Progress report for LNE19-378
Forty fruit and vegetable growers with 1,000 acres of crops adopt precision irrigation methods to optimize water usage, ten of these farms report an increase in average crop yield as a result of improved irrigation.
The primary goal of this project is to provide an effective sensor-based irrigation system to fruit and vegetable growers, and enhance the practices for growers to implement the technology in their farms through various educational activities. In year 2019, some research studies were carried out to investigate the effective irrigation strategies for growers, and test an IoT based precision irrigation system. Extension publications and presentations were provided by project team to various audiences including growers on the outcomes from these studies. A brief survey was sent out for the importance of precision practices in plant and soil water monitoring. A series of on-farm experiment trials were started with some representative growers, and trainings on installation and system management also provided to them. In the coming seasons, more specific survey will be delivered in need and implementing the precision irrigation systems; seminars and field demonstration will be conducted in the new season to reach out to more growers. We will also continue and extend our extension activities on publication, instruction videos, workshops, and individual grower consultant, to encourage the implementation of precision irrigation system in the field.
1) What is the most effective evapotranspiration (ET) and/or soil-moisture sensor-based system for irrigation scheduling? Various treatments will be investigated to identify the irrigation scheduling strategies using ET, and soil moisture sensors.
2) What sensor data is most effective for real-time control of an irrigation system? An automated precision irrigation system will be developed based on the wireless sensor networks, embedded controls, and data analysis.
3) How effective and sustainable are automated precision irrigation systems? Cost, efficacy, impact on water and nutrient productivity, yield and quality of different automated precision irrigation systems will be assessed in fruit and vegetable crops.
1. Identification of irrigation strategies for apple orchards
A sensor-based irrigation test was conducted at Fruit Research and Extension Center research orchard at Biglerville, PA. This is a 0.9-acre Fuji block, located at a location with relative high elevation. There are total nine row of apple trees. The emitter space along the tube is 24 inches, the size of the tube is 0.5 inch. Three irrigation strategies were used, including conventional irrigation, soil moisture sensor irrigation, and ET irrigation. There were three rows of trees for each treatment.
Soil moisture-based irrigation: In the soil moisture based irrigation, three soil water content sensors (TEROS 12, Meters Group, Inc.) were installed at three depths of 1, 2, and 3 ft, and another two soil water potential sensors (TEROS 21, Meters Group, Inc.) were installed at two depths of 1.25 and 2.75 ft. A wireless communication datalogger (ZL6) was used in the system to record the sensor data. Users could access the soil moisture data with phone or computer through internet. Soil water content and soil water potential values were monitored and recorded through a web based cloud service (Zentra Cloud). In our study, once the soil water content reached to 0.3 m3/m3, the irrigation was applied to the corresponding rows. The values of soil water potential were used as reference and comparation.
Evaportransportation (ET)-based irrigation: The daily reference ET (ETr) was calculated using the Hargreaves model, which was described in detail in FAO-56 Hargreaves equation. The model must be modified to suit different growing conditions and the disparate plant architectures of tall discontinuous crops like fruit trees. Then the reference ET was adjusted by local crop coefficients Kc. While, the Kc could be slightly different at different crop growing stage. In this study, we used a constant value of Kc=0.9 for apple tree orchard through the season. For simplification, the effective rainfall was assumed to be equal to the amount of rainfall from the nearby weather station. The daily water deficit was defined as the input water (rainfall) subtracting the daily evapotranspiration. The water deficit was calculated daily, and it was accumulated through the time. When the water deficit reaches certain value setting to 25.4 mm (1 inch), then the irrigation will need to be scheduled. Our irrigation goal was to reduce the water deficit to zero. In our calculation, if the rainfall/irrigation was greater than the current accumulated water deficit, the value of accumulated water deficit was set to zero at the end of the day (the extra water was regarded as run-off). The water deficit was set to zero at the beginning of the season, with soil water content at field capacity was added to soil prior to the test.
Conventional irrigation: The conventional method for irrigation in the test block was based on the experience.
