**3. Evaluating the SmartIrrigation™ vegetable application in tomatoes and watermelons**

#### **3.1. SmartIrrigation™ vegetable application performance in tomatoes**

Studies conducted during the 2016 and 2017 spring growing seasons in Georgia compared the new VegApp to currently recommend WB-based methods as well as an SMS-based system. Total water use, yield, irrigation water use efficiency (IWUE), soil moisture status, and plant macronutrient content in tomato "Red Bounty" (HM Clause, Davis, CA) were measured.

**2.3. Smartphone irrigation technologies**

and ETo

calculated using K<sup>c</sup>

40 Irrigation in Agroecosystems

methods [25].

ETo

. Then ETc

Recently, a suite of smartphone-based irrigation scheduling tools, which use real-time ET<sup>o</sup>

from statewide weather station networks, were developed [24]. Called SmartIrrigation™ Apps [24], these tools use meteorological parameters to determine irrigation schedules based on ETc

cations for avocado (*Persea americana*), citrus, strawberry (*Fragaria × ananassa*), cotton, turfgrass, and several vegetables. Prior studies have reported that the applications have performed well for citrus in Florida and cotton in Georgia [23, 25]. Migliaccio et al. [25] reported up to a 37% reduction in water use for growers using the SmartIrrigation™ Citrus App. in Southern Florida. SmartIrrigation™ applications developed for turfgrass management evaluated in Southern Florida were found to improve water savings of up to 57% compared to traditional methods [26]. The use of SmartIrrigation™ Cotton App resulted in the reduction of water used for irrigation by 40–75% with concomitant 10–25% increases in yield in Georgia when compared to the WB-based method recommended for cotton by the University of Georgia Cooperative Extension Service. The SmartIrrigation™ Cotton App also performed well when compared to SMS-based

The SmartIrrigation™ Vegetable App (VegApp) generates irrigation recommendations based on real-time weather for vegetables. The VegApp currently can be used to schedule irrigation for multiple crops including tomato (*Solanum lycopersicum*), cabbage (*Brassica oleracea var. capitata*), squash (*Cucurbita pepo*), and watermelon (*Citrullus lanatus*). The weather data are retrieved from the Florida Automated Weather Network or the University of Georgia Automated

radiation, wind speed, and relative humidity measurements using the FAO Penman-Monteith Equation [23]. Each new field registered in the VegApp by a user is automatically associated with the closest weather station; however, the user has the option to select any of the other

drip-irrigated crop grown on plastic mulch [27, 28]. The VegApp may then provide an irrigation schedule for the subsequent 2 weeks. The user can recalculate requirements at any time to devise a weekly or even daily irrigation schedule. The irrigation schedule is provided to the user as an irrigation run time per day. Additional model variables used by the VegApp to schedule irrigation include crop, row spacing, irrigation rate, irrigation system efficiency, and planting date. The VegApp differs from other applications in the SmartIrrigation™ suite, in that it does not account for precipitation or soil type as it is designed for use with vegetables

Studies conducted during the 2016 and 2017 spring growing seasons in Georgia compared the new VegApp to currently recommend WB-based methods as well as an SMS-based system.

grown in a drip irrigation and raised-bed plastic mulch production system [23].

