4. Control maps of central pivot motion speed

The development of the crop is evidenced captured by the images throughout the crop, and the information contained in this sensing is the NDVI values. By analyzing the information contained in Figure 13, it is possible to verify the similarity between the values attributed to Kc. It is noticed that as the crop evolves, the greater the exposure of the leaf area, and thus it is possible to establish a relation of NDVI. This process is described in [56], with ratios to calculate the base crop coefficient (Kcb) for cotton as a function of NDVI. When we look closely at each stage of the development of the plantation, two distinct areas are noticed: one with

Figure 13. Variation of NDVI in one crop cycle.

little growth and the other with normal growth. From this type of differentiation, it is possible to construct water demand maps, as well as speed control maps.

Data contained in this remote sensing are described in Table 4. In this configuration, the table presents the preprocessed data, near-soil temperature, soil moisture, and NDVI, besides latitude and longitude.

#### 4.1 Case study on June 15th, 2016

Canopy temperature, near-soil moisture and NDVI data, analyzed and processed, will be the set of inputs for the intelligent fuzzy system. The following are illustrated in Figure 14: (a) NDVI images, (b) temperature images, and (c) soil moisture images.

The intelligent system gave the result shown in Figure 15, where it is possible to verify different regions within the area, with different values for the pivot rotation speed. The indirect relationship between the pivot rotation speed and the level of the applied water depth implies a smaller applied water depth in a higher speed, and

Input data from the fuzzy inference system, (a) NDVI data, (b) canopy temperature data, (c) near-soil

Lat Long NDVI (%) Near-soil moisture (%) Canopy temperature (°C)

Integrating Remote Sensing Data into Fuzzy Control System for Variable Rate Irrigation Estimates

15.2463 54.0157 5.96 25.75 30.75 15.2463 54.0156 6.49 25.68 30.24 15.2463 54.0154 6.67 25.68 30.03 15.2463 54.0153 6.85 25.68 30.19 15.2463 54.0152 6.66 25.8 30.63 15.2463 54.015 6.47 25.92 30.67 15.2463 54.0149 6.82 25.84 30.44 15.2463 54.0142 7.01 25.77 29.33 15.2463 54.0141 6.37 25.88 29.58

DOI: http://dx.doi.org/10.5772/intechopen.87023

Table 4. Pre-processed data.

Figure 14.

45

moisture data.

The inputs, as shown in Figure 14, are arranged according to the linguistic variables of the fuzzy system and separated by tonalities for better visualization.

Integrating Remote Sensing Data into Fuzzy Control System for Variable Rate Irrigation Estimates DOI: http://dx.doi.org/10.5772/intechopen.87023


#### Table 4.

Pre-processed data.

#### Figure 14.

little growth and the other with normal growth. From this type of differentiation, it

Data contained in this remote sensing are described in Table 4. In this configuration, the table presents the preprocessed data, near-soil temperature, soil mois-

is possible to construct water demand maps, as well as speed control maps.

Irrigation - Water Productivity and Operation, Sustainability and Climate Change

Canopy temperature, near-soil moisture and NDVI data, analyzed and processed, will be the set of inputs for the intelligent fuzzy system. The following are illustrated in Figure 14: (a) NDVI images, (b) temperature images, and (c) soil

The inputs, as shown in Figure 14, are arranged according to the linguistic variables of the fuzzy system and separated by tonalities for better visualization.

ture, and NDVI, besides latitude and longitude.

4.1 Case study on June 15th, 2016

Variation of NDVI in one crop cycle.

moisture images.

44

Figure 13.

Input data from the fuzzy inference system, (a) NDVI data, (b) canopy temperature data, (c) near-soil moisture data.

The intelligent system gave the result shown in Figure 15, where it is possible to verify different regions within the area, with different values for the pivot rotation speed. The indirect relationship between the pivot rotation speed and the level of the applied water depth implies a smaller applied water depth in a higher speed, and

Figure 15. Control map of pivot rotating speed, (a) speed control map, (b) pivot turning speed setpoints.

a higher water application in the soil in a lower rotation speed [98]. When analyzing the input data, it is possible to identify two large areas with a lower leaf development, which may indicate a lack of water for development. After processing this input data, the intelligent irrigation system indicates that these areas with lower leaf development, in a redder color, indicate that the pivot should reduce its speed and thus increase the water depth in that area.

The expected result is the creation of control maps, and in this case, it was possible to determine the speed reference values for the eight zones initially programmed. The areas that presented different coloration in Figure 14 are in the control map result. It is possible to verify well divided zones, and in each one there is a determined value for the speed that the pivot must develop to decrease or increase the water depth in the cropped area. The result shown in Figure 15b corresponds to the reference values that should be sent to the pivot controller, since the control systems of these devices work with percentage of rotation speed.

