Sensor-based Irrigation Scheduling Research Articles

Sensor Based Irrigation Management in Crop Production: A Review

Efficient water management in agriculture is crucial for sustainable crop production, especially in regions facing water scarcity. Sensor-based irrigation scheduling offers a promising solution by enabling precise and timely irrigation, optimizing water usage while maintaining or enhancing crop yields. This study was made in order to investigate the efficacy of sensor-based technologies in irrigation scheduling for improving water use efficiency in agricultural settings. Data on utilization of an array of sensors including soil moisture, weather and crop-specific indicators., real-time data was collected and analyzed to determine the optimum irrigation timing and volume. The procedure integrated these sensor-derived insights with irrigation scheduling algorithms to dynamically adjust water delivery, aligning with the crop's actual water needs. This study reviews the importance of soil moisture sensors for irrigation, as well as sensor technology and its uses irrigation management and irrigation scheduling.

EBMAN-HP: A parallel model for simulation of sensor-based ebb-and-flow subirrigation systems

Ebb-and-flow irrigation system is a closed-loop efficient subirrigation system. In this study, a numerical model (EBMAN-HP) has been presented for simulation of all components (variations of water depth in supply tank and concrete floor/tank) and all phases of flood-floor/bench ebb-and-flow subirrigation systems. The model benefits from a fine-tuned computational algorithm for hysteresis module. The model can simulate both time-specified and sensor-based irrigation scheduling. Since ebb-and-flow irrigation system incorporates numerous pots, Richards’ equation should be solved for several pots to obtain sufficient understanding of the whole system. Therefore, the proposed model benefits from OpenMP parallel programming to speed up the execution time. Besides, a novel parallel TDMA solver have been presented that accelerates the computation speed by breaking a large system of equations into several simultaneously-solved portions. The model has been validated and verified against several analytical, numerical and experimental test cases. The results showed hysteresis module can completely remove artificial pumping error in two critical test cases. The parallel TDMA solver was shown to be able to reach to the speedup of about 90 %. The model was shown to perform faster than Hydrus-1D even in serial mode for coarser grids (about 52 % faster in average of 8 test cases) and similar to Hydrus-1D for dense grids (about 6 % faster in average of 4 test cases) with the perfect agreement (NSE between 0.999 and 1.000 and the average difference in MBE less than 0.1 % for 12 cases). Parallel model could boost the models’ performance to about 500 % using 6 processors. Finally, comprehensive illustrative example has been shown to present almost all capabilities of model.

A comparison of precision and conventional irrigation in corn production in Southeast Alabama

Adoption of water-conservation irrigation practices could potentially reduce water and energy use and increase profitability, as well as protect the environment. Precision irrigation consisting of soil sensors (SS) for irrigation scheduling and variable rate irrigation (VRI) was compared with conventional uniform irrigation (URI). The study was conducted in South Alabama during the 2018 and 2019 corn growing seasons. The SS-VRI and URI treatments spanned the length of the field and were compared across five different management zones (MZ) that exhibited soil and terrain differences. Soil water tension sensors were installed on each MZ-treatment area to monitor hourly soil water changes. Results showed that on the two zones covering 55% of the study field, MZ 1 and MZ 2, the SS-VRI treatment, on a two-year average, resulted in 26% less irrigation water applied compared to the URI treatment; however, there were no statistical differences between yields or yield variability among treatments. Even though in MZ 4, there was not a substantial difference in irrigation water applied among treatments, soil sensors increased the precision of irrigation rate determination during the peak of high crop water demand. Findings from this study showed that as rainfall amount and distribution change over a crop growing period, soil sensor-based irrigation scheduling could be used to prevent over- or under irrigation. With proper management, the combination of soil sensors and VRI provides farmers with the opportunity to reduce water use, while increasing or maintaining yields; however, farmers must consider the investment and operating costs relative to the benefits.

Sensor-based Irrigation Scheduling, Soil Physical Properties and Wheat Productivity under Different Organic Amendments

ABSTRACT Sensor-based irrigation scheduling in field crops like wheat for achieving optimum yield with minimum use of water needs investigation under different amendment practices. The present study was thus conducted to investigate the effect of four irrigation regimes i.e., soil moisture indicator (red level of indication, SMI), tensiometer (35 k Pa), IW/Pan-E 0.9, and IW/Pan-E 0.6 in main plots and four organic amendments i.e., farm yard manure (FYM), rice straw mulch, hydrogel and without amendments in subplots on soil physical properties and wheat productivity. The field experiment was laid out in split plot design with three replications during rabi 2020–21 at Research Farm, Department of Soil Science, PAU, Ludhiana. The use of SMI shows promising results in respect of scheduling irrigation in wheat as similar biological yield with same amount of water was achieved as that observed for recommended practice i.e., IW/PAN-E = 0.9. The results further indicate that the use of amendments, particularly FYM, leads to reduction in soil bulk density by 4%, while the soil moisture content at field capacity and water-holding capacity by 14% and 9.3%. It is, thus, concluded that scheduling irrigation based on SMI along with FYM in wheat is a suitable practice for northwest India.