Data analysis: At the end of the season, three irrigation strategies were compared in different ways, such as total water use, crop yield and quality.
2. Investigation of the internet of things (IoT)-based precision irrigation system
Precision irrigation with sensor-based decision making system has been approved to be effective for water saving in crop production. Internet of things (IoT) system is necessary for monitoring the real-time data from sensors as well as automatically applying water. LoRaWAN, a new low-power wide-range network technology, is low-cost and easy to architect in IoT systems that can be used for precision crop irrigation. In our study, an IoT-based precision irrigation system with LoRaWAN technology was developed and evaluated for a high tunnel vegetable field . Four different irrigation treatments were set for the test field, including timer, soil water content, soil water potential #1, and soil water potential #2. The field was divided into sections (15 ft long for each section), and three replicates were used for each treatment.
The overview of the experiment site, the solenoid valves for turn on/off the irrigation system, and the sensor/control box is shown in the figure below. Soil water content sensors (TEROS 10) were installed at two depth (15, 30 cm) at each section using soil water content-based irrigation, the same to the water potential sensor (Watermark 200SS-5). For the timer-based irrigation, there was no sensor installed at these sections. The sensor boxes are Vinduino R3 sensor stations. Program was developed for these boxes to record different types of sensor data, and communicating with Gateway to upload these data to a IoT platform.
In our study, sensor data was recorded and uploaded to an IoT platform called ‘allthingstalk’ for monitoring and automated irrigation control. Thresholds were set for each irrigation method to start and stop the irrigation. In the interface from the platform, the sensor data is updating with the designed time interval, for example 10 minutes per data. The status of the solenoid valves are presented in real-time, for example Black-close, and Green-open. The valve control can be done manually from the interface by clicking the buttons, while it could also be set up to run automatically through the program. In this study, we used manual option. We will further develop our system to achieve full automation.
3. IoT based irrigation system for open field tomato production
A set of experiments were conducted at the open field of the Penn State Russell E. Larson Agricultural Research Center (Furnace, PA) using tomatoes as a test crop. Four irrigation scheduling were designed and tested, including one based on crop evapotranspiration (ETc) (Treatment 1), two based on soil matric potential at different setpoints #1 -60kPa (Treatment 2), #2 -40kPa (Treatment 3) and a decision making system GesCoN (Treatment 4).
Experimental design (with four treatments) and field view of the sensor-based irrigation for open field tomato
Four main pipelines were set in the field, one for each treatment. For each treatment, 4 matric potential sensors (Watermark 200SS-5, Irrometer company, Inc., Riverside, CA) were installed in two bed locations at two depths (20cm, 40cm). The average matric potential of two sensors at 20 cm was used for the irrigation threshold in the treatment 2 and 3. Pressure sensors was installed behind the valves to indicate the status of the irrigation system (on or off). The major components of the data loggers included a base control board (Vinduino LLC, Temecula, CA) and a LoRaWAN wireless communication unit with antenna (LM130-H1, GlobalSat WorldCom Corp., New Taipei City, Taiwan).
Irrigation system setup, sensors, and data logger for the open field tomato irrigation test
4. Investigation of optimal sensor installation locations in drip irrigation for fruit orchards
It is important to investigate the water movement underground for soil moisture based irrigation, which can guide soil moisture sensors placement for a well-designed soil moisture based irrigation system. An IoT system was developed to measure the soil moisture during irrigation events. The system included sixteen soil water potential (SWP) sensors, a LoRa communication system, a local gateway, and an IoT cloud server. Four columns of sensors with four sensors at each column at different depths were installed along a dripline close to an emitter. The lateral distances of two neighboring sensors at the same depth were 0.1 m, and the vertical distances between two neighboring sensors in the same column was 0.2 m. The distribution of the sensors is illustrated as figure below.
