WO2017106962A1 - Système et procédé de gestion d'eau en temps réel - Google Patents

Système et procédé de gestion d'eau en temps réel Download PDF

Info

Publication number
WO2017106962A1
WO2017106962A1 PCT/CA2016/051334 CA2016051334W WO2017106962A1 WO 2017106962 A1 WO2017106962 A1 WO 2017106962A1 CA 2016051334 W CA2016051334 W CA 2016051334W WO 2017106962 A1 WO2017106962 A1 WO 2017106962A1
Authority
WO
WIPO (PCT)
Prior art keywords
irrigation
soil
crop
threshold
water
Prior art date
Application number
PCT/CA2016/051334
Other languages
English (en)
Inventor
Sylvio Jose GUMIERE
Jonathan LAFOND
Jean Caron
Yann PERIARD LARRIVEE
Original Assignee
Universite Laval
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Universite Laval filed Critical Universite Laval
Publication of WO2017106962A1 publication Critical patent/WO2017106962A1/fr

Links

Classifications

    • 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
    • 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
    • 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
    • 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
    • G06Q50/06—Energy or water supply

Definitions

  • the present invention relates to systems and methods for water management.
  • ETP evapotranspiration
  • a computer- implemented method for real-time water management comprises receiving in real-time input data related to a water status of a soil provided within a planting area, determining at least one critical irrigation threshold specific to a crop provided within the planting area, determining a forecasted evapotranspiration demand of the crop over a predetermined period, and providing a recommendation of irrigation amount for the planting area.
  • providing a recommendation of irrigation amount for the planting area comprises adjusting the at least one critical irrigation threshold to meet the forecasted evapotranspiration demand, growth stage, and soil characteristics, establishing a real-time irrigation sequence for the crop, the irrigation sequence indicative of an amount of irrigation water, a time, and a location for at least one irrigation event to be prescribed to the soil for maintaining the at least one adjusted irrigation threshold through irrigation, and causing the planting area to be irrigated in accordance with the at least one adjusted irrigation threshold.
  • causing the planting area to be irrigated in accordance with the at least one adjusted irrigation threshold comprises predicting a time to crop stress and an irrigation priority schedule, outputting the established irrigation sequence for causing the at least one irrigation event, and sending a control signal with instructions for causing an irrigation system to deliver the irrigation water to the crop in accordance with the irrigation sequence.
  • receiving the input data comprises receiving at least one crop characteristic, at least one soil characteristic, and at least one weather characteristic.
  • receiving the at least one crop characteristic comprises receiving at least one of a type, a seeding date, a harvest date, a growth stage, and the planting area for the crop.
  • receiving the input data comprises receiving the at least one soil characteristic indicative of a current soil water status and comprising at least one of a matric potential, a water content for the soil, and a hydraulic conductivity of the soil.
  • receiving the at least one weather characteristic comprises receiving a weather forecast for the predetermined time period.
  • determining the at least one critical irrigation threshold comprises deriving the at least one critical irrigation threshold on the basis of one of an algorithm, a numerical model, experimental evaluation, and professional evaluation.
  • determining the at least one critical irrigation threshold comprises determining one of a soil water potential threshold for irrigation and a soil moisture threshold for irrigation.
  • determining the soil water potential threshold comprises computing a matric potential threshold as: where thresholds is the matric potential threshold, MWD is a maximum water deficit for the crop, and ⁇ , ⁇ , and ⁇ are empirical parameters.
  • determining the at least one critical irrigation threshold comprises querying with the input data a memory having stored therein a plurality of crop types, a plurality of soil types, a plurality of growth stages, and a plurality of parameters selected from the group consisting of a soil water potential threshold and a soil moisture threshold, each combination of crop type, soil type, and growth stage having associated therewith a given one of the plurality of parameters, and determining on the basis of the query the given parameter relevant for the soil and crop.
  • adjusting the at least one critical irrigation threshold comprises one of increasing and decreasing the at least one critical irrigation threshold to obtain an updated threshold value meeting the forecasted evapotranspiration demand, growth stage, and soil characteristics, the updated threshold value derived on the basis of one of an algorithm, a numerical model, experimental evaluation, and professional evaluation.