**3. Evaluating the SmartIrrigation™ vegetable application in** 

**3.1. SmartIrrigation™ vegetable application performance in tomatoes**

Environmental Monitoring Network and are used to calculate ETo

a weeks-after-planting model of crop maturity [27, 28]. The K<sup>c</sup>

available weather stations. The VegApp uses ET<sup>o</sup>

is estimated using K<sup>c</sup>

**tomatoes and watermelons**

x *Kc*

in the following relationship: *ETc = ETo*

data

. The suite includes appli-

from air temperature, solar

curve for tomato is based on a

from the prior 5 d to calculate an average

curves developed by The University of Florida based on

Results of studies conducted with tomatoes in Georgia over 2 years suggested that the weather conditions during the growing season can influence the relative performance of the VegApp. Results from the 2016 growing season showed that the WB-based method of irrigation used the most water, followed by plants grown using the VegApp and SMS-based irrigation (**Table 1**). The SMS irrigation method used the least amount of water in 2016, which was similar to results obtained in other studies evaluating the impact of tensiometers for irrigation scheduling [29]. In 2016, plants grown with the VegApp utilized less water than the WB method, suggesting that applying real-time ET<sup>o</sup> values obtained by nearby weather stations may be more efficient than using historic ETo values [28] in some seasons. Irrigation volumes in the second year of the study were lower than the first year levels for WB and VegApp-based irrigations. There were two likely causes for the increase in water use for the SMS-based and VegApp methods relative to the WB method in 2017. In 2017, the VegApp accounted for higher levels of ET<sup>c</sup> in the earlier growing season than historic ETo values. In addition, there were several significant rain events late in the 2017 growing season, which resulted in irrigations in the VegApp and WB being discontinued for a period of several days. During the time period when irrigation was turned off, the WB method would have called for more water than the VegApp based on historic ETo values.

Discontinuing irrigation led to relatively less water being used by the WB method in 2017. The contribution of rainfall has not been incorporated into the VegApp due to limited information regarding the impact of rain on soil moisture levels under raised beds covered with plastic mulches and the potential for significant spatial variability in precipitation [23]. Soil water tension readings (data not shown) suggested that levels of soil moisture were not significantly affected by rainfall. This suggests that the assumption that the VegApp does not incorporate rainfall into irrigation recommendations for crops grown on raised beds with plastic mulch is appropriate.


z Mean separation could not be performed between treatments as water meters were not replicated in individual treatments.

**Table 1.** Season irrigation volume and daily water use for tomatoes grown using the vegetable app (VegApp), water balance (WB), and soil moisture sensor (SMS) methods in Tifton, GA, in 2016 and 2017.

When averaged over the two study years, the VegApp used 16% less water than the WB method, though much of this was due to the 2016 growing season. The SMS-managed plots utilized 31% less water than the WB method. This suggests that the VegApp and SMS-based irrigation can reduce water use when compared to methods relying on historic ETo to manage irrigation. This may be expected as numerous studies have demonstrated the efficiencies of a microclimate and SMS-based irrigation when compared to historical ET-based methods [30].

be slightly more efficient than tensiometer-based irrigation scheduling. The automated SMSbased system has the ability to deliver water at a high frequency with short-duration (pulsed) irrigation events, which have been shown to reduce water use while maintaining yields of tomato [31]. Pulsed irrigation typically results in a shallower wetting front shortly after the irrigation event, increasing application efficiencies [32, 33]. The VegApp and WB-based irrigations were scheduled for two events per day to simulate optimal grower practices, suggesting that the twice-daily irrigations with the VegApp tool may be as efficient in some years as a

Foliar concentrations of macronutrients were measured during this 2-year trial. While there were no significant differences among treatments for most macronutrients in either study year, plants grown with the VegApp had significantly higher nitrogen (N) levels than the WBand SMS-grown plants in 2017 (**Figure 1**). In 2017, the VegApp had foliar N concentrations of 5.56% when compared to 5.04% and 4.61% in the WB and SMS-treated plants, respectively. In 2017, less water was applied to WB-grown plants, yet these plants had lower leaf N concentra-

the WB-based irrigation methods were higher than those generated using the VegApp. This additional application of water during the sampling period may have resulted in leaching of

Watermelons were also grown in order to evaluate the performance of the VegApp when compared to WB-based and SMS-managed irrigation regimes. Water usage, fruit yield, quality, and nutrient content were measured in plasticulture-grown "Melody" seedless watermelons over 2 study years. Results in the watermelon trial were similar to those of the tomatoes.