Distribution of soil water potential sensors for the water movement test
A pre-installation procedure was applied according to the instruction. The sensors were cemented onto the pipe. A small hole was drilled at the bottom of PVC pipe to align with the slot at the top of the sensor housing. This allows any water that gets trapped in the pipe to drain away. Four SWP sensors were installed as a single assembly to make field installation easier. The wires from the lower sensors were routed out a hole below the upper sensors and then routed back into the pipe through a hole above the sensor to keep all the wires contained inside the pipe sections where possible. The SWP sensors were placed in the pre-selected locations in an orchard. The sensors were connected to a datalogger. Data was recorded and accessed with the ‘Allthings Talk’ IoT platform (details in the materials and methods #2).
The water potential sensors and field setup for the test
5. Preliminary study for IoT based automated irrigation system for peach orchard
A drip irrigation system was used in a peach orchard in the Penn State Fruit Research and Extension Center. The pressure gauge was regulated at 40 psi pressure for the irrigation system. Three watermark soil water potential sensors were installed at three depths (1, 2, and 3 ft) in the field. A solenoid valve was installed in-line on a PVC pipe to turn on or Off water to the test block remotely according to the soil moisture level.
The overview of the peach orchard, and the sensors and datalogger in the field
1. Sensor-based irrigation strategies
Soil water content: Figure below shows the daily average soil water content through days. Meanwhile, the rainfall and irrigation events were presented as well. In the Figure, WC #1 to WC #3 represent three water content sensors from top to bottom in the ground. Water content, rainfall, and irrigation are also illustrated. A threshold was set for the irrigation, in this study, 0.30 m3/m3 was used for the threshold. As shown in Figure, the water contents were slightly different at the three depths. The water content values from three sensors increased after a few hours of irrigation, and reached the field capacity at the end of the irrigation event.
Soil water potential: Figure also shows the daily average soil water potential through days. In the Figure, WP #1, and WP #2 represent two water potential sensors from top to bottom in the ground. Water potential, rainfall, and irrigation are also illustrated. A threshold was set for the irrigation, in this study, -80 kPa was used for the threshold (different soil type has different threshold). After the irrigation event, there is an obvious change of the soil water potential, which went close to -10 kPa. The water content reached the field capacity at the end of the irrigation event.
In the late season (September to Middle of October), we reduced the irrigation event to avoid crop cracking. We can see that the water potential is more sensitive than water content sensors. Even the water content readings only slightly lower than 0.3, while the water potential readings could rapidly drop to big numbers. Figure 3 shows the hourly soil water moisture change during an irrigation event. As shown in the figure, it took some time for the readings of the sensors to change after the irrigation starting, that because the sensors were at certain depth underground. Therefore, the location of the sensors to the emitters could be critical for how fast the changing of readings.
ET-based irrigation: as shown in figure, the daily ET, estimated deficit based on the calculated ET, irrigation and rainfall are presented from early June to the middle of October. In our calculation, if the rainfall/irrigation was larger than the current water deficit, the value of water deficit was set to zero at the end of the day.
Comparation of three irrigation strategies: at the end of the season, three different irrigation strategies were compared in the table below, including water use, and crop yield and quality. In overall, the water use for three irrigation setups were 8.7, 11 and 9.2 inches. ET-based and conventional irrigation had slightly higher water use than soil moisture sensor based method. The conventional method had least crop yield per tree, and the soil moisture based method achieved highest. The average crop size in soil moisture-based irrigation is smaller than other two methods. The first row of the block, there were more fruits but with smaller size by comparing to other rows, which may be affected by the efficacy of the chemical thinning. We will continue our experiment in the 2020 season, and may provide more data then.
|Irrigation strategies||Overall water use (inch)||Crop yield/tree (kg)||Crop size (g)||Hardness (Kg)||Soluble solids (Brix)|
2. IoT based automated irrigation system
The preliminary results indicated that the sensor data was updated in the IoT platform (website) as expected, and the developed IoT system worked properly for the valve control for the irrigation events. We observed that there were some data loss during the time, as well as the battery dead in the soil water content sensor box. These are the issues that we are going to resolve in our future studies. The results on the water use efficiency as well as crop yield on different irrigation strategies were not available for this experiment because the water supply was terminated due to the cold weather condition.