Pan evaporation and sensor based approaches of irrigation scheduling for crop water requirement, growth and yield of okra

The results of this study revealed that the pan evaporation and sensor based irrigation scheduling along with fertigation scheduling significantly affected the plant height, fruit weight, fruit length, crop water requirement, crop yield and water use efficiency of okra crop. The pan evaporation and crop evapotranspiration (ETc) values significantly vary over different stages of okra crop. The maximum and minimum average daily pan evaporation was recorded 8.4 mm day-1 and 4.5 mm day-1 at mid stage and late stage of okra crop respectively while, the maximum average daily ETc was recorded 7 mm day-1 at mid stage of okra crop. A approach of irrigation scheduling with 100 per cent field capacity, based on soil moisture sensor under automated drip irrigation system along with 100 per cent RDF through fertigation in equal splits at 4 day intervalor 80 % per cent of volume of crop water requirement based on pan evaporation under automated drip irrigation along with 100 per cent RDF through fertigation in equal splits at 2 day interval, can be used for irrigating okra crop with significant water saving, crop yield and water use efficiency under limited availability of daily weather datain climatic condition of Udaipur district of Rajasthan.

Efficient IoT-Based Control for a Smart Subsurface Irrigation System to Enhance Irrigation Management of Date Palm.

Drought is the most severe problem for agricultural production, and the intensity of this problem is increasing in most cultivated areas around the world. Hence improving water productivity is the primary purpose of sustainable agriculture. This study aimed to use cloud IoT solutions to control a modern subsurface irrigation system for improving irrigation management of date palms in arid regions. To achieve this goal, we designed, constructed, and validated the performance of a fully automated controlled subsurface irrigation system (CSIS) to monitor and control the irrigation water amount remotely. The CSIS is based on an autonomous sensors network to instantly collect the climatic parameters and volumetric soil water content in the study area. Therefore, we employed the ThingSpeak cloud platform to host sensor readings, perform algorithmic analysis, instant visualize the live data, create event-based alerts to the user, and send instructions to the IoT devices. The validation of the CSIS proved that automatically irrigating date palm trees controlled by the sensor-based irrigation scheduling (S-BIS) is more efficient than the time-based irrigation scheduling (T-BIS). The S-BIS provided the date palm with the optimum irrigation water amount at the opportune time directly in the functional root zone. Generally, the S-BIS and T-BIS of CSIS reduced the applied irrigation water amount by 64.1% and 61.2%, respectively, compared with traditional surface irrigation (TSI). The total annual amount of applied irrigation water for CSIS with S-BIS method, CSIS with T-BIS method, and TSI was 21.04, 22.76, and 58.71 m3 palm−1, respectively. The water productivity at the CSIS with S-BIS (1.783 kg m−3) and T-BIS (1.44 kg m−3) methods was significantly higher compared to the TSI (0.531 kg m−3). The CSIS with the S-BIS method kept the volumetric water content in the functional root zone next to the field capacity compared to the T-BIS method. The deigned CSIS with the S-BIS method characterized by the positive impact on the irrigation water management and enhancement on fruit yield of the date palm is quite proper for date palm irrigation in the arid regions.

Evaluation of Two Irrigation Scheduling Methods and Nitrogen Rates on Corn Production in Alabama

Regulations on nutrient application amounts and environmental impacts of fertilizers are promoting advances in agricultural management strategies to optimize irrigation application and N fertilization in corn. Previous studies have found a relationship between irrigation application, available water in the soil, and N fertilizer uptake. The objective of this study was to evaluate interactions between two irrigation scheduling methods and four N rate applications (0-control, 202, 269, and 336 kg ha−1) on grain yield, aboveground biomass, plant N concentration, N uptake, and nitrogen use efficiency in corn. The study was conducted at the Tennessee Valley Research and Extension Center (TVREC) during two growing seasons (2014 and 2015). The irrigation scheduling methods consisted of (i) the pan evaporation method, which is based on managing the crop’s estimated evapotranspiration (ET) using pan evaporation values and the crop’s consumptive water use and (ii) the sensor-based irrigation scheduling method based on soil matric potential values recorded by soil moisture tension sensors installed in the field. Irrigation amounts from both irrigation scheduling methods indicated that less water was applied with the sensor-based method. The different amounts of irrigation applied associated with the two irrigation scheduling methods did not impact grain yield, aboveground biomass, and NUE. In general, NUEs values decreased with increased N rates, which means that additional N fertilizer added to the soil was not converted into grain yield or/and adsorbed by plants; therefore, more N remained in the soil, increasing the risk for environmental problems.

