Open access peer-reviewed chapter

Using Smartphone Technologies to Manage Irrigation

Written By

Timothy Coolong, Luke Miller and George Vellidis

Submitted: 31 January 2018 Reviewed: 17 April 2018 Published: 05 November 2018

DOI: 10.5772/intechopen.77304

From the Edited Volume

Irrigation in Agroecosystems

Edited by Gabrijel Ondrašek

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Abstract

Numerous tools have been developed with the aim of improving irrigation scheduling. Some methods involve using soil moisture sensors and irrigating based on soil moisture thresholds. Others may be based on evapotranspiration models. More novel techniques include irrigating based on the water status within the target crop. However, growers have been reluctant to adopt many of these irrigation scheduling methods because they may be too cumbersome to use, require specialized equipment, or are perceived as too risky compared to traditional methods. Recently, smartphone applications have been developed that schedule irrigation based on crop coefficients and real-time weather data. Called the SmartIrrigation™ application (smartirrigationapps.org), these tools have the potential to aid farmers in conserving water and nutrients, while maintaining crop yields. These applications were developed by the University of Florida and include such crops as citrus (Citrus spp.), cotton (Gossypium hirsutum), turfgrass, blueberries (Vaccinium darrowii), and several vegetables. These applications can be downloaded for free by the public and utilize real-time data from nearby weather stations in Georgia and Florida. To determine the efficacy of the new SmartIrrigation™ applications for watermelons and tomatoes, trials were conducted over 2 years in southern Georgia, USA.

Keywords

  • drip irrigation
  • plasticulture
  • soil moisture sensor
  • evapotranspiration

1. Introduction

Fruit and vegetable farmers in the USA rely on irrigation to produce high-value crops. Though drip irrigation is perceived to be efficient compared to other forms of irrigation, mismanagement can result in excessive water applications with water migrating through macropores (worm holes, cracks, root channels) to below the root zone. Previous experiments have demonstrated that water used for irrigation can be detected in a pan lysimeter within 20 min of drip irrigation initiation on tomatoes [1]. When the water used for irrigation migrates below the root zone, there may be associated leaching of fertilizer and pesticides [2]. Efficient irrigation scheduling requires that farmers manage the timing and duration of irrigation in a manner that maintains yield and quality, while efficiently using water. Many irrigation scheduling methods exist including: the water balance (WB) method, soil moisture monitoring, hand feel and soil appearance, and crop phenology observations. Water balance-based irrigation scheduling relies on reference (ETo) measurements to estimate water losses from a given area [3].

A majority of vegetable growers use traditional methods of measuring soil moisture, by observing soil dryness and through feeling the soil itself. Recent surveys conducted in Georgia (US) found that this method accounts for over 40% of the irrigation scheduling occurring on farms. In addition, an estimated 88% of growers in Georgia may allow crops to be visibly stressed before watering [4]. Other methods of soil moisture-based irrigation may utilize tensiometers, granular matrix probes, or resistance-based sensors to determine thresholds for irrigation management [5, 6]. While soil moisture sensor (SMS)-based irrigation has been shown to be more efficient than a time-based system [7, 8, 9], proper placement of sensors to accurately reflect conditions experienced by the plant can be challenging [10]. Furthermore, placement of sensors within an irrigation zone can be problematic for growers with heterogeneous soils or topography within a field. Irrigation thresholds may also be impacted by factors such as soil type and depth of drip tubing [11].

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2. Determining irrigation scheduling

2.1. Evapotranspiration

Evaporation and transpiration are two important processes involved in the removal of water from soil and plants into the atmosphere. These processes occur simultaneously and are inherently connected to each other [12]. While transpiration and evaporation occur simultaneously, evaporation is based on the availability of water in topsoil and the amount of solar radiation reaching the soil surface [13]. Transpiration is a function of crop canopy density and soil water status. Evaporation accounts for the majority of crop evapotranspiration (ETc) during early stages of crop growth in bare-ground plantings, while transpiration contributes to nearly 90% of the ETc for a mature crop [14].