**Figure 1.** Comparison of foliar nitrogen levels between tomato plants grown using Vegetable App (VegApp), water

balance (WB), and soil moisture sensor (SMS) methods in Tifton, GA in 2016 and 2017.

values used in

43

Using Smartphone Technologies to Manage Irrigation http://dx.doi.org/10.5772/intechopen.77304

tions. However, during periods of sampling (fruit formation), the historic ETo

**3.2. SmartIrrigation vegetable application performance in watermelon**

more complex SMS-based system.

some fertilizer during fruit formation.

While tomatoes grown using the VegApp utilized less water than the currently recommended WB irrigation method, yields were comparable among the three treatments (**Table 2**). In both study years, plants grown using the VegApp had the highest numerical total yield, but this was not significantly different than the other treatments.

In 2016, plants grown using the SMS-based irrigation method had a significantly higher IWUE when compared to those grown using the VegApp and WB-based methods (**Table 2**). While the yield of the SMS-managed plots was numerically lower than the other irrigation treatments in 2016, the SMS plots used substantially less water than the VegApp and WB-based plots, resulting in a significantly greater IWUE. In 2017, the VegApp had a significantly greater IWUE than the SMS-based irrigated plants. The increased IWUE in 2017 for VegApp and WB-grown plants was due to the decrease in irrigation volume used (**Table 1**). During this study, the SMSgrown plants had the most consistent IWUE, with 25.2 g·L−1 and 24.0 g·L−1 in 2016 and 2017, respectively, which were similar to those reported for fresh market tomato in North Florida [7]. The IWUE of the other irrigation treatments were more variable. This variability was the result of fluctuations in water used with no significant difference in yield (**Table 2**). However, when averaged over both study years, the IWUE of the VegApp and SMS-based irrigations were numerically similar. DePascale et al. [30] reported real-time microclimate-based irrigation to


z IWUE = total marketable yield divided by seasonal irrigation volume.

y Values in the same column and year followed by the same letter are not significantly different at *P* ≤ 0.05 according to Tukey's honest significant difference test.

**Table 2.** Marketable yields of total, extra-large, and large fruit and irrigation water use efficiency (IWUE) for tomatoes grown using the vegetable app (VegApp), water balance (WB), and soil moisture sensor (SMS) methods in Tifton, GA, in 2016 and 2017.

be slightly more efficient than tensiometer-based irrigation scheduling. The automated SMSbased system has the ability to deliver water at a high frequency with short-duration (pulsed) irrigation events, which have been shown to reduce water use while maintaining yields of tomato [31]. Pulsed irrigation typically results in a shallower wetting front shortly after the irrigation event, increasing application efficiencies [32, 33]. The VegApp and WB-based irrigations were scheduled for two events per day to simulate optimal grower practices, suggesting that the twice-daily irrigations with the VegApp tool may be as efficient in some years as a more complex SMS-based system.

Foliar concentrations of macronutrients were measured during this 2-year trial. While there were no significant differences among treatments for most macronutrients in either study year, plants grown with the VegApp had significantly higher nitrogen (N) levels than the WBand SMS-grown plants in 2017 (**Figure 1**). In 2017, the VegApp had foliar N concentrations of 5.56% when compared to 5.04% and 4.61% in the WB and SMS-treated plants, respectively. In 2017, less water was applied to WB-grown plants, yet these plants had lower leaf N concentrations. However, during periods of sampling (fruit formation), the historic ETo values used in the WB-based irrigation methods were higher than those generated using the VegApp. This additional application of water during the sampling period may have resulted in leaching of some fertilizer during fruit formation.