Irrigation Scheduling to Promote Corn Productivity in Central Alabama

Agriculture is the largest consumer of water in the United States. Results from previous studies have shown that it is possible to substantially reduce irrigation amounts and maintain corn yield. The objectives of this study were to evaluate the advantages and disadvantages of two irrigation scheduling methods for corn production in Alabama. Two irrigation scheduling methods evaluated were: a) Checkbook, which is one of the conventional methods used by farmers that is based on the soil water balance estimated using water lost by evapotranspiration and its replacement through rainfall or irrigation, and b) Sensor-based, which was based on soil matric potential values recorded by soil moisture tension sensors installed in the field. The experimental field was divided into two irrigation management zones (zone A and zone B) based on soil properties of each field. During the 2014 season in zone A, significant grain yield differences were observed between the two irrigation methods. The Checkbook plots exhibited greater yield than Sensor-based plots: 10181 kg ha-1 and 9696 kg ha-1, respectively. The greater yield on the Checkbook plots could be associated with higher irrigation rate applied, 148 mm more, compared with the Sensor-based plots. In zone B, there were no significant yield differences between both irrigation methods; however, Sensor-based plots out yielded Checkbook plots, with 9673 kg ha-1 and 9584 kg ha-1, respectively. Even though the irrigation amount applied in Checkbook located in zone B was higher, 102 mm more, there were no significant yield differences. Therefore, it suggests that the Sensor-based method was promissory irrigation scheduling strategy under the conditions of zone B. In 2015, there were no significant grain yield differences between zone A and zone B when the data from the Checkbook plots were analyzed. However, the Sensor-based treatment produced a statistically significant difference of grain yield of 13597 kg ha-1 in zone A and 11659 kg ha-1 in zone B, also both zones received the same amount of irrigation. Overall results of both growing seasons indicated that the use of the Sensor-based irrigation scheduling treatment resulted in similar values of total profit per hectare when compared to Checkbook method. The Sensor-based method seems a promising strategy that could result in water and financial savings, but more research is required.

Water and Nitrogen Budget Dynamics for a Maize-Peanut Rotation in Florida

HighlightsDSSAT simulations of final N uptake, biomass, and yield for a maize-peanut rotational field experiment with three irrigation treatments and three N fertilizer rates had good performance for the irrigated treatments (average nRMSE of 9%) but greater error for the rainfed treatments (average nRMSE of 15%).Experiments and DSSAT simulations demonstrated that N fertilizer and irrigation applications were reduced by 26% and 60%, respectively, when using a 247 kg N ha-1 fertilizer rate and a sensor-based irrigation schedule rather than conventional practices of 336 kg N ha-1 and a calendar-based irrigation method, with no impact on yield.Simulations demonstrated that N leaching during the crop rotation was reduced by 37% when an N fertilizer rate of 247 kg N ha-1 and sensor-based irrigation scheduling were used versus conventional practices.Soil N increased (=15 mg kg-1) when maize and peanut residues decayed and then leached during the fallow season. Cover or cash crops planted immediately after the maize and peanut harvests have potential to take up this N and reduce leaching.Abstract. Nitrogen (N) is an essential element for crop growth and yield; however, excessive N applications not taken up by crops can result in N leaching from the root zone, increasing N loads to waterbodies and leading to a host of environmental problems. The main objective of this study was to simulate water and N balances for a maize-peanut (Zea mays L. and Arachis hypogaea L.) rotational field experiment with three irrigation treatments and three N fertilizer rates. The irrigation treatments consisted of mimicking grower irrigation practices in the region (GROW), using soil moisture sensors to schedule irrigation (SMS), and non-irrigated (NON). The N fertilizer rates were low, medium, and high (157, 247, and 336 kg N ha-1, respectively) for maize with a constant 17 kg ha-1 for all peanut treatments. DSSAT maize genetic coefficients were calibrated using the SMS-high treatment combination under the assumption of no water or N stress. The other eight treatment combinations were used as independent data for model validation of the crop coefficients. All soil hydrologic parameters were specified based on measured values, and default DSSAT peanut genetic coefficients were used with no calibration. For the irrigated treatments, DSSAT models had good performance for N uptake, biomass, and yield (average nRMSE of 8%) and moderate performance for soil water content (average nRMSE of 18%). Soil nitrate RMSE was 21% lower than the standard deviation of the observed data (5.8 vs. 7.2 mg kg-1). For the rainfed treatments, DSSAT had greater error (average nRMSE of 15% for N uptake, biomass, and yield, and average nRMSE of 31% for soil water). Soil nitrate RMSE was 11% greater than the standard deviation of the observed data (8.0 vs. 7.2 mg kg-1), and nRMSE was >30% during the crop rotation. Simulations estimated that N leaching over the crop rotation was reduced by 24% on average when using the 247 kg N ha-1 fertilizer rate compared to 336 kg N ha-1 across the irrigation treatments. Furthermore, N leaching was reduced by 37% when using SMS to schedule irrigation and the 247 kg N ha-1 fertilizer rate for maize and 17 kg N ha-1 for peanut compared to conventional practices (GROW and 336 kg N ha-1 for maize and 17 kg N ha-1 for peanut). Moreover, this management practice reduced N fertilizer use by 26% and irrigation water use by up to 60% without negative impacts on yield. Observed and simulated soil N increased during maize and peanut residue decay, with simulations estimating that this soil N would leach below the root zone during the fallow season. This leaching could potentially be reduced if a cover crop or cash crop were planted between the maize and peanut crops to take up the mineralized N. Keywords: Agricultural best management practices, Bare fallow, BMPs, Maize-peanut rotation, N balance, N fertilization, N leaching, Sandy soils, Sensor-based irrigation scheduling, Water balance.