Evapotranspiration can be separated into ETo and ETc. Crop evapotranspiration is calculated from ETo of a given area and the crop coefficient (Kc) of the crop being measured. Factors affecting ETc include extent of ground cover, crop canopy properties, and aerodynamic resistance [12]. Reference ETo is the amount of water exiting the soil at any time from a reference surface covered by grass at a 0.12 m height that is adequately watered, actively growing, and with a fixed surface resistance [14]. Weather conditions are also important to quantify as they affect the amount of energy available for ETo to occur. The four most important conditions to measure are solar radiation, wind speed, temperature, and humidity, with the most important factor being solar radiation [15].

Crop coefficients are an adjustable constant that define the amount of transpiration occurring within a plant at a given stage of development. Crop coefficients are computed as the ratio ETo:ETc. Environmental and physiological factors affecting Kc include crop type, crop growth stage, climate, and soil type [14]. Plant developmental stage encompasses the relative activity of the plant. Plant size is also impacted by the crop development stage, thus affecting leaf area and canopy density, which in turn impacts transpiration. Accounting for environmental and management factors that influence the rate of canopy development is also important in calculating Kc. Climatic factors that significantly affect Kc are rainfall frequency, wind speed, temperature, and photoperiod [14]. Soil profile characteristics that affect Kc development are water table depth and soil porosity. Therefore, regional Kc estimates from several seasons are important to account for the variability in weather, irrigation, drainage, and runoff [16, 17].

Several WB-based methods exist to calculate ETo rate, such as the Priestley-Taylor method and Hargreaves method. The Priestly-Taylor equation is a modification of the Penman-Monteith equation that approximates parameters established by the Penman-Monteith, using solar radiation to determine ETo. However, calculations at a research site in the humid Southeastern USA found that Priestley-Taylor could overestimate ETo for the region [18]. Priestly-Taylor has also been reported to overestimate the cumulative ETo for the Georgian Coastal Plain area during months with significant rainfall, corresponding to peak early summer vegetable production [18]. Another method that has been used to estimate ETo has been the Hargreaves method. This equation is an empirical model that considers incoming solar energy, evaporation, monthly maximum and minimum temperature, and a temperature coefficient [19]. This method has a high correlation with the Penman-Monteith model for estimates of average weekly ETo in humid regions [19]. These methods of calculating evapotranspiration are easier to use than the Penman-Monteith method; however, this can also result in reduced precision over the course of a season.

2.2. Current recommendations

Current recommendations for drip-irrigated tomatoes in Georgia and Florida are based on variations of the WB method [20]. The WB method estimates daily crop water use based on historical theoretical ETo values for the region adjusted with a Kc [14]. An advantage of using the WB method is that it allows growers to anticipate crop water requirements at certain times during the growing season and plan irrigation based on anticipated ETo. However, irrigating solely based on predicted ETo values may be inaccurate due to changes in annual weather patterns as well as differences in production practices for which crop coefficients were developed [21].

Regulated deficit irrigation is another method of irrigation management performed by imposing water deficits only at certain crop development stages [22]. Progressive or sustained deficit irrigation is the systematic application of water at a constant fraction of ETc throughout the season. Reducing irrigation based on deficit ETc levels may not result in optimal yields or quality in some crops as reducing ETc has been shown to result in a concomitant decrease in yield of many crops [22].

2.3. Smartphone irrigation technologies

Recently, a suite of smartphone-based irrigation scheduling tools, which use real-time ETo data from statewide weather station networks, were developed [24]. Called SmartIrrigation™ Apps [24], these tools use meteorological parameters to determine irrigation schedules based on ETc calculated using Kc and ETo in the following relationship: ETc = ETo x Kc. The suite includes applications 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 methods [25].