3. IoT based irrigation system for open field tomato production
The soil moisture sensors data were recorded through June to later September. When the sensor readings reached the setting thresholds, irrigation was applied. The change of sensor readings throughout the season reflected the irrigation events and rainfalls. 2019 was a dry year, we had applied water to the test field with high frequency.
Soil water potential readings throughout the growing season for the treatment 2
Throughout the harvest window, five harvests were conducted for sampled plants. 10 plants were sampled in each replicates for each treatment, so there were 40 plants were selected for harvesting in each treatments. Then crop yield was projected to Mg per hectare based on the density of crop planted. The fruits were categorized into different quality levels.
|Treatments||Crop yield (Mg ha-1) – Projected from the sampled harvested tomatoes|
|T1||46.35 bc||4.52 b||3.34 a||25.73 ab||54.21 bc||79.95 a|
|T2||52.71 ab||6.46 a||3.26 a||23.66 b||62.43 ab||86.09 a|
|T3||38.16 c||5.43 ab||3.75 a||27.49 a||47.34 c||74.83 a|
|T4||56.72 a||5.95 a||3.52 a||20.00 c||66.19 a||86.20 a|
XL: Extra Large, L: Large, M: Medium, TMY: Total Marketable yield, TY: Total yield. Treatments sharing the same letter is in the same group.
4. Investigation of optimal sensor installation locations in drip irrigation for fruit orchards
Figure below shows the soil tension measured by one column of SWP sensors under irrigation and rainfall. In total of 14 irrigation events were scheduled during June 26th to September 16th. It is clear that the soil tension decreased rapidly then remained stable for several days until next irrigation, which meant the water applied by drip irrigation established a favorable soil moisture condition.
Soil water potential from one column of the sensors throughout the season
Figure below shows the soil moisture profile after an irrigation event. In the start of irrigation (t=0), the value of SWP sensors were different, which means the distribution of soil moisture in profile are inhomogeneous after last irrigation. The soil both in bottom and top was drier than the soil in the middle according to the value of SWP sensors. The SWP value in the middle soil showed the similar soil moisture level. The soil in the bottom has the largest soil tension, which result from water loss from deep percolation. After irrigation, the soil moisture level at all locations were gradually increasing (the readings of soil water potentials getting smaller).
The soil moisture change over the time in an irrigation event from start to 5 hours later
5. Preliminary study for the automated irrigation in peach orchard
Figure below shows three irrigation events we applied to the peach orchard. Currently, the irrigation was applied remotely through the IoT platform to turn on/off the solenoid valve. In the future, with the defined threshold, the irrigation can be applied automatically by the IoT platform.
The sensor readings for three irrigation events in a peach orchard
The primary goal of educational program in this project is to achieve the adoption of the practices identified from the research outcomes by tree fruit and vegetable producers in Pennsylvania. The main approaches to achieve the goal include: 1) cultivate interest among producers through field visits, presentations, workshops, and publications; 2) demonstrate the selection, installation, configuration, and monitoring capability of sensor technologies and automated irrigation systems; and 3) assist growers in implementing precision sensing and irrigation system on their farms.
Educational activities in 2019:
In 2019, the major educational activities: need survey to growers on precision irrigation; in-service training of extension educators on precision irrigation; on-farm trails at commercial orchards (four commercial tree fruit growers in four apple orchards); extension fact sheets and other materials; presentations at academic, professional, and grower meetings. The detail on individual educational activities are in the milestone report.
Educational activities in 2020:
In 2020, the major educational activities: a webinar series (three webinars and a panel discussion) were organized by PIs through Penn State Extension on the topics of automated irrigation and application for PA growers; survey to growers on the status and need of irrigation systems in their farms; on-farm trials at commercial fruit orchards and vegetable fields (four commercial tree fruit growers in four apple orchards and one pear orchard, and one vegetable growers in high tunnel tomato field); presentations at academic, professional, and grower meetings; publications and extension articles. The detail on individual educational activities are in the milestone report.