ARSPivot, A Sensor-Based Decision Support Software for Variable-Rate Irrigation Center Pivot Systems: Part A. Development

HighlightsThe ARSPivot software seamlessly integrates site-specific irrigation scheduling methods with weather, plant, and soil water sensing systems in the operation of variable-rate irrigation (VRI) center pivot systems.ARSPivot embodies an Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system that incorporates site-specific irrigation scheduling methods and automates the collection and processing of data obtained from sensing systems supporting them.ARSPivot incorporates a friendly graphical user interface (GUI) that assists in the process of setting up a computerized representation of a coupled ISSCADA VRI center pivot system and simplifies the review of irrigation prescriptions automatically generated based on sensor feedback.ARSPivot’s GUI includes a geographic information system (GIS) that relates sensed data and imported GIS data to specific field control zones.Abstract. The commercial availability of variable-rate irrigation (VRI) systems gives farmers access to unprecedented control of the irrigation water applied to their fields. To take full advantage of these systems, their operations must integrate site-specific irrigation scheduling methods that in turn should be supported by a network of sensing systems. An Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system patented by scientists with the USDA-Agricultural Research Service (ARS) at Bushland, Texas, incorporates site-specific irrigation scheduling methods informed by weather, plant, and soil water sensing systems. This article introduces a software package, ARSPivot, developed to integrate the ISSCADA system into the operation of VRI center pivot systems. ARSPivot assists the operation and integration of a complex network of sensing systems, irrigation scheduling methods, and irrigation machinery to achieve this end. ARSPivot consists of two independent programs interacting through a client-server architecture. The client program is focused on automatically collecting and processing georeferenced data from sensing systems and communicating with a center pivot control panel, while the server program is focused on communicating with users through a friendly graphical user interface (GUI) involving a geographic information system (GIS). The GUI allows users to visualize and modify site-specific prescription maps automatically generated based on sensor-based irrigation scheduling methods, and to control and monitor the application of irrigation amounts specified in these recommended prescription maps using center pivots equipped for VRI zone control or VRI speed control. This article discusses the principles and design considerations followed in the development of ARSPivot and presents tools implemented in the software for the virtual design and physical operation of a coupled ISSCADA VRI center pivot system. This article also illustrates how the ISSCADA system and ARSPivot constitute a comprehensive sensor-based decision support system (DSS) for VRI management that is accessible to users without in-depth knowledge of sensing systems or irrigation scheduling methods. Keywords: Center pivot irrigation, Decision support system, Precision agriculture, Sensors, Site-specific irrigation scheduling, Software, Variable rate irrigation.n