#### **3.2. SmartIrrigation vegetable application performance in watermelon**

When averaged over the two study years, the VegApp used 16% less water than the WB method, though much of this was due to the 2016 growing season. The SMS-managed plots utilized 31% less water than the WB method. This suggests that the VegApp and SMS-based

irrigation. This may be expected as numerous studies have demonstrated the efficiencies of a microclimate and SMS-based irrigation when compared to historical ET-based methods [30]. While tomatoes grown using the VegApp utilized less water than the currently recommended WB irrigation method, yields were comparable among the three treatments (**Table 2**). In both study years, plants grown using the VegApp had the highest numerical total yield, but this

In 2016, plants grown using the SMS-based irrigation method had a significantly higher IWUE when compared to those grown using the VegApp and WB-based methods (**Table 2**). While the yield of the SMS-managed plots was numerically lower than the other irrigation treatments in 2016, the SMS plots used substantially less water than the VegApp and WB-based plots, resulting in a significantly greater IWUE. In 2017, the VegApp had a significantly greater IWUE than the SMS-based irrigated plants. The increased IWUE in 2017 for VegApp and WB-grown plants was due to the decrease in irrigation volume used (**Table 1**). During this study, the SMSgrown plants had the most consistent IWUE, with 25.2 g·L−1 and 24.0 g·L−1 in 2016 and 2017, respectively, which were similar to those reported for fresh market tomato in North Florida [7]. The IWUE of the other irrigation treatments were more variable. This variability was the result of fluctuations in water used with no significant difference in yield (**Table 2**). However, when averaged over both study years, the IWUE of the VegApp and SMS-based irrigations were numerically similar. DePascale et al. [30] reported real-time microclimate-based irrigation to

**Irrigation treatment (kg·ha−1) (g·L−1)**

VegApp 58,490ay 36,310a 17,180a 18.0b WB 57,500a 35,280a 17,490a 13.2b SMS 48,740a 30,350a 14,160a 25.2a

VegApp 57,990a 51,130a 5560a 31.1a WB 50,620a 43,660a 5840a 30.0ab SMS 54,590a 46,370a 6970a 24.0b

Values in the same column and year followed by the same letter are not significantly different at *P* ≤ 0.05 according to

**Table 2.** Marketable yields of total, extra-large, and large fruit and irrigation water use efficiency (IWUE) for tomatoes grown using the vegetable app (VegApp), water balance (WB), and soil moisture sensor (SMS) methods in Tifton, GA,

**Total Extra large Large IWUEz**

to manage

irrigation can reduce water use when compared to methods relying on historic ETo

was not significantly different than the other treatments.

42 Irrigation in Agroecosystems

2016

2017

IWUE = total marketable yield divided by seasonal irrigation volume.

Tukey's honest significant difference test.

z

y

in 2016 and 2017.

Watermelons were also grown in order to evaluate the performance of the VegApp when compared to WB-based and SMS-managed irrigation regimes. Water usage, fruit yield, quality, and nutrient content were measured in plasticulture-grown "Melody" seedless watermelons over 2 study years. Results in the watermelon trial were similar to those of the tomatoes.

**Figure 1.** Comparison of foliar nitrogen levels between tomato plants grown using Vegetable App (VegApp), water balance (WB), and soil moisture sensor (SMS) methods in Tifton, GA in 2016 and 2017.

The SMS irrigation method used the least amount of water in 2016, which was similar to results found in tomatoes in 2016 (**Table 3**). Likewise, irrigation volumes in 2017 were lower than 2016 in watermelons. This is not unexpected as ETc was 29% lower in 2017 than in 2016. As with tomatoes, in 2017, the VegApp accounted more appropriately for lower levels of ET<sup>c</sup> in late May and June for watermelons when compared to the WB method using historic ETo values. This resulted in a larger relative reduction in water use in the VegApp plots when compared to plants grown using the WB method in 2017.

When averaged over the 2 years of the study, the VegApp used 15% less water than the WB method, and the SMS-based regime utilized 29% less water than the WB method. Unlike tomatoes, the VegApp used less water than the WB-grown plants in both study years. The cumulative water use data suggests that the VegApp was more conservative in scheduling water than the current recommended WB method.