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 Environmental Monitoring Network and are used to calculate ETo from air temperature, solar 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 available weather stations. The VegApp uses ETo from the prior 5 d to calculate an average ETo. Then ETc is estimated using Kc curves developed by The University of Florida based on a weeks-after-planting model of crop maturity [27, 28]. The Kc curve for tomato is based on a 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 grown in a drip irrigation and raised-bed plastic mulch production system [23].

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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.

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 ETo 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 ETc 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.

Irrigation treatment Irrigation volume Daily water use
(L·ha−1) (L·ha−1·d−1)
2016
VegApp 3306,000z 39,380
WB 4,526,000 53,880
SMS 1,935,000 23,010
2017
VegApp 1,895,000 29,180
WB 1,684,000 25,910
SMS 2,339,000 36,010

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.

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


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.

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].

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.

Irrigation treatment (kg∙ha−1) (g·L−1)
Total Extra large Large IWUEz
2016
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
2017
VegApp 57,990a 51,130a 5560a 31.1a
WB 50,620a 43,660a 5840a 30.0ab
SMS 54,590a 46,370a 6970a 24.0b

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.

IWUE = total marketable yield divided by seasonal irrigation volume.


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.


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 SMS-grown 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 be slightly more efficient than tensiometer-based irrigation scheduling. The automated SMS-based 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 WB- and 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.

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.

3.2. SmartIrrigation vegetable application performance in watermelon

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.

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 ETc 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.

Irrigation treatment Irrigation volume Daily water use
(L·ha−1) (L·ha−1·d−1)
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

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.

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


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 ETc 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 ETo values obtained by nearby weather stations may be more efficient than historic ETo 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 SMS-grown 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.

Irrigation treatment (kg∙ha−1)
Total 45 ctz 36 ct First harvest
2016
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
2017
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

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.

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


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.


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 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].

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

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.

IWUE = season irrigation volume divided by total marketable yield.


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.


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4. Conclusions

The rapid incorporation of smartphones into the daily lives of individuals has opened new avenues for data delivery. A 2015 survey indicated that 69% of farmers owned smartphones, and this number was expected to increase to 87% by 2016 [35]. As access to smartphone technology increases, dispersal of precise irrigation scheduling methods may also increase. Using real-time weather data to schedule irrigation is not a new concept; however, previously, it would have involved directly downloading data from a weather station or, more recently, accessing data from the Internet-based site and entering it into a fairly complicated equation to develop irrigation recommendations. This process was generally too time-consuming for growers who may be managing dozens if not hundreds of irrigation zones. By linking to nearby weather stations and generating automated recommendations that are sent directly to a smartphone in the field, these new SmartIrrigation™ applications bypass the cumbersome data transfer and calculations previously required for scheduling irrigation. Our data suggest that the VegApp is more efficient in terms of water use than a well-managed irrigation program developed from historic Eto data and, in most cases, just as efficient as a relatively complicated SMS-based system, while maintaining similar yields. In addition, our data suggest that some of the assumptions incorporated into the VegApp (e.g., rainfall not accounted for when using raised beds covered with plastic mulch) are indeed appropriate. Because these trials were conducted on a loamy sand soil, we could not confirm how soil type would affect the efficiency of the VegApp. Nonetheless, our findings suggest that the SmartIrrigation™ applications represent an easily accessible tool that growers and managers can use to produce vegetables by an efficient irrigation management system.

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Acknowledgments

We would like to thank the Georgia Department of Agriculture for funding this research through a Specialty Crop Block Grant titled: SmartPhone Apps for Scheduling Irrigation in Six Specialty Crops. We would also like to thank the University of Florida for developing the SmartIrrigation™ used in these experiments.