1. Six growers agree to host irrigation research/demonstration plots on their farms.
Four tree fruit growers have participated in the project. The sensor systems were installed in their farms in early June. One set of soil moisture sensors (three soil water content sensors and one soil water potential sensor) and datalogger system were installed in an orchard from each participant, including Hollabaugh Bros. Inc. and Mt. Ridge Farm on June 4th, and El Vista Orchards, and Twin Springs Farms on June 12th. The real time soil moisture data can be recorded and monitored. The sensors are working normally, and the data could be accessed with Zentra Cloud using internet. The instruction of how to install the sensor and access the data by the growers are provided. Suggestions have provided to individuals for their irrigation scheduling during the season. We are preparing a season report to the participating growers now. In the report, the data results will be illustrated, and further discussion and suggestion will be provide to them.
Soil moisture sensor and datalogger installation in an orchard
In 2019, we tried to recruit some vegetable growers to attend this program, while encountered some difficulties because the growers we reached out to either in a very small scale or plain growers. In 2020 season, we will continue working with these tree fruit growers and also identify at least three vegetable growers, not only in open field, but also in greenhouses or high tunnels.
In 2020, we have conducted our field trials in six different farms, include four apple orchards, one pear orchards, and one high tunnel tomato field. For the four apple orchards, we just continued the same activities as year 2019, including monitoring the soil moisture level, assisting irrigation scheduling with the growers based on the sensor data. We have added one more sensor set in a pear orchard at Hollabaugh Brothers farm (the sensor set was installed in July). Also, we conducted a field trial at a commercial high tunnel tomato field – ReDivined farm (young lead growers), and the growers monitored the soil moisture throughout the growing season for their irrigation decision based on the sensing set we installed.
2. 50 farmers attend on-farm workshop(s) and report learning about sensor data collection, device installation and operation.
2019 Workshops and on-farm demonstration:
Four commercial orchards has been selected for on-farm demonstration using the sensor based irrigation technology. On each farm, orchard operation manager and other related workers have joined the sensor installation process, and instruction has provided to them for operation the system, and recording and understanding the sensor data. In overall, four individuals has participated this process with individual training. The number of farmer participants in 2019 is much less than the proposed number, which can be attributed to the fact that 2019 is the first year of the project, and we mainly focused on obtaining some preliminary results from commercial orchards, as well as some fresh experience from representative growers.
Besides the farmers, a in-service demonstration has been taken place for the extension educators (20 educators and agricultural service company individuals attended). These educators will deliver the information, obtain the need and answer questions from commercial growers in the future.
2020 Workshops and on-farm demonstration:
Besides the four fruit orchards in 2019, a pear orchard and a high tunnel tomato field were recruited for trials. The growers from these farms were involved and trained with sensor installation, data accessing and monitoring, and irrigation scheduling.
In 2020, a series of webinars were organized by the project PIs through Penn State Extension for fruit and vegetable growers, covering the fundamentals of drip irrigation systems, automated irrigation, and opportunities for growers to implement the irrigation technologies. Growers participated these webinars through Zoom, and there were 60 – 100 grower participants in these webinars. Questions and discussions were well addressed through these webinars. The three webinars are:
- The Basic of Drip Irrigation and its Application to Vegetable and Fruit Crops (Speaker: Dr. Troy Peters)
- Vinduino Irrigation Automation and its Use in Grape, Vegetable, and Fruit Production (Speaker: Mr. Reinier Van De Lee)
- The Successes and Challenges of Sensor-Based Irrigation for Crops in Pennsylvania (Speakers and panelists: Drs. Long He, Francesco Di Gioia, and Nick Flax).
3. 200 growers respond to a survey of irrigation practices and water usage.
A survey was carried out at different events in 2019 through Penn State Extension. The importance of ‘Monitoring plant and soil water with precision agriculture’ is among them. In this survey, 64 individuals responded to this question, and mainly indicated moderate importance of adopting precision plant and soil water monitoring.
