stage because of numerous factors, such as strong winds and errors in the GPS coordinates of the drone. Also, the packet losses of the WSN are not 0%, because of the influence of various factors such as vegetation and buildings. All these influencing factors are regarded as uncertainties that should be considered. To this end, an adaptive UAV flight speed adjustment method should be developed to ensure that the UAV can collect all pertinent data under any condition encountered during applications. Furthermore, we will investigate the benefits of adopting different types of UAVs and different communication methods, such as Wi-Fi and Long Range (LoRa) in different applications. Conceptualization, F.Z., G.W., Y.H., L.C. and A.-x.Z. ; Data curation, F.Z. and L.C. ; Formal analysis, F.Z. and A.-x.Z. ; Funding acquisition, Y.H. ; Investigation, F.Z. ; Methodology, F.Z. and L.C.