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WO2022040756A1 - Refill point or target deficit for crop irrigation - Google Patents

Refill point or target deficit for crop irrigation Download PDF

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Publication number
WO2022040756A1
WO2022040756A1 PCT/AU2021/050996 AU2021050996W WO2022040756A1 WO 2022040756 A1 WO2022040756 A1 WO 2022040756A1 AU 2021050996 W AU2021050996 W AU 2021050996W WO 2022040756 A1 WO2022040756 A1 WO 2022040756A1
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WIPO (PCT)
Prior art keywords
crop
deficit
soil moisture
water
soil
Prior art date
Application number
PCT/AU2021/050996
Other languages
French (fr)
Inventor
David John Aughton
Andrew William WESTERN
Dongryeol RYU
Danlu Guo
Arash PAREHKAR
Original Assignee
Rubicon Research Pty Ltd
The University Of Melbourne
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Filing date
Publication date
Priority claimed from AU2020903090A external-priority patent/AU2020903090A0/en
Application filed by Rubicon Research Pty Ltd, The University Of Melbourne filed Critical Rubicon Research Pty Ltd
Publication of WO2022040756A1 publication Critical patent/WO2022040756A1/en

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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G25/00—Watering gardens, fields, sports grounds or the like
    • A01G25/16—Control of watering
    • A01G25/167—Control by humidity of the soil itself or of devices simulating soil or of the atmosphere; Soil humidity sensors
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N19/00—Investigating materials by mechanical methods
    • G01N19/10—Measuring moisture content, e.g. by measuring change in length of hygroscopic filament; Hygrometers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0098—Plants or trees
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/24—Earth materials
    • G01N33/245—Earth materials for agricultural purposes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/24—Earth materials
    • G01N33/246—Earth materials for water content
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/02—Instruments for indicating weather conditions by measuring two or more variables, e.g. humidity, pressure, temperature, cloud cover or wind speed
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J2005/0077—Imaging
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01J—MEASUREMENT OF INTENSITY, VELOCITY, SPECTRAL CONTENT, POLARISATION, PHASE OR PULSE CHARACTERISTICS OF INFRARED, VISIBLE OR ULTRAVIOLET LIGHT; COLORIMETRY; RADIATION PYROMETRY
    • G01J5/00—Radiation pyrometry, e.g. infrared or optical thermometry
    • G01J5/02—Constructional details
    • G01J5/025—Interfacing a pyrometer to an external device or network; User interface
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01K—MEASURING TEMPERATURE; MEASURING QUANTITY OF HEAT; THERMALLY-SENSITIVE ELEMENTS NOT OTHERWISE PROVIDED FOR
    • G01K2213/00—Temperature mapping
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N2021/8466—Investigation of vegetal material, e.g. leaves, plants, fruits
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10048—Infrared image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30181—Earth observation
    • G06T2207/30188—Vegetation; Agriculture

Definitions

  • the present invention relates to methods and systems of ascertaining the refill point or target deficit to determine the need to irrigate a crop and the amount of water to apply using a computer-based system.
  • Soil Moisture Deficit is the difference between the amount of water actually in the soil and the amount of water that the soil can hold.
  • Full point is the amount of water that remains after gravitational forces have drained water from the large soil pores (macropores). Depending on the type of soil, this drainage may take from a few hours up to several days. When the large pores have drained, the soil is still wet, but not saturated. The soil is said to be at full point or field capacity. Full point or field capacity in most soils is at a soil-water tension of about -8 kPa.
  • Refill Point is the point at which a particular crop finds it difficult to extract water from the soil and begins to stress, slowing crop growth. For most cotton and grain crops, this usually occurs when the soil water potential is between -60 and -100 kPa.
  • the refill point changes during the season. Young plants have small roots that only have access to a limited part of the soil profile. As the plant grows, the roots can access more of the soil profile and therefore tolerate a larger soil moisture deficit before reaching refill point. Determining the refill point can be achieved by measuring soil water potential or by analysing daily water use patterns to determine when the crop is finding it difficult to remove water. If irrigation is not applied prior to soil water levels passing an accurate refill point, then a yield reduction will occur, depending on the stage of the crop.
  • Crop Water Stress Index was developed as a normalized index to quantify stress and overcome the effects of other environmental parameters affecting the relationship between stress and plant temperature. This index has been widely used for crop water status monitoring.
  • Crop Water Stress Index is a means of irrigation scheduling and crop water stress quantification based on canopy temperature measurements and prevailing meteorological conditions. Plant temperature is an indicator of plant water status because stomata close in response to soil water depletion causing a decrease in water uptake and an increase in leaf temperature.
  • Soil Infiltration is the process by which water on the ground surface enters the soil. It is commonly used in both hydrology and soil sciences.
  • the infiltration capacity is defined as the maximum rate of infiltration. It is most often measured in meters per day but can also be measured in other units of distance over time if necessary.
  • the infiltration capacity decreases as the soil moisture content of soils surface layers increases. If the precipitation rate exceeds the infiltration rate, runoff will usually occur unless there is some physical barrier.
  • Infiltrometers. permeameters and rainfall simulators are all devices that can be used to measure infiltration rates. Infiltration is caused by multiple factors including gravity, capillary forces, adsorption and osmosis. Many soil characteristics can also play a role in determining the rate at which infiltration occurs.
  • PCT/AU2010/001125 - discloses a device and method to measure the infiltration into the soil as a result of applying water to a crop during surface (flood) irrigation. This is the infiltration rate for the soil being in an unsaturated state (low soil moisture) prior to the measurement.
  • PCT/AU2020/050909 - discloses a device and method to measure the infiltration immediately following the irrigation of a crop using surface (flood) irrigation. This is the infiltration rate for the soil being in a saturated state (high soil moisture) prior to the measurement.
  • Soil infiltration can be used to characterize a soil’s hydrological properties.
  • a soil with a high infiltration rate is often associated with a light soil (a soil with higher sand content).
  • a soil with low infiltration rate is usually associated with a heavy soil (a soil with higher clay content).
  • Lighter soils are therefore more permeable and are better suited to more frequent irrigations with smaller quantities of water applied for each irrigation.
  • Heavier soils are less permeable and are more suited to becoming saturated during an irrigation, which in turn are less frequent and higher quantities of water applied.
