# Low-Cost Wireless Sensing System for Precision Agriculture Applications in Orchards
## 3 Results and Discussion
### Topology-Sensor Nodes Allocation  
Apart from the laboratory tests, the system was installed in a commercial orchard to evaluate its performance in real field conditions. The orchard size was 35 ha which is considered a medium sized field for tree crops in Greece and other countries in southern Europe. A basic analysis of the orchard's topology and soil spatial properties was performed to define the most appropriate positions for nodes installation. In the literature, sensor deployment was setup following a structured pattern at strategic positions in the field [23]. According to [10], the first phase in the deployment of wireless nodes in agriculture is performing an exhaustive study of the terrain in terms of geographical, topographical, and climatic points of view, for more efficient allocation. A soil map should be added to classify the field into zones.  
In our study, the soil ECa data acquired by the EM38 electromagnetic sensor, were analyzed using a fuzzy clustering algorithm. Three parameters were introduced in the management zone analysis; the ECa deep measurement (1.4 m), the ECa shallow measurement (0.65 m), and the surface elevation acquired from the RTK GPS used for georeferencing the ECa measurements. Based on the analysis, the FPI and NCE were minimal for the delineation in three zones (Figure 5). Therefore, three sensor nodes were adequate to monitor the experimental orchard, allocating one in each zone (Figure 6).  
The images acquired by the UAS were analyzed in the farm.eye module of farmB farm management information system to export the orthomosaic and the 3D point cloud of the orchard's environment (Figure 7).  
The soil management zones, defined by the spatial analysis of soil ECa and field elevation data, were overlayed on the 3D orthomosaic of the orchard. Based on the 3D point cloud, the nodes were positioned in the orchard at locations with the clearer fields of view with the Coordinator. Special care was given to setup a node in each major management zone that shows unique and uniform soil properties (Figure 8). This way, the data acquired by the sensors are representative of the specific management zone where each node is installed.  
More parameters could also be taken into account in our decision for locating the nodes, such as yield spatial variability or other crop spatial properties. However, the scope of this work was to study the properties and limitations of the developed wireless sensing system and locate the soil moisture sensors at representative locations. Thus, only communication based on the landscape and topology properties and the soil variability were taken into consideration.  
Figure 5: Diagram of the fuzziness Performance Index (FPI) and Normalized Classification Entropy (NCE) for delineating the orchard into 2–6 management zones.  
Figure 8: Sensor nodes positioning based on the zones defined by performing fuzzy clustering on the soil ECa, surface elevation data, and the field’s topology environment.  
Figure 6: Field maps from farmB software interface presenting (**a**) soil ECa scanning at 0.65 m depth, (**b**) soil ECa scanning at 1.4 m depth, (**c**) the surface elevation map of the field, and (**d**) the management zones delineated using fuzzy clustering analysis on the soil ECa and surface elevation data.  
Figure 7: The 3D point cloud as depicted at farmB.eye module based on the aerial images acquired by the UAS flying at altitude of 50 m over the orchard.