The performance of the VegApp when compared to the SMS-based system was more variable over the 2 study years. Several studies have reported improved irrigation efficiencies using SMS-based or real-time ET<sup>c</sup> data when compared to historic ETo -based methods [30, 31]. Nonetheless, in both study years, the VegApp utilized less water than the WB method, again suggesting that applying real-time ET<sup>o</sup> values obtained by nearby weather stations may be more efficient than historic ET<sup>o</sup> values.

As with tomatoes, total yields of watermelon were not impacted by irrigation treatment in either study year (**Table 4**). There were differences between first harvest yields in 2016, with plants grown using the SMS-based irrigation regime having a significantly lower first harvest than the other treatments. This may be due to the lower irrigation volume used by the SMSgrown plants in the hot and dry 2016 growing season. In 2017, there were differences in yields of 45-ct fruit among the treatments, with WB-grown plants having the lowest yields of this size category of melon.

Similar to tomatoes, there were differences in IWUE among treatments and study years. However, there were no interactions between the study year and the treatment. Analysis of main effects indicated that IWUE in the VegApp was not significantly different than either the SMS or WB irrigation systems (**Table 5**). In addition, results of foliar nutrient analysis in the watermelons were similar to those in tomatoes. Foliar N concentrations were significantly higher in the VegApp-treated plots than the SMS-grown plants (**Table 5**). In this instance, the increase in foliar N levels in VegApp-grown plants compared to SMS-managed plants may not be due to differences in leaching, as the SMS-grown plants utilized less water than those managed using the VegApp. A shallower wetting front that may be associated with pulsed-type irrigations in the SMS system may have resulted in a shallower root system in

Values in the same column and year followed by the same letter are not significantly different at *P* ≤ 0.05 according to

**Table 5.** Effects of treatment for irrigation water use efficiency (IWUE) and foliar nitrogen (N) concentrations for watermelons grown using the vegetable app (VegApp), water balance (WB), and soil moisture sensor (SMS) methods in

Irrigation treatment (g·L−1) (%) VegApp 28.8aby 4.54a SMS 33.6a 4.21b WB 24.0b 4.30ab

IWUE = season irrigation volume divided by total marketable yield.

Tukey's honest significant difference test.

Tifton, GA, in 2016 and 2017.

**IWUEz N**

Values in the same column and year followed by the same letter are not significantly different at *P* ≤ 0.05 according to

**Table 4.** Total marketable yields, first harvest yields, and yield of 45 and 36 count (ct) fruit for watermelons grown using the vegetable app (VegApp), water balance (WB), and soil moisture sensor (SMS) methods in Tifton, GA, in 2016 and

**Total 45 ctz 36 ct First harvest**

Using Smartphone Technologies to Manage Irrigation http://dx.doi.org/10.5772/intechopen.77304 45

**Irrigation treatment (kg·ha−1)**

45 ct = 6.2 to 7.9 kg, 36 ct = 8.0 to 9.7 kg.

Tukey's honest significant difference test.

z

x

2017.

z

y

2016

2017

VegApp 55,640ax 12,100a 22,750a 30,350a SMS 55,190a 11,400a 23,150a 22,960b WB 48,600a 7990a 21,290a 31,990a

VegApp 56,310a 23,730ab 10,180a 20,440a SMS 65,430a 28,970a 12,870a 23,510a WB 66,580a 16,720b 16,020a 23,770a


z Mean separation could not be performed between treatments as water meters were not replicated in individual treatments.

**Table 3.** Season irrigation volume and daily water use for watermelon grown using the vegetable app (VegApp), water balance (WB), and soil moisture sensor (SMS) methods in Tifton, GA, in 2016 and 2017.


z 45 ct = 6.2 to 7.9 kg, 36 ct = 8.0 to 9.7 kg.