Comparison of Sensor-Based and Weather-Based Irrigation Scheduling

HighlightsSensor-based irrigation scheduling methods (SBISM) were compared with computerized water balance scheduling.Number and time of irrigation events scheduled using the SBISM were often different from those predicted by the computerized method.The highly variable soils at the Missouri site complicated interpretation of the sensor values.Both SBISM and computerized water balance scheduling could be used for irrigation scheduling with close attention to soil texture and effective rainfall or irrigation.Abstract. Sensor-based irrigation scheduling methods (SBISM) measure soil moisture to allow scheduling of irrigation events based on the soil-water status. With rapid development of soil moisture sensors, more producers have become interested in SBISM, but interpretation of the sensor data is often difficult. Computer-based methods attempt to estimate soil water content and the Arkansas Irrigation Scheduler (AIS) is one example of a weather-based irrigation scheduling tool that has been used in the Mid-South for many years. To aid producers and consultants interested in learning more about irrigation scheduling, field studies were conducted for two years in Mississippi and a year in Missouri to compare SBISM with the AIS. Soil moisture sensors (Decagon GS-1, Acclima TDR-315, Watermark 200SS) were installed in multiple locations of a soybean field (Mississippi) and cotton field (Missouri). Soil water contents of the fields were measured hourly at multiple depths during the growing seasons. The AIS was installed on a computer to estimate soil water content and the required data were obtained from nearby weather stations at both locations and manually entered in the program. In Mississippi, numbers and times of the irrigation events triggered by the SBISM were compared with those that would have been scheduled by the AIS. Results showed the number and time of irrigation events scheduled using the SBISM were often different from those predicted by the AIS, especially during the 2018 growing season. The highly variable soils at the Missouri site complicated the interpretation of the sensor values. While all of the sites were within the Tiptonville silt loam map unit, some of the measurements appeared to come from sandier soils. The AIS assumed more water entered the soil than the sensors indicated from both irrigations and rainfalls less than 25 mm. While the irrigation amounts were based on the pivot sprinkler chart, previous testing had confirmed the accuracy of the charts. Furthermore, the difference varied among sites, especially for rainfall large enough to cause runoff. The recommendations based on the Watermark sensors agreed fairly well with the AIS in July after the data from the sandiest site was omitted; however, the later irrigations called for by the AIS were not indicated by the sensors. Both the sensor-based irrigation scheduling method and the AIS could be used as tools for irrigation management in the Mid-South region, but with careful attention to soil texture and the effective portion of rainfall or irrigation. Keywords: Irrigation scheduling, Soil moisture sensor, Soil water content, Water management.

Evaluating a Physiological-based, On-demand Irrigation System for Container-grown Woody Plants with Different Water Requirements

Two sensor-based irrigation scheduling systems were compared for water use and plant growth in container-grown Green Velvet boxwood (Buxus sempervirens L. × B. microphylla Siebold & Zucc. var. koreana ‘Green Velvet’) and slender deutzia (Deutzia gracilis Siebold & Zucc). These crops were chosen because they have different water requirements during production. The two sensor-based irrigation systems included a physiological-based on-demand (OD) irrigation system where the set point was derived from the relationship between substrate moisture and photosynthetic rate. The second system was a daily water use (DWU) method where the amount of water used by the crop was replaced each day. The objective of the study was to evaluate and compare water use and growth metrics using the OD and DWU irrigation scheduling regimes for two container-grown woody plants that differed in their water consumption. There were no differences in root and shoot biomass or growth index due to the irrigation schedule employed for either boxwood or deutzia. For boxwood plants, OD irrigation reduced water consumption by 35.5% and enhanced water use efficiency (WUE) by 54.5% compared with DWU. Total water use of deutzia in OD zones was reduced by 26.5% compared with DWU. DWU offers the labor scheduling advantage of irrigation occurring at a set time of day, and OD offers the advantage of watering as required, potentially reducing water stress as the season progresses and as the plant size and atmospheric demand increase.

Irrigation application efficiency calculation of sprinkler irrigated container plants grown under sensor based irrigation scheduling

Irrigation application efficiency calculation of sprinkler irrigated container plants grown under sensor based irrigation scheduling

Development of a Portable Electro-Mechanical Educational Model for Variable Rate Center Pivot Irrigation Technology

Center pivot irrigation systems usually apply a relatively uniform amount of water to fields that are often inherently variable, which could lead to significant waste of water and energy. To address this issue, our team is now developing an Intelligent Center Pivot (ICP) by integrating sensor-based irrigation scheduling with variable rate irrigation technology. However, before this technology can be applied in commercial production, it is necessary to educate growers about its practicality and potential benefits. The objective of this study was to develop a portable tabletop intelligent center pivot model (ICPDemo) to demonstrate and promote adoption of the ICP technology. This paper describes an ICPDemo constructed in 2014, including the design specifications, electro-mechanical design, control strategy, and performance. The ICPDemo has performed according to design specifications and is successfully being used to demonstrate the benefits and effectiveness of ICP technology for irrigation scheduling.