  • Lower volumes of water application are more suited to drip or sprinkler methods of application. Higher volumes of water application are more suited to surface of sprinkler methods of application.
  • Permanent Wilting Point occurs when the soil reaches a point where the plant can no longer extract moisture. Once the soil has passed this point, water is held by the soil so tightly that the plant cannot extract it and will start to die.
  • the measurement of crop or plant stress is a method of determining the optimal time when a crop should be irrigated with objectives of maximizing yield and minimizing water use. Another method is through the measurement of soil moisture, whether directly, via a soil moisture probe or indirectly, deriving the soil moisture using weather data and the FAO-56 Penman-Monteith equation.
  • Crop stress is directly derived from the leaf temperature of the crop.
  • a plant transpires through the stomata in the leaf.
  • the leaf is cool when water is transpiring and heats up as transpiration slows as the crop is becoming stressed due to a lack of available water that the roots can access.
  • the plant can also be in stress state when the roots become saturated i.e. after heavy rainfall or an irrigation. Rainfall can also cool the leaf temperature of the plant which in turn indicates low crop stress, whereas the drop in temperature may be momentary, and the crop will assume a stress state not long after cessation of the rainfall. It is therefore necessary to differentiate between the potential stress states of the crop and decide which one is related to water deficiency.
  • the measurement of moisture in the soil can be shown in the graph of Fig. 1.
  • the graph illustrates the soil moisture reading against time for an irrigated area based on measurements from a soil moisture probe.
  • the graph represents the typical soil moisture readings as the soil moisture decreases due to the uptake of water/moisture by the plant roots and in turn, transpires via photosynthesis.
  • the soil moisture is also shown to increase following rainfall or irrigation.
  • the refill point (target deficit) 200 at which the crop is stressed, and should be irrigated is shown as a straight line. This is a typical approach when using the soil moisture method, although a different refill point can be used during different growth stages of certain crops (i.e. cotton).
  • the full point (saturated soil) 202 and the refill point 200 are characteristics of the water holding capacity of the soil in question. They are deduced from knowledge and analysis of the soil type and can be very subjective without undertaking detailed analysis.
  • the accuracy of the refill point (target deficit) 200 is a key determinant in the overall accuracy of the soil moisture method. The assumption that the refill point (target deficit) 200 is relatively constant alone reflects this inaccuracy, typically as a result of seasonal changes and root growth.
  • Irrigation occurs at points 200 to 210.
  • irrigation has occurred too early as it has not reached the refill point (target deficit) 200. Irrigation was not necessary at this point as the plant still had sufficient moisture to draw upon. The irrigation saturates the ground and extends beyond the full point (field capacity) 202.
  • irrigation occurred even earlier than at point 204 and has resulted in an extended period of water logging.
  • irrigation was delayed until refill point (target deficit) 200 had been reached. This delay resulted in adequate soil moisture and a shortened period of water logging compared with the irrigation at point 206.
  • Irrigation at point 210 had similar results to point 208 but was very close to the permanent wilting point 212 where plant damage could occur.
  • Fig. 2 is a graph similar to Fig. 1 showing measurements of soil moisture against time, based on data from a soil moisture probe in the ground of an area to be irrigated.
  • Fig. 3 is a graph displaying the calculated crop water stress index against time, based on leaf temperature of a crop in the same area to be irrigated as that in Fig. 2.
  • the time base is the same and shows the differences in measurements which have been simultaneously recorded.
  • the soil moisture (Fig.2) shows a relatively smooth downward trend, whereas the crop water stress index (Fig. 3) shows very “noisy”, or haphazard fluctuations in measured values. These fluctuations may, for example, be due to moisture on the leaves from frost or rainfall, or shadowing, which will lower the leaf temperature.
  • a method of ascertaining the refill point or target deficit to determine the need to irrigate a crop using a computer-based system including the steps of monitoring soil moisture directly, and/or indirectly, of said crop using field measurements to compute a soil moisture deficit, monitoring the thermal activity of leaf temperature of at least one plant of said crop allowing determination of crop water stress index, and interacting the results of said soil moisture deficit and said thermal activity monitoring to derive said refill point or target deficit.
  • said step of monitoring soil moisture directly is by using a moisture sensor in the ground to be irrigated.
  • said step of monitoring soil moisture indirectly may be based on the FAO-56 Penman-Monteith equation requiring site location, air temperature, humidity, radiation and wind speed data.
  • said interaction of the results of said soil moisture deficit monitoring and said thermal activity monitoring includes filtering to ensure said results trend in the same direction and determined to be approaching the refill point or target deficit.
  • said computer-based system is based on a smartphone.
  • said thermal activity is captured by a thermal camera, radiometer or thermal sensor associated with said smartphone.
  • the thermal activity may be captured by a thermal camera, radiometer or thermal sensor associated with said smartphone.
  • thermal activity is captured by a fixed ground based thermal camera, radiometer or thermal sensor, or thermal activity is captured by a ground based/deployable thermal camera, radiometer or thermal sensor.
  • computer-based system has access to weather and satellite/air thermal data.
  • a further aspect the derivation of the fill point or target deficit allows an irrigation schedule for an operator linked to a networked computer system to be determined in real time.
  • said interaction of the results of said soil moisture deficit monitoring and said thermal activity monitoring additionally includes one or more of crop feedback, machine learning, artificial intelligence, system identification and the water applied through irrigation.
  • said step of monitoring soil moisture deficit includes input of measured rainfall.
  • a still further aspect the invention may further include the steps of comparing said soil moisture deficit against said crop water stress index, if said crop water index is greater than a crop water stress threshold and said soil moisture deficit is greater than said refill point or target deficit, then a computer controlled irrigation of a calculated volume of water can occur.
  • said computer controlled irrigation of said calculated volume of water is based on crop yield feedback and said refill point or target deficit.
  • a further aspect of the invention the steps of monitoring soil moisture of said crop uses both direct and indirect field measurements to compute said soil moisture deficit.
  • the derivation of said refill point or target deficit includes consideration of one or more of said field measurements, rainfall, crop factor (coefficient), soil type, historic yields, crop conditions and water applied.
  • said derivation of said refill point or target deficit also includes interaction with determination of soil infiltration.
  • said determination of said soil infiltration interacts with the calculation of said irrigation schedule enabling a computer controlled irrigation of a calculated volume of water and depth to occur.