The SMS irrigation method used the least amount of water in 2016, which was similar to results found in tomatoes in 2016 (**Table 3**). Likewise, irrigation volumes in 2017 were lower

As with tomatoes, in 2017, the VegApp accounted more appropriately for lower levels of ET<sup>c</sup> in late May and June for watermelons when compared to the WB method using historic ETo values. This resulted in a larger relative reduction in water use in the VegApp plots when

When averaged over the 2 years of the study, the VegApp used 15% less water than the WB method, and the SMS-based regime utilized 29% less water than the WB method. Unlike tomatoes, the VegApp used less water than the WB-grown plants in both study years. The cumulative water use data suggests that the VegApp was more conservative in scheduling

The performance of the VegApp when compared to the SMS-based system was more variable over the 2 study years. Several studies have reported improved irrigation efficiencies

Nonetheless, in both study years, the VegApp utilized less water than the WB method, again

As with tomatoes, total yields of watermelon were not impacted by irrigation treatment in either study year (**Table 4**). There were differences between first harvest yields in 2016, with plants grown using the SMS-based irrigation regime having a significantly lower first harvest than the other treatments. This may be due to the lower irrigation volume used by the SMSgrown plants in the hot and dry 2016 growing season. In 2017, there were differences in yields of 45-ct fruit among the treatments, with WB-grown plants having the lowest yields of this

Mean separation could not be performed between treatments as water meters were not replicated in individual

**Table 3.** Season irrigation volume and daily water use for watermelon grown using the vegetable app (VegApp), water

data when compared to historic ETo

**(L·ha−1) (L·ha−1·d−1)**

was 29% lower in 2017 than in 2016.


values obtained by nearby weather stations may be

than 2016 in watermelons. This is not unexpected as ETc

compared to plants grown using the WB method in 2017.

water than the current recommended WB method.

values.

**Irrigation treatment Irrigation volume Daily water use**

2016 VegApp 2892,000z 26,570 WB 3,024,000 27,780 SMS 1,997,000 18,330 2017 VegApp 1,438,000 16,000 WB 2,067,000 23,010 SMS 1,629,000 17,960

balance (WB), and soil moisture sensor (SMS) methods in Tifton, GA, in 2016 and 2017.

using SMS-based or real-time ET<sup>c</sup>

44 Irrigation in Agroecosystems

more efficient than historic ET<sup>o</sup>

size category of melon.

z

treatments.

suggesting that applying real-time ET<sup>o</sup>

x Values in the same column and year followed by the same letter are not significantly different at *P* ≤ 0.05 according to Tukey's honest significant difference test.

**Table 4.** Total marketable yields, first harvest yields, and yield of 45 and 36 count (ct) fruit for watermelons grown using the vegetable app (VegApp), water balance (WB), and soil moisture sensor (SMS) methods in Tifton, GA, in 2016 and 2017.

Similar to tomatoes, there were differences in IWUE among treatments and study years. However, there were no interactions between the study year and the treatment. Analysis of main effects indicated that IWUE in the VegApp was not significantly different than either the SMS or WB irrigation systems (**Table 5**). In addition, results of foliar nutrient analysis in the watermelons were similar to those in tomatoes. Foliar N concentrations were significantly higher in the VegApp-treated plots than the SMS-grown plants (**Table 5**). In this instance, the increase in foliar N levels in VegApp-grown plants compared to SMS-managed plants may not be due to differences in leaching, as the SMS-grown plants utilized less water than those managed using the VegApp. A shallower wetting front that may be associated with pulsed-type irrigations in the SMS system may have resulted in a shallower root system in


z IWUE = season irrigation volume divided by total marketable yield.

y Values in the same column and year followed by the same letter are not significantly different at *P* ≤ 0.05 according to Tukey's honest significant difference test.

**Table 5.** Effects of treatment for irrigation water use efficiency (IWUE) and foliar nitrogen (N) concentrations for watermelons grown using the vegetable app (VegApp), water balance (WB), and soil moisture sensor (SMS) methods in Tifton, GA, in 2016 and 2017.

those plants reducing nitrogen uptake by those plants. Alternatively, the VegApp, through improved early-season irrigation management, may improve root growth and the ability for crops to remove nutrients from the soil profile [34].

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