#### 4.2 Case study on June 28th, 2016

The data analyzed and processed by the GIS were used as inputs to the intelligent fuzzy system. They are illustrated in Figure 16: (a) NDVI images, (b) temperature images and (c) soil moisture images.

Similar to the previous case study, the study of June 28 presents the values of the input variables of the fuzzy system with the linguistic definitions necessary for interpretation. The results of the intelligent irrigation system are shown in Figure 17, where is also possible to observe different regions within the crop area, with different values for the pivot rotation speed. A higher speed of rotation implies a smaller applied water depth, and with a lower speed of rotation, there is a greater application of water to the soil, if the application flow is kept constant by the sprinklers.

When comparing satellite images once again, it is seen that NDVI and canopy temperature are essential for the decision-making of the intelligent irrigation system. It is possible to see that there are large areas with a lower leaf development, which may indicate a lack of water for development. In the case of intelligent irrigation system output, areas in a redder color indicate that the pivot should slow down.

The expected result is the creation of the control maps, and for this study it was possible to find the reference values of the central pivot rotation speed for the eight irrigation zones initially programmed, shown in Figure 17. In this result, it is also possible to identify the areas that presented different colors in Figure 16.

The irrigation management zones are fairly divided, and in each one a value is determined for the pivot rotation speed, decreasing or increasing the water depth applied to the crop area. The result in Figure 17b corresponds to the reference

Pivot rotating speed control map, (a) speed control map, (b) pivot rotation speed setpoints.

Input data from the fuzzy inference system, (a) NDVI data, (b) canopy temperature data, c) near-soil

Integrating Remote Sensing Data into Fuzzy Control System for Variable Rate Irrigation Estimates

DOI: http://dx.doi.org/10.5772/intechopen.87023

values to be sent to the pivot controller.

Figure 16.

moisture.

Figure 17.

47

Integrating Remote Sensing Data into Fuzzy Control System for Variable Rate Irrigation Estimates DOI: http://dx.doi.org/10.5772/intechopen.87023

Figure 16.

a higher water application in the soil in a lower rotation speed [98]. When analyzing the input data, it is possible to identify two large areas with a lower leaf development, which may indicate a lack of water for development. After processing this input data, the intelligent irrigation system indicates that these areas with lower leaf development, in a redder color, indicate that the pivot should reduce its speed and

Control map of pivot rotating speed, (a) speed control map, (b) pivot turning speed setpoints.

Irrigation - Water Productivity and Operation, Sustainability and Climate Change

The expected result is the creation of control maps, and in this case, it was possible to determine the speed reference values for the eight zones initially programmed. The areas that presented different coloration in Figure 14 are in the control map result. It is possible to verify well divided zones, and in each one there is a determined value for the speed that the pivot must develop to decrease or increase the water depth in the cropped area. The result shown in Figure 15b corresponds to the reference values that should be sent to the pivot controller, since the control systems of these devices work with percentage of rotation speed.

The data analyzed and processed by the GIS were used as inputs to the intelligent fuzzy system. They are illustrated in Figure 16: (a) NDVI images, (b) temper-

Similar to the previous case study, the study of June 28 presents the values of the

input variables of the fuzzy system with the linguistic definitions necessary for interpretation. The results of the intelligent irrigation system are shown in

Figure 17, where is also possible to observe different regions within the crop area, with different values for the pivot rotation speed. A higher speed of rotation implies a smaller applied water depth, and with a lower speed of rotation, there is a greater application of water to the soil, if the application flow is kept constant by the

When comparing satellite images once again, it is seen that NDVI and canopy temperature are essential for the decision-making of the intelligent irrigation system. It is possible to see that there are large areas with a lower leaf development, which may indicate a lack of water for development. In the case of intelligent irrigation system output, areas in a redder color indicate that the pivot should slow

The expected result is the creation of the control maps, and for this study it was possible to find the reference values of the central pivot rotation speed for the eight irrigation zones initially programmed, shown in Figure 17. In this result, it is also

possible to identify the areas that presented different colors in Figure 16.

thus increase the water depth in that area.

4.2 Case study on June 28th, 2016

sprinklers.

Figure 15.

down.

46

ature images and (c) soil moisture images.

Input data from the fuzzy inference system, (a) NDVI data, (b) canopy temperature data, c) near-soil moisture.

Figure 17. Pivot rotating speed control map, (a) speed control map, (b) pivot rotation speed setpoints.

The irrigation management zones are fairly divided, and in each one a value is determined for the pivot rotation speed, decreasing or increasing the water depth applied to the crop area. The result in Figure 17b corresponds to the reference values to be sent to the pivot controller.