  • Fig. 1 shows a typical graph illustrating the soil moisture reading against time for an irrigated area based on measurements from a soil moisture probe
  • Fig. 2 is a graph similar to Fig. 1 to be used in combination with the graph shown in Fig. 3;
  • Fig. 3 is a graph displaying the calculated crop water stress index based on leaf temperature and aligned with Fig. 2 to show simultaneous soil moisture measurement and crop stress; and;
  • Fig. 4 is a flow chart showing the operation of a preferred embodiment of the present invention.
  • FIG. 4 The preferred embodiment of the present invention is shown in Fig. 4.
  • This embodiment of the present invention can be integrated into our computer controlled networked irrigation system that has been described in International Patent Application PCT/AU2018/050858 and previously incorporated into this specification.
  • the components and operation of the preferred embodiment shown in Figs. 2 to 6 of PCT/AU2018/050858 will not be repeated here in order to avoid unnecessary repetition of description.
  • This embodiment of the present invention can also be readily integrated into our computer controlled networked irrigation system that has been described in International Patent Application
  • PCT/AU2019/050919 and previously incorporated into this specification.
  • the components and operation of the preferred embodiment shown in Figs. 1 to 9 of PCT/AU2019/050919 will again not be repeated here in order to further avoid unnecessary repetition of description.
  • the present preferred embodiment can utilise the hardware, field measurements, derived data and downloadable data to provide inputs for ascertaining the refill point or target deficit for determination of the need to irrigate a crop.
  • the flow chart in Fig. 4 illustrates the major steps that result in the determination of the soil moisture deficit (SMD) at 230 and the determination of crop water stress index (CWSI) through thermal monitoring of leaf temperature at 232.
  • the interaction of steps 230 and 232 allow derivation of the refill point or target deficit (RFP) at 234.
  • the dynamic derivation of refill point or target deficit (RFP) at 234 allows determination at 236 of the volume or depth of water required for an irrigation schedule to be reviewed by an operator 256 linked to a networked computer system (not shown but described in the International Patent Applications previously referenced) that computes data in real time.
  • Step 230 receives inputs from step 238 from monitoring of soil moisture indirectly based on the FAO-56 Penman-Monteith equation.
  • This equation is well known and requires field measurements 240 of site location, air temperature, humidity, radiation and wind speed. These measurements can be provided by weather stations (not shown but described in the International Patent Applications previously referenced).
  • the Reference Potential Evapotranspiration (ETo) which constitutes the primary use of water in agriculture is calculated conventionally by the Penman-Monteith approach and can be estimated using only the meteorological measurements 240 and assumes that there is no limitation of water in soil available for the evapotranspiration.
  • K c integrates the effect of characteristics that distinguish the crop from the grass reference crop used to calculate ET 0 . Different crops have different K c values due to different crop characteristics. The K c value also changes over the growing season with changes in crop development and with changes affecting soil evaporation.
  • the derived Crop Evapotranspiration (ET C ) can be passed to step 230.
  • Step 230 also receives inputs from field measurements 244 taken from monitoring of soil moisture directly, based on a moisture sensor 242 in the ground to be irrigated.
  • Step 230 additionally receives measurements 246 of the time and depth of rainfall from the weather stations (not shown) at step 248.
  • the rainfall needs to be considered as this will increase the soil moisture deficit (SMD) and thus delay the required irrigation in view of access to non-irrigation water.
  • Step 235 determines soil infiltration which is used to compute the refill point RFP at step 234 and the depth (volume) of water to be applied for the specific irrigation at step 236.
  • Soil infiltration relates to the water holding capacity of a soil and is a key parameter in determining the optimal refill point (RFP) for an irrigation.
  • the infiltration characteristics of the soil is to be used, along with other field measurements identified in International Patent Application Nos. PCT/AU2010/001125 and PCT/AU2020/050909 previously disclosed, in the determination (using System Identification techniques) of the soil moisture deficit (SMD) at which water should be applied (RFP) and the necessary volume to apply.
  • the determination of crop water stress index (CWSI) at step 232 is calculated by thermal monitoring of leaf temperature from field measurements at 250.
  • the measurements can be taken by a thermal imaging camera commercially available from FLIR Systems, Inc. for attachment to Apple IOS or Android smartphones as discussed in PCT/AU2019/050919.
  • a point-scale thermal sensor (not shown), e.g. a Melexis field of view radiometer could also be used.
  • Such thermal sensors have a range of field of view, and most commercially available thermal sensors have a laser pointer to show the target. The laser pointer does not show the field of view.
  • the sensor could be combined with the smartphone to check the field of view using a laser pointer that could shoot a circle that matches the field of view and make it easier to measure leaf temperature of the desired area.
  • the thermal monitoring can be undertaken by handheld devices as previously described, or ground based/redeployable sensors pointed at plant foliage.
  • Crop feedback 252 can include inputs 254 from one or more of machine learning, artificial intelligence and system identification from a growing information base stored in the networked computer system and/or the smartphone discussed in International Patent Application Nos. PCT/AU2018/050858 and PCT/AU2019/050919.
  • Steps 258, 260 and 262 provide a feedback loop using a filtering process to avoid the computation processes dealing with ‘noisy or highly variable’ crop water stress index (CWSI) data based on thermal monitoring of leaf temperature.
  • the ‘noisy’ crop water stress index (CWSI) data has been previously discussed in the BACKGROUND of this specification.
  • Step 258 is based on a pre-determined refill zone (RFZ) value that ensures the soil moisture deficit (SMD) is approaching the refill point or target deficit (RFP), and therefore the crop water stress index (CWSI) should have a stable value.
  • the pre-determined refill zone (RFZ) could be an offset from a previous irrigation refill point or target deficit (RFP).
  • Step 260 ensures the crop water stress index (CWSI) is also at a crop water stress threshold (CWST) that ensures the soil moisture deficit (SMD) is approaching the refill point or target deficit (RFP).
  • CWSI crop water stress index
  • CWST crop water stress threshold
  • SMD soil moisture deficit
  • RFP target deficit
  • Step 262 is the final logic function test of when to irrigate. Unless the soil moisture deficit (SMD) has reached the refill point or target deficit (RFP) irrigation cannot automatically occur but could be overridden by the operator 256.