References

  1. 1. Malone RW, Ma L, Wauchope D, Ahuja L, Rojas K Ma Q, Warner R, Byers M. Modeling hydrology, metribuzin degradation, and metribuzin transport in macroporous tilled and no-till silt loam soil using RZWQM. Pest Management Science. 2004;60:253-266
  2. 2. Tindall JA, Vencil W. Transport of atrazine, 2-4 D and dicamba through preferential flowpaths in an unsaturated claypan soil near Centralia, Missouri. Journal of Hydrology. 1995;166:37-59
  3. 3. Andales AA, Chavez JL, Bauder TA. Irrigation scheduling: The water balance approach. Colorado State University Cooperative Extension Bulletin. 2015;4:707
  4. 4. United States Department of Agriculture. Methods used in deciding when to irrigate. In: Farm and Ranch Irrigation Survey 2013. Vol. 3. Washington, DC: USDA; 2014. p. 87
  5. 5. Cardenas-Lailhacar B, Dukes MD, Miller GL. Sensor-based automation of irrigation on bermudagrass, during dry weather conditions. Journal of Irrigation and Drainage Engineering. 2010;136:184-193
  6. 6. Munoz-Carpena R, Dukes MD, Li YCC, Klassen W. Field comparison of tensiometer and granular matrix sensor automatic drip irrigation on tomato. HortTechnology. 2005;15:584-590
  7. 7. Zotarelli L, Scholberg JM, Dukes MD, Munoz-Carpena R, Icerman J. Tomato yield, biomass accumulation, root distribution, and irrigation water use efficiency on a sandy soil, as affected by nitrogen rate and irrigation scheduling. Agricultural Water Management. 2009;96:23-34
  8. 8. Zotarelli L, Scholberg JM, Dukes MD, Munoz-Carpena R, Icerman J. Tomato nitrogen accumulation and fertilizer use efficiency on a sandy soil, as affected by nitrogen rate and irrigation scheduling. Agricultural Water Management. 2009;96:1247-1258
  9. 9. Zotarelli L, Dukes MD, Scholberg JM, Femminella K, Munoz-Carpena R. Irrigation scheduling for green bell peppers using capacitance soil moisture sensors. Journal of Irrigation and Drainage Engineering. 2011;137:73-81
  10. 10. Dabach S, Shani U, Lazarovitch N. Optimal tensiometer placement for high-frequency subsurface drip irrigation management in heterogeneous soils. Agricultural Water Managment. 2015;152:91-98
  11. 11. Coolong T. Evaluation of shallow subsurface drip irrigation for the production of acorn squash. HortTechnology. 2016;26:436-443
  12. 12. Shukla S, Jaber F, Srivastava S, Knowles J. Water use and crop coefficient for watermelon in Southwest Florida. University of Florida Institute of Food and Agricultural Sciences Report. 2007; WRP-LY-009
  13. 13. Pereira LS, Perrier A, Allen RG, Alves I. Evapotranspiration: Review of concepts and future trends. Journal of Irrigation and Drainage Engineering. 1999;125:45-51
  14. 14. Allen RG, Pereira LS, Raes D, Smith M. Crop Evapotranspiration, Irrigation and Drain-age. Paper No. 56. Rome: United Nations FAO; 1998. p. 300
  15. 15. Brown P. Basics of Evaporation and Transpiration. Tuscon, Arizona, USA: The University of Arizona Cooperative Extension Publication; 2000. p. 4
  16. 16. Rana G, Katerji N. Measurements and estimation of actual evapotranspiration in the field under Mediterranean climate: A review. European Journal of Agronomy. 2000;13:25-153
  17. 17. Shukla S, Jaber FH, Goswami D, Srivastava S. Evapotranspiration losses for pepper under plastic mulch and shallow water table conditions. Irrigation Science. 2012;31:523-536
  18. 18. Suleiman AA, Hoogenboom G. Comparison of Priestley–Taylor and FAO-56 penman–Monteith for daily reference evapotranspiration estimation in Georgia, USA. Journal of Irrigation and Drainage Engineering. 2007;133:175-182
