# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 3 Results and Discussion
### Experiments on Camera Capture Parameters and Plant/Soil Segmentation  
The A20/A6C camera used in the RICS nodes allows for the configuration of the main capture parameters, such as the brightness, contrast and white balance. On the other hand, the viewing angle can also be manually adjusted by modifying the angle of the capture box, as shown in Figure 9a.  
For this reason, some additional tests have been done to find the optimal setup of the system. The same scene was captured under different configurations of the parameters, searching for the optimal setup. Figure 14 shows an example of some of these tests for different configurations under outdoor natural lighting conditions. Since the image quality is a subjective property, the experimentation was done by trial and errors by experts in image processing.  
Figure 13: Some sample images received in the coordinator node at different distances from the remote node: (**a**) 600 m (image received without errors); (**b**) 700 m; (**c**) 950 m; (**d**) 1125 m.  
The ultimate objective of the system is to calculate the percentage of green cover (PGC) to estimate the water requirements of the crop. Therefore, the last experiment consists in analyzing the images with the plant/soil segmentation algorithm implemented in the application and presented in [52]. This algorithm uses models for plant and soil color distributions in different color spaces. The models consist of the normalized histograms of the plant pixels and the soil pixels, selected by the user during the training phase. Given a new image, the algorithm reprojects the histograms in the image, obtaining the probabilities of plant and soil for each pixel. The class with the highest probability is selected pixel-by-pixel. Finally, morphology operators _open_ and _close_ are applied to remove some false positives and false negatives. The algorithm tests 11 standard color spaces and selects the optimal space and combination of channels [13].  
To test the accuracy and robustness of the method, 10 images of the crops were analyzed using different models based on L*a*b* and III2I3 color spaces. Some sample results are presented in Figure 15. Generic color models for plants were applied in both cases.  
Figure 14: Sample images obtained with different capture configurations of the A20/A6C camera, changing the parameters of brightness and contrast: (**a**) cloudy mode; (**b**) daylight mode 1; (**c**) daylight mode 2.  
Figure 15: Plant/soil segmentation of the images obtained by the RICS nodes for a crop of lettuce. (**a–c**) are captured images. (**d–f**) are segmented images. For each image, the percentage of green cover is indicated using the L*a*b* (PGC\({}_{1}\)) and III2I3 color model (PGC\({}_{2}\)). PGC = the percentage of green cover.  
The relative error in the obtained PGCs, calculated as the average absolute difference between the PGCs produced by both models, is only 0.945%. This high accuracy allows for a precise estimation of the crop coefficient parameter, \(Kc\), which according to [8] can be obtained within a margin of error around 2%. From this coefficient \(Kc\), the water balance can be computed using the FAO-56 methodology [53], achieving an optimal management of the water resources.