  • SMD soil moisture deficit
  • RFP target deficit
  • the feedback loop associated steps 258, 260 and 262 represent the real-time (continuous) nature of the computation and that the refill point or target deficit (RFP) is dynamic and needs to be computed for each irrigation.
  • the irrigation will occur at step 264 based on the determination of the water required at step 236.
  • Step 236 is a parallel outcome of the computation of refill point or target deficit (RFP) at step 234. They are both an output of the maximum yield/minimum water application objective of the soil infiltration SI computation at step 235 and use crop feedback at step 252 containing information on the crop type, stage of growth, health of the crop, forecast and historical yield data.
  • the present embodiment monitors soil moisture directly through a soil moisture sensor 242, and indirectly using field measurements to compute a soil moisture deficit based on the FAO-56 Penman-Monteith equation at step 238.
  • the prior art would only consider one of these methods.
  • a network of weather stations (including rainfall) allows the spatial mapping of soil moisture over large areas (relative low cost)
  • the embodiment proposes the combination of these two methods to produce a more accurate method of determining the optimal time when a crop should be irrigated and how much to apply.
  • crop water stress index measurement it will be possible to dynamically determine the refill point. This can be determined in real-time using the regular measurement of crop stress as the soil moisture approaches a notional refill zone. It will also be possible to learn the refill characteristics of the soil and associated crop in question over time.
  • An important feature of this embodiment is that a heightened crop stress measurement, when the soil moisture is in (or approaches) the refill zone, can be attributed to a depletion in available water for the plant and the need to apply water (irrigate). High crop stress readings due to other reasons can therefore be readily filtered using this technique.
  • the preferred embodiment can dynamically (in close to real-time) determine when to irrigate and how much water to apply in order to maximise yield (agricultural production) and minimise the water applied to the crop.
  • the RFP (when to irrigate) is determined using all field inputs, i.e. those to produce SMD and CWSI plus feedback on yield data, both short term (during the growing season) and long term (from season to season).
  • the computation of RFP uses system identification techniques (machine learning, artificial intelligence) as part of the computer-based system.
  • SMD contains information on water in the soil whereas CWSI checks if crops are water-stressed, regardless of the actual water in the soil.
  • SMD falls within RFZ, it indicates that crops may start feeling water stress (this might indicate time for water ordering if water delivery takes days).
  • the actual SMD at which crops feel water stress varies with crop types, weather condition, and growth stage. So, within a range of possible values of SMD for irrigation, CWSI can indicate time for irrigation.
  • the improved accuracy achieved using the method of preferred embodiment is dependent on the frequency of measurements of crop stress as the soil moisture approaches the refill point. These are typically of the order of daily measurements or better. If required, the measurements can be taken reasonably frequently, e.g. of the order an hour or better. This is often beyond the capacity of satellite/air methods and best achieved using ground point source methods and relying on satellite/air for the provision of spatial variability of crop stress measurements. Satellite measurements can vary from 3 days to several weeks depending on the satellite provider, and issues such as cloud cover when the satellite passed over the field in question. Similarly, air measurements (aircraft, drones etc.) are typically limited for when these services are available for use by the farmer/irrigator. It may also be possible to obtain higher frequency of CWSI data by deployment of personal low cost drones.
  • Embodiments of the invention have been described above by way of non-limiting example only. In practice, a plurality of weather stations, flow gates, flow meters, radiometers and soil moisture sensors are scattered around the irrigation district to provide an extensive irrigation system. Variations and modifications to the embodiments may be made without departing from the scope of the invention.

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Abstract

The invention provides a method of ascertaining the refill point or target deficit (234) to determine the need to irrigate a crop (236) using a computer-based system. The method including the steps of monitoring soil moisture directly (242), and/or indirectly (238), of the crop using field measurements to compute a soil moisture deficit (230), monitoring the thermal activity of leaf temperature of at least one plant of the crop allowing determination of crop water stress index (232), and interacting the results of said soil moisture deficit (230) and said thermal activity monitoring (232) to derive said refill point or target deficit (230).

Description

REFILL POINT OR TARGET DEFICIT FOR CROP IRRIGATION
FIELD
[0001] The present invention relates to methods and systems of ascertaining the refill point or target deficit to determine the need to irrigate a crop and the amount of water to apply using a computer-based system.
BACKGROUND
[0002] The invention is a further enhancement over our earlier International Patent Application Nos. PCT/AU2018/050858 and PCT/AU2019/050919, the full contents of each application including description, claims and drawings are assumed to have been read and incorporated herein by reference to avoid repetition of description.
[0003] International Patent Application No. PCT/AU2018/050858 discloses spatially deriving soil moisture for any location within an irrigation district and the ability to map the soil moisture at any point in time and spatially across the irrigation district.
[0004] International Patent Application No. PCT/AU2019/050919 discloses a method of plant stress determination using, at ground level, a hand-held computer-based camera system having thermal imaging and visual imaging to capture foliage at close proximity to provide high resolution images/video thereof. The images are analyzed to form a composite image and the thermal activity of the composite image is determined together with the photosynthesis state of the plant from the composite image using the computer-based camera system. The plant stress is then derived from this determination.
[0005] In this specification some terms need to be defined and are as follows: Soil Moisture Deficit is the difference between the amount of water actually in the soil and the amount of water that the soil can hold.
Full point (field capacity) is the amount of water that remains after gravitational forces have drained water from the large soil pores (macropores). Depending on the type of soil, this drainage may take from a few hours up to several days. When the large pores have drained, the soil is still wet, but not saturated. The soil is said to be at full point or field capacity. Full point or field capacity in most soils is at a soil-water tension of about -8 kPa.
Refill Point (target deficit) is the point at which a particular crop finds it difficult to extract water from the soil and begins to stress, slowing crop growth. For most cotton and grain crops, this usually occurs when the soil water potential is between -60 and -100 kPa. The refill point changes during the season. Young plants have small roots that only have access to a limited part of the soil profile. As the plant grows, the roots can access more of the soil profile and therefore tolerate a larger soil moisture deficit before reaching refill point. Determining the refill point can be achieved by measuring soil water potential or by analysing daily water use patterns to determine when the crop is finding it difficult to remove water. If irrigation is not applied prior to soil water levels passing an accurate refill point, then a yield reduction will occur, depending on the stage of the crop.