We will take more opportunities to continue our survey more specifically on what are the advantages and obstacles for growers to adopt sensor-based soil water monitoring and irrigation.
In 2020, a grower survey was conducted after the Mid-Atlantic Fruit and Vegetable Convention, where a few presentations (as class-based workshop) were given to the growers. In total 265 growers participated the survey, and 164 of them answered the questions related to the automated irrigation system. Two major survey results are shown below. One grower may have answered with multiple items.
The major obstacles that affect growers to adopt the automated irrigation
From the results, we can see that large percentage of the growers concerned about the operation of automated irrigation system (27% concerned on the required high skills from sensor installation, to data interpreting, and system control; 21% of them concerned on the reliability of the system, including reliability of the sensor readings and control system). About 23% of growers concerned about the cost of the automated irrigation system, and thought it would not be economically feasibly to adopt it. About 15% of the growers thought they are not interested in the automated irrigation due to either no need for irrigation at all or just prefer to irrigate with experience. In the returned surveys, three growers reported that they are using automated irrigation. They are using timer-based automated irrigation instead of soil moisture sensors.
The preference of extension outreach methods for growers to receive information on irrigation technologies
The classroom-based workshops refer to trainings, presentations, and webinars. The in-field workshops typically refer to on-site sensor installation instruction and automated irrigation system operation training. The newsletter articles include extension articles in PA fruit news, Good Fruit Growers, and so on. Penn State Extension online materials include online courses, online fact sheet, and ‘LearnNow’ tutorial videos. The site visits mainly refer to addressing some concurrent questions during spring or summer orchard meetings. One grower may have voted for more than one methods in the survey. In the survey results, we can find that the proposed five methods were weighted very similarly except the ‘site visit’ was slightly less. We have been working with growers with all the methods in the past.
4. 50 growers attend a seminar and report learning new information about how to optimize irrigation.
A webinar series were hosted for our growers with learning some basic knowledge on irrigation, precision irrigation strategies, and application of automated irrigation in fruit and vegetable fields. There are 60-100 growers participated in these webinars. We also have presented our research outcomes in the project to more than 200 growers during Mid-Atlantic Fruit and Vegetable Convention, winter fruit school program, and spring orchard meetings.
5. 50 farmers attend on-farm workshop(s) and report learning about sensor data collection, device installation and operation.
We planned to have an on-farm workshop at one of our commercial farm trial in 2020. While due to the strict situation throughout the year, we cancelled our on-farm workshop. Instead, we have provided more like person-to-person training to our collaborative growers, including system installation, data access, and irrigation management and operation with soil moisture sensors.
We did have a few classroom-based workshops/presentations on the sensor-based irrigation systems, which has been included in our products.
6. 50 farmers receive individualized support to adopt precision irrigation technologies through phone calls and site visits.
We had worked with four tree fruit growers, and one vegetable growers on the on-farm trial with precision irrigation system. Meanwhile, we received questions by emails and phone calls about precision irrigation or regular drip irrigation from individual growers and extension educators (about five times). Of course, we answered questions to the growers on the irrigation related issues during of all our workshops and presentations.
7. 100 farmers that completed workshops and/or workbooks complete a survey describing their plans to make changes to their irrigation systems and any adopt precision irrigation technology before the end of the project.
8. 100 farmers access at least one of four on-line video tutorials in support of completing their farm irrigation workbooks.
9. 6 farmers that hosted on-farm research/demonstration plots complete a detailed survey to gauge their satisfaction with the technology. Specific topics to be addressed include operational cost and efficiency, total water savings, observed reductions in labor, satisfaction with crop quality and yield, and overall profitability.
We have been working with 5 growers for sensor-based irrigation technologies, including four tree fruit growers and one vegetable growers. We have obtained positive feedback from them for using the soil moisture sensor for guiding the irrigation. While, it is very difficult to request them to send a formal report since there is no comparation study in their farm to calculate the improvement of water use efficiency and crop yield.
10. 100 farmers attend one of four Extension workshops, where they complete a workbook guiding them through automated irrigation design and operations.