  19. 19. Hargreaves GH, Samani ZA. Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture. 1985;1:96-99
  20. 20. Harrison K. Irrigation Scheduling Methods. Vol. 974. Univ. Georgia Coop. Ext. Bul.; 2009. p. 8
  21. 21. Amayreh J, Al-Abed N. Developing crop coefficients for field-grown tomato (Lyco-persicon esculentum mill.) under drip irrigation with black plastic mulch. Agricultural Water Managment. 2005;73:247-254
  22. 22. Fereres E, Goldhamer DA, Parsons LR. Irrigation water management of horticultural crops. Historical review compiled for the American Society of Horticultural Science’s 100th anniversary. Hortscience. 2003;38:1036-1042
  23. 23. Migliaccio KL, Morgan KT, Vellidis G, Zotarelli L, Fraisse C, Zurweller BA, Rowland D, Andreis JH, Crane JH. Smartphone apps for irrigation scheduling. Transactions of the ASABE. 2016;59:291-301
  24. 24. University of Florida. Smart Irrigation Applications. 2012. Available from: www.smartirrigationapps.org [Accessed 2016-01-12]
  25. 25. Vellidis G, Liakos V, Perry C, Roberts P, Tucker M, Barnes E. Field evaluation of a smartphone app for scheduling irrigation in cotton. In: Proceedings of the 2015 Beltwide Cotton Conference; 5-7 January. Vol. 2015. New Orleans. Memphis, Tennessee: National Cotton Council of America; 2015. pp. 913-918
  26. 26. Migliaccio KL, Morgan KT, Vellidis G, Zotarelli L, Fraisse C, Rowland D, Andreis JH, Crane JH. Smartphone apps for irrigation scheduling. In: Emerging Technologies for Sustainable Irrigation: A Joint ASABE/IA Irrigation Symposium; 10-12 November, 2015 Long Beach California. St. Joseph, MI. 2015. pp. 1-16
  27. 27. Stanley CD, Clark GA. Water Requirements for Drip Irrigated Tomato Production in Southwest Florida. University of Florida Institute of Food and Agricultural Sciences Publication; 2009. p. SL213
  28. 28. Simonne EH, Dukes MD, Zotarelli L. Principles and practices of irrigation management for vegetables, p. 17-26. In: Olson SM, Simonne E, editors. Vegetable Production Handbook for Florida 2010-2011. Gainesville, FL: University of Florida Institute of Food and Agricultural Sciences; 2010. pp. 17-26
  29. 29. Smajstrla AG, Locascio SJ. Irrigation scheduling of drip-irrigated tomato using tensiometers and pan evaporation. Proceedings of the Florida State Horticulture Society. 1990;103:88-91
  30. 30. DePascale S, Costa D, Vallone S, Barbieri G, Maggio A. Increasing water use efficiency in vegetable crop production: From plant to irrigation systems efficiency. HortTechnology. 2011;21:301-308
  31. 31. Munoz-Carpena R, Dukes MD, Li YCC, Klassen W. Field comparison of tensiometer and granular matrix sensor automatic drip irrigation on tomato. HortTechnology. 2005;15:584-590
  32. 32. Assouline S, Ben-Hur M. Effects of rainfall intensity and slope gradient on the dynamics of interrill erosion during soil surface sealing. Catena. 2006;66:211-220
  33. 33. Coolong T, Surendan S, Warner R. Evaluation of irrigation threshold and duration for tomato grown in a silt loam soil. HortTechnology. 2011;21:466-473
  34. 34. Dukes MD, Zotarelli L, Morgan KT. Use of irrigation technologies for vegetable crops in Florida. HortTechnology. 2010;20:133-142
  35. 35. Potter B. 87% of Farmers Will Own a Smartphone by 2016 [Internet]. 2015. Available from: https://www.agweb.com/article/87-of-farmers-will-own-a-smartphone-by-2016-naa-ben-potter/ [Accessed 2016-11-20]

Written By

Timothy Coolong, Luke Miller and George Vellidis

Submitted: 31 January 2018 Reviewed: 17 April 2018 Published: 05 November 2018