Crop Water Stress Index (CWSI) was developed as a normalized index to quantify stress and overcome the effects of other environmental parameters affecting the relationship between stress and plant temperature. This index has been widely used for crop water status monitoring. Crop Water Stress Index is a means of irrigation scheduling and crop water stress quantification based on canopy temperature measurements and prevailing meteorological conditions. Plant temperature is an indicator of plant water status because stomata close in response to soil water depletion causing a decrease in water uptake and an increase in leaf temperature.
Soil Infiltration is the process by which water on the ground surface enters the soil. It is commonly used in both hydrology and soil sciences. The infiltration capacity is defined as the maximum rate of infiltration. It is most often measured in meters per day but can also be measured in other units of distance over time if necessary. The infiltration capacity decreases as the soil moisture content of soils surface layers increases. If the precipitation rate exceeds the infiltration rate, runoff will usually occur unless there is some physical barrier. Infiltrometers. permeameters and rainfall simulators are all devices that can be used to measure infiltration rates. Infiltration is caused by multiple factors including gravity, capillary forces, adsorption and osmosis. Many soil characteristics can also play a role in determining the rate at which infiltration occurs. Further methods to measure infiltration in the soil are also described in the following Rubicon International Patent Applications: a. PCT/AU2010/001125 - discloses a device and method to measure the infiltration into the soil as a result of applying water to a crop during surface (flood) irrigation. This is the infiltration rate for the soil being in an unsaturated state (low soil moisture) prior to the measurement. b. PCT/AU2020/050909 - discloses a device and method to measure the infiltration immediately following the irrigation of a crop using surface (flood) irrigation. This is the infiltration rate for the soil being in a saturated state (high soil moisture) prior to the measurement.
Soil infiltration can be used to characterize a soil’s hydrological properties. A soil with a high infiltration rate is often associated with a light soil (a soil with higher sand content). A soil with low infiltration rate is usually associated with a heavy soil (a soil with higher clay content). Lighter soils are therefore more permeable and are better suited to more frequent irrigations with smaller quantities of water applied for each irrigation. Heavier soils are less permeable and are more suited to becoming saturated during an irrigation, which in turn are less frequent and higher quantities of water applied.
Lower volumes of water application are more suited to drip or sprinkler methods of application. Higher volumes of water application are more suited to surface of sprinkler methods of application.
[0006] Permanent Wilting Point occurs when the soil reaches a point where the plant can no longer extract moisture. Once the soil has passed this point, water is held by the soil so tightly that the plant cannot extract it and will start to die.
[0007] The measurement of crop or plant stress is a method of determining the optimal time when a crop should be irrigated with objectives of maximizing yield and minimizing water use. Another method is through the measurement of soil moisture, whether directly, via a soil moisture probe or indirectly, deriving the soil moisture using weather data and the FAO-56 Penman-Monteith equation.
[0008] The direct measurement of crop stress is a very good indicator of when to irrigate, but on its own can be unreliable. Crop stress is directly derived from the leaf temperature of the crop. During productive photosynthesis a plant transpires through the stomata in the leaf. The leaf is cool when water is transpiring and heats up as transpiration slows as the crop is becoming stressed due to a lack of available water that the roots can access. The plant can also be in stress state when the roots become saturated i.e. after heavy rainfall or an irrigation. Rainfall can also cool the leaf temperature of the plant which in turn indicates low crop stress, whereas the drop in temperature may be momentary, and the crop will assume a stress state not long after cessation of the rainfall. It is therefore necessary to differentiate between the potential stress states of the crop and decide which one is related to water deficiency.
[0009] The measurement of moisture in the soil, either directly, or indirectly, can be shown in the graph of Fig. 1. The graph illustrates the soil moisture reading against time for an irrigated area based on measurements from a soil moisture probe. The graph represents the typical soil moisture readings as the soil moisture decreases due to the uptake of water/moisture by the plant roots and in turn, transpires via photosynthesis. The soil moisture is also shown to increase following rainfall or irrigation.
[0010] In the graph the refill point (target deficit) 200 at which the crop is stressed, and should be irrigated, is shown as a straight line. This is a typical approach when using the soil moisture method, although a different refill point can be used during different growth stages of certain crops (i.e. cotton). The full point (saturated soil) 202 and the refill point 200 are characteristics of the water holding capacity of the soil in question. They are deduced from knowledge and analysis of the soil type and can be very subjective without undertaking detailed analysis. The accuracy of the refill point (target deficit) 200 is a key determinant in the overall accuracy of the soil moisture method. The assumption that the refill point (target deficit) 200 is relatively constant alone reflects this inaccuracy, typically as a result of seasonal changes and root growth. [0011] Irrigation occurs at points 200 to 210. At point 204, irrigation has occurred too early as it has not reached the refill point (target deficit) 200. Irrigation was not necessary at this point as the plant still had sufficient moisture to draw upon. The irrigation saturates the ground and extends beyond the full point (field capacity) 202. At point 206 irrigation occurred even earlier than at point 204 and has resulted in an extended period of water logging. At point 208 irrigation was delayed until refill point (target deficit) 200 had been reached. This delay resulted in adequate soil moisture and a shortened period of water logging compared with the irrigation at point 206. Irrigation at point 210 had similar results to point 208 but was very close to the permanent wilting point 212 where plant damage could occur.
[0012] Fig. 2 is a graph similar to Fig. 1 showing measurements of soil moisture against time, based on data from a soil moisture probe in the ground of an area to be irrigated. Fig. 3 is a graph displaying the calculated crop water stress index against time, based on leaf temperature of a crop in the same area to be irrigated as that in Fig. 2. The time base is the same and shows the differences in measurements which have been simultaneously recorded.
[0013] The differences between the graphs shown in Figs. 2 and 3 illustrate practical real life disparities between these measurements as an indication of plant or crop stress. The dotted lines 214, 216, 218 and 220 indicate the times when irrigation occurred. It can be seen that the graphs in Figs. 2 and 3 between irrigations 214 and 216, do not show a smooth downward trend. The graphs show the measured values can fluctuate up and down, even though the trend is generally downward. These fluctuations, if taken individually, can provide a false impression of soil moisture (Fig. 2) or crop water stress index (Fig. 3) that may result in an early and unnecessary prediction of the commencement of irrigation. Between irrigations 218 and 220 the soil moisture (Fig.2) shows a relatively smooth downward trend, whereas the crop water stress index (Fig. 3) shows very “noisy”, or haphazard fluctuations in measured values. These fluctuations may, for example, be due to moisture on the leaves from frost or rainfall, or shadowing, which will lower the leaf temperature.
[0014] It is not admitted that any of the information in this patent specification is common general knowledge, or that the person skilled in the art could be reasonably expected to ascertain or understand it, regard it as relevant, or combine it in any way before the priority date. OBJECT OF THE INVENTION
[0015] It is an object of the invention to provide a method that improves the accuracy of determining the refill point or target deficit at which to irrigate a crop using a computer-based system.
SUMMARY OF THE INVENTION
[0016] In one aspect of the invention there is provided a method of ascertaining the refill point or target deficit to determine the need to irrigate a crop using a computer-based system, said method including the steps of monitoring soil moisture directly, and/or indirectly, of said crop using field measurements to compute a soil moisture deficit, monitoring the thermal activity of leaf temperature of at least one plant of said crop allowing determination of crop water stress index, and interacting the results of said soil moisture deficit and said thermal activity monitoring to derive said refill point or target deficit.
[0017] Preferably said step of monitoring soil moisture directly is by using a moisture sensor in the ground to be irrigated. Further said step of monitoring soil moisture indirectly may be based on the FAO-56 Penman-Monteith equation requiring site location, air temperature, humidity, radiation and wind speed data.
[0018] In a preferred embodiment said interaction of the results of said soil moisture deficit monitoring and said thermal activity monitoring includes filtering to ensure said results trend in the same direction and determined to be approaching the refill point or target deficit.
[0019] In a further aspect said computer-based system is based on a smartphone.
[0020] In a further embodiment said thermal activity is captured by a thermal camera, radiometer or thermal sensor associated with said smartphone. The thermal activity may be captured by a thermal camera, radiometer or thermal sensor associated with said smartphone.
[0021] Another aspect allows said thermal activity to be captured by a fixed ground based thermal camera, radiometer or thermal sensor, or thermal activity is captured by a ground based/deployable thermal camera, radiometer or thermal sensor. [0022] Preferably said computer-based system has access to weather and satellite/air thermal data.
[0023] A further aspect the derivation of the fill point or target deficit allows an irrigation schedule for an operator linked to a networked computer system to be determined in real time.
[0024] In yet a further aspect of the invention said interaction of the results of said soil moisture deficit monitoring and said thermal activity monitoring additionally includes one or more of crop feedback, machine learning, artificial intelligence, system identification and the water applied through irrigation. Preferably said step of monitoring soil moisture deficit includes input of measured rainfall.
[0025] A still further aspect the invention may further include the steps of comparing said soil moisture deficit against said crop water stress index, if said crop water index is greater than a crop water stress threshold and said soil moisture deficit is greater than said refill point or target deficit, then a computer controlled irrigation of a calculated volume of water can occur.
[0026] In yet a further aspect of the invention said computer controlled irrigation of said calculated volume of water is based on crop yield feedback and said refill point or target deficit.
[0027] A further aspect of the invention the steps of monitoring soil moisture of said crop uses both direct and indirect field measurements to compute said soil moisture deficit.
[0028] In a preferred embodiment the derivation of said refill point or target deficit includes consideration of one or more of said field measurements, rainfall, crop factor (coefficient), soil type, historic yields, crop conditions and water applied.
In yet a further aspect of the invention said derivation of said refill point or target deficit also includes interaction with determination of soil infiltration. In addition, said determination of said soil infiltration interacts with the calculation of said irrigation schedule enabling a computer controlled irrigation of a calculated volume of water and depth to occur. BRIEF DESCRIPTION OF DRAWINGS
[0029] An embodiment of the method and apparatus will now be described by way of example only with reference to the accompanying drawing in which:
[0030] Fig. 1 shows a typical graph illustrating the soil moisture reading against time for an irrigated area based on measurements from a soil moisture probe;
[0031] Fig. 2 is a graph similar to Fig. 1 to be used in combination with the graph shown in Fig. 3;
[0032] Fig. 3 is a graph displaying the calculated crop water stress index based on leaf temperature and aligned with Fig. 2 to show simultaneous soil moisture measurement and crop stress; and;
[0033] Fig. 4 is a flow chart showing the operation of a preferred embodiment of the present invention.
DESCRIPTION OF EMBODIMENTS
[0034] The preferred embodiment of the present invention is shown in Fig. 4. This embodiment of the present invention can be integrated into our computer controlled networked irrigation system that has been described in International Patent Application PCT/AU2018/050858 and previously incorporated into this specification. The components and operation of the preferred embodiment shown in Figs. 2 to 6 of PCT/AU2018/050858 will not be repeated here in order to avoid unnecessary repetition of description. This embodiment of the present invention can also be readily integrated into our computer controlled networked irrigation system that has been described in International Patent Application
PCT/AU2019/050919 and previously incorporated into this specification. The components and operation of the preferred embodiment shown in Figs. 1 to 9 of PCT/AU2019/050919 will again not be repeated here in order to further avoid unnecessary repetition of description. The present preferred embodiment can utilise the hardware, field measurements, derived data and downloadable data to provide inputs for ascertaining the refill point or target deficit for determination of the need to irrigate a crop.
[0035] The flow chart in Fig. 4 illustrates the major steps that result in the determination of the soil moisture deficit (SMD) at 230 and the determination of crop water stress index (CWSI) through thermal monitoring of leaf temperature at 232. The interaction of steps 230 and 232 allow derivation of the refill point or target deficit (RFP) at 234. The dynamic derivation of refill point or target deficit (RFP) at 234 allows determination at 236 of the volume or depth of water required for an irrigation schedule to be reviewed by an operator 256 linked to a networked computer system (not shown but described in the International Patent Applications previously referenced) that computes data in real time.
[0036] Step 230 receives inputs from step 238 from monitoring of soil moisture indirectly based on the FAO-56 Penman-Monteith equation. This equation is well known and requires field measurements 240 of site location, air temperature, humidity, radiation and wind speed. These measurements can be provided by weather stations (not shown but described in the International Patent Applications previously referenced). The Reference Potential Evapotranspiration (ETo), which constitutes the primary use of water in agriculture is calculated conventionally by the Penman-Monteith approach and can be estimated using only the meteorological measurements 240 and assumes that there is no limitation of water in soil available for the evapotranspiration. A Crop Coefficient or Factor (KC> is used to convert the weather derived Reference Evapotranspiration (ET0) to an estimate of Crop Evapotranspiration (ETC) using the following formula: ETC=KCXET0
Kc integrates the effect of characteristics that distinguish the crop from the grass reference crop used to calculate ET0. Different crops have different Kc values due to different crop characteristics. The Kc value also changes over the growing season with changes in crop development and with changes affecting soil evaporation. The derived Crop Evapotranspiration (ETC) can be passed to step 230.
[0037] Step 230 also receives inputs from field measurements 244 taken from monitoring of soil moisture directly, based on a moisture sensor 242 in the ground to be irrigated.
[0038] Step 230 additionally receives measurements 246 of the time and depth of rainfall from the weather stations (not shown) at step 248. The rainfall needs to be considered as this will increase the soil moisture deficit (SMD) and thus delay the required irrigation in view of access to non-irrigation water. Step 235 determines soil infiltration which is used to compute the refill point RFP at step 234 and the depth (volume) of water to be applied for the specific irrigation at step 236. Soil infiltration relates to the water holding capacity of a soil and is a key parameter in determining the optimal refill point (RFP) for an irrigation. The infiltration characteristics of the soil is to be used, along with other field measurements identified in International Patent Application Nos. PCT/AU2010/001125 and PCT/AU2020/050909 previously disclosed, in the determination (using System Identification techniques) of the soil moisture deficit (SMD) at which water should be applied (RFP) and the necessary volume to apply.
[0039] The determination of crop water stress index (CWSI) at step 232 is calculated by thermal monitoring of leaf temperature from field measurements at 250. The measurements can be taken by a thermal imaging camera commercially available from FLIR Systems, Inc. for attachment to Apple IOS or Android smartphones as discussed in PCT/AU2019/050919. A point-scale thermal sensor (not shown), e.g. a Melexis field of view radiometer could also be used. Such thermal sensors have a range of field of view, and most commercially available thermal sensors have a laser pointer to show the target. The laser pointer does not show the field of view. The sensor could be combined with the smartphone to check the field of view using a laser pointer that could shoot a circle that matches the field of view and make it easier to measure leaf temperature of the desired area. The thermal monitoring can be undertaken by handheld devices as previously described, or ground based/redeployable sensors pointed at plant foliage.
[0040] The determination of crop water stress index (CWSI) at step 232 interacts with step 234 together with the determination of the soil moisture deficit (SMD) at 230 for the derivation of the refill point or target deficit (RFP) at step 234. Step 234 also includes input from crop feedback at step 252. Crop feedback 252 can include inputs 254 from one or more of machine learning, artificial intelligence and system identification from a growing information base stored in the networked computer system and/or the smartphone discussed in International Patent Application Nos. PCT/AU2018/050858 and PCT/AU2019/050919.
[0041] Steps 258, 260 and 262 provide a feedback loop using a filtering process to avoid the computation processes dealing with ‘noisy or highly variable’ crop water stress index (CWSI) data based on thermal monitoring of leaf temperature. The ‘noisy’ crop water stress index (CWSI) data has been previously discussed in the BACKGROUND of this specification. [0042] Step 258 is based on a pre-determined refill zone (RFZ) value that ensures the soil moisture deficit (SMD) is approaching the refill point or target deficit (RFP), and therefore the crop water stress index (CWSI) should have a stable value. For example, the pre-determined refill zone (RFZ) could be an offset from a previous irrigation refill point or target deficit (RFP).
[0043] Step 260 ensures the crop water stress index (CWSI) is also at a crop water stress threshold (CWST) that ensures the soil moisture deficit (SMD) is approaching the refill point or target deficit (RFP).
[0044] Step 262 is the final logic function test of when to irrigate. Unless the soil moisture deficit (SMD) has reached the refill point or target deficit (RFP) irrigation cannot automatically occur but could be overridden by the operator 256.
[0045] The feedback loop associated steps 258, 260 and 262 represent the real-time (continuous) nature of the computation and that the refill point or target deficit (RFP) is dynamic and needs to be computed for each irrigation. The irrigation will occur at step 264 based on the determination of the water required at step 236. Step 236 is a parallel outcome of the computation of refill point or target deficit (RFP) at step 234. They are both an output of the maximum yield/minimum water application objective of the soil infiltration SI computation at step 235 and use crop feedback at step 252 containing information on the crop type, stage of growth, health of the crop, forecast and historical yield data.
[0046] The present embodiment monitors soil moisture directly through a soil moisture sensor 242, and indirectly using field measurements to compute a soil moisture deficit based on the FAO-56 Penman-Monteith equation at step 238. Typically, the prior art would only consider one of these methods. These separate methods have strengths and weaknesses by themselves as follows:
1. Soil Moisture Sensor a. Strengths:
(i) Ability to provide a relative depth profile of soil moisture (ii) Relative trend in soil moisture (rapid uptake by plant during active photosynthetic growth, and corresponding slowing in uptake as soil moisture is depleted) b. Weaknesses:
(i) Provides relative soil moisture, not absolute. Need to derive soil moisture content by site-specific calibration which is very much an estimate at best.
(ii) Point source information. Need a number of sensors (relatively expensive) to get spatial view.
2, FAO-56 Penman-Monteith equation a. Strengths:
(i) Computes changes in absolute soil moisture as crop water consumption
(ii) A network of weather stations (including rainfall) allows the spatial mapping of soil moisture over large areas (relative low cost)
(iii) Can produce forecast soil moisture when combined with weather forecast input b. Weaknesses:
(i) Cannot give a depth profile of soil moisture without knowing the absolute soil moisture at a certain time
(ii) Derived soil moisture (subject to accuracy of Crop Factor (Coefficient) and overall FAO-56 Penman-Monteith equation accuracy)
(iii) Net rainfall contribution to soil moisture is uncertain (e.g., light rain may not reach or infiltrate soil, heavy rain may run off).
(iv) Net drainage depletion of soil moisture by flow between the root zone and deeper soil layers is uncertain.
[0047] The embodiment proposes the combination of these two methods to produce a more accurate method of determining the optimal time when a crop should be irrigated and how much to apply. When combined with crop water stress index measurement it will be possible to dynamically determine the refill point. This can be determined in real-time using the regular measurement of crop stress as the soil moisture approaches a notional refill zone. It will also be possible to learn the refill characteristics of the soil and associated crop in question over time. An important feature of this embodiment is that a heightened crop stress measurement, when the soil moisture is in (or approaches) the refill zone, can be attributed to a depletion in available water for the plant and the need to apply water (irrigate). High crop stress readings due to other reasons can therefore be readily filtered using this technique.
[0048] The determination of RFP based on (combining outputs from) (i) water balance via soil moisture sensor 242, (ii) water balance by FAO-56 Penman-Monteith equation 238, (iii) crop water stress 232 measured via leaf temperature. Outputs from i) and ii) can also provide information on how much water should be added to the irrigated area. Crops can be water- stressed at different soil moisture levels dependent on atmospheric demand and different growth stages. Daily variation of soil water from soil moisture sensor 242 against the rate predicted by FAO-56 Penman-Monteith equation (actual or potential) can show reduced water use by crops due to stress. This can also be done by analysing curvature of time series of soil moisture sensor data.
[0049] The preferred embodiment can dynamically (in close to real-time) determine when to irrigate and how much water to apply in order to maximise yield (agricultural production) and minimise the water applied to the crop. The RFP (when to irrigate) is determined using all field inputs, i.e. those to produce SMD and CWSI plus feedback on yield data, both short term (during the growing season) and long term (from season to season). The computation of RFP uses system identification techniques (machine learning, artificial intelligence) as part of the computer-based system. SMD contains information on water in the soil whereas CWSI checks if crops are water-stressed, regardless of the actual water in the soil. Once SMD falls within RFZ, it indicates that crops may start feeling water stress (this might indicate time for water ordering if water delivery takes days). The actual SMD at which crops feel water stress varies with crop types, weather condition, and growth stage. So, within a range of possible values of SMD for irrigation, CWSI can indicate time for irrigation.
[0050] The improved accuracy achieved using the method of preferred embodiment is dependent on the frequency of measurements of crop stress as the soil moisture approaches the refill point. These are typically of the order of daily measurements or better. If required, the measurements can be taken reasonably frequently, e.g. of the order an hour or better. This is often beyond the capacity of satellite/air methods and best achieved using ground point source methods and relying on satellite/air for the provision of spatial variability of crop stress measurements. Satellite measurements can vary from 3 days to several weeks depending on the satellite provider, and issues such as cloud cover when the satellite passed over the field in question. Similarly, air measurements (aircraft, drones etc.) are typically limited for when these services are available for use by the farmer/irrigator. It may also be possible to obtain higher frequency of CWSI data by deployment of personal low cost drones.
[0051] The preferred embodiment is not restricted to its integration with the inventions disclosed in International Patent Application Nos. PCT/AU2018/050858 and
PCT/AU2019/050919 as it can be readily incorporated into similar networked computer irrigation systems.
[0052] Embodiments of the invention have been described above by way of non-limiting example only. In practice, a plurality of weather stations, flow gates, flow meters, radiometers and soil moisture sensors are scattered around the irrigation district to provide an extensive irrigation system. Variations and modifications to the embodiments may be made without departing from the scope of the invention.

Claims

THE CLAIMS DEFINING THE INVENTION ARE AS FOLLOWS:
1. A method of ascertaining the refill point or target deficit to determine the need to irrigate a crop using a computer-based system, said method including the steps of monitoring soil moisture directly, and/or indirectly, of said crop using field measurements to compute a soil moisture deficit, monitoring the thermal activity of leaf temperature of at least one plant of said crop allowing determination of crop water stress index, and interacting the results of said soil moisture deficit and said thermal activity monitoring to derive said refill point or target deficit.
2. The method of claim 1, wherein said step of monitoring soil moisture directly is by using a moisture sensor in the ground to be irrigated.
3. The method of claim 1 or 2, wherein said step of monitoring soil moisture indirectly is based on the FAO-56 Penman-Monteith equation requiring site location, air temperature, humidity, radiation and wind speed data.
4. The method of any one of the preceding claims, wherein said interaction of the results of said soil moisture deficit monitoring and said thermal activity monitoring includes filtering to ensure said results trend in the same direction and determined to be approaching the refill point or target deficit.
5. The method of any one of the preceding claims, wherein said computer-based system is based on a smartphone.
6. The method of claim 5, wherein said thermal activity is captured by a thermal camera, radiometer or thermal sensor associated with said smartphone.
7. The method of any one of claims 1 to 4, wherein said thermal activity is captured by a fixed ground based thermal camera, radiometer or thermal sensor, or thermal activity is captured by a ground based/deployable thermal camera, radiometer or thermal sensor.
8. The method of any one of the preceding claims, wherein said computer-based system has access to weather and satellite/air thermal data.
9. The method of any one of the preceding claims, wherein the derivation of the refill point or target deficit allows an irrigation schedule for an operator linked to a networked computer system to be determined in real time.
10. The method of any one of the preceding claims, wherein said interaction of the results of said soil moisture deficit monitoring and said thermal activity monitoring additionally includes one or more of crop feedback, machine learning, artificial intelligence, system identification and the water applied through irrigation.
11. The method of any one of the preceding claims, wherein said step of monitoring soil moisture deficit includes input of measured rainfall.
12. The method of any one of the preceding claims, further including the steps of comparing said soil moisture deficit against said crop water stress index, if said crop water stress index is greater than a crop water stress threshold and said soil moisture deficit is greater than said refill point or target deficit, then a computer controlled irrigation of a calculated volume of water can occur.
13. The method of claim 12, wherein said computer controlled irrigation of said calculated volume of water is based on crop feedback and said refill point or target deficit.
14. The method of any one of the preceding claims, wherein the steps of monitoring soil moisture of said crop uses both direct and indirect field measurements to compute said soil moisture deficit.
15. The method of claim 14, wherein the derivation of said refill point or target deficit includes consideration of one or more of said field measurements, rainfall, crop factor (coefficient), soil type, historic yields, crop conditions and water applied.
16. The method of any one of the preceding claims, wherein said derivation of said refill point or target deficit also includes interaction with determination of soil infiltration.
17. The method of claim 16, when dependent on claim 9, wherein said determination of said soil infiltration interacts with the calculation of said irrigation schedule enabling a computer controlled irrigation of a calculated volume of water and depth to occur.
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