  • the method further comprises generating a report indicative of a water use efficiency for the crop computed as a ratio of a marketable yield to an amount of water received by the crop, the amount of water including an amount of irrigation water prescribed to the soil and an amount of rainfall.
  • the method further comprises determining from the input data a soil water availability over the predetermined time period throughout the planting area, generating a real-time spatial representation of the forecasted soil water availability, and dynamically updating the generated spatial representation as new input data is received.
  • the method further comprises determining a spatial position of at least one acquisition device the input data is received from, the spatial representation dynamically updated in accordance with the spatial position, the spatial position one of directly obtained from the at least one acquisition device and determined on the basis of a digital elevation model.
  • a system for real-time water management comprises a memory, a processor, and at least one application stored in the memory and executable by the processor for receiving in real-time input data related to a water status of a soil provided within a planting area, determining at least one critical irrigation threshold specific to a crop provided within the planting area, determining a forecasted evapotranspi ration demand of the crop over a predetermined period, and providing a recommendation of irrigation amount for the planting area.
  • the at least one application is executable by the processor for providing a recommendation of irrigation amount for the planting area, comprising adjusting the at least one critical irrigation threshold to meet the forecasted evapotranspiration demand, growth stage, and soil characteristics, establishing a real-time irrigation sequence for the crop, the irrigation sequence indicative of an amount of irrigation water, a time, and a location for at least one irrigation event to be prescribed to the soil for maintaining the adjusted irrigation threshold through irrigation, and causing the planting area to be irrigated in accordance with the at least one adjusted irrigation threshold.
  • the system further comprises an irrigation system configured to deliver the irrigation water to the crop, the at least one application executable by the processor for causing the planting area to be irrigated in accordance with the at least one adjusted irrigation threshold comprising predicting a time to crop stress and an irrigation priority schedule, outputting the established irrigation sequence for causing the at least one irrigation event, and sending a control signal with instructions for causing the irrigation system to deliver the irrigation water to the crop in accordance with the established irrigation sequence.
  • the at least one application is executable by the processor for receiving the input data comprises receiving at least one crop characteristic, at least one soil characteristic, and at least one weather characteristic.
  • the at least one application is executable by the processor for determining the at least one critical irrigation threshold comprising deriving the at least one critical irrigation threshold on the basis of one of an algorithm, a numerical model, experimental evaluation, and professional evaluation.
  • the at least one application is executable by the processor for determining the at least one critical irrigation threshold comprising determining one of a soil water potential threshold for irrigation and a soil moisture threshold for irrigation.
  • the memory has stored therein a plurality of crop types, a plurality of soil types, a plurality of growth stages, and a plurality of parameters selected from the group consisting of a soil water potential threshold and a soil moisture threshold, each combination of crop type, soil type, and growth stage having associated therewith a given one of the plurality of parameters.
  • the at least one application is executable by the processor for determining the at least one critical irrigation threshold comprising querying the memory with the input data, and determining on the basis of the query the given parameter relevant for the soil and crop.
  • the at least one application is executable by the processor for adjusting the at least one critical irrigation threshold comprising one of increasing and decreasing the at least one critical irrigation threshold to obtain an updated threshold value meeting the forecasted evapotranspiration demand, growth stage, and soil characteristics, the updated threshold value derived on the basis of at least one of an algorithm, a numerical model, experimental evaluation, and professional evaluation.
  • the at least one application is executable by the processor for generating a report indicative of a water use efficiency for the crop computed as a ratio of a marketable yield to an amount of water received by the crop, the amount of water including an amount of irrigation water prescribed to the soil and an amount of rainfall.
  • the at least one application is executable by the processor for determining from the input data a soil water availability over the predetermined time period throughout the planting area, generating a real-time spatial representation of the forecasted soil water availability, and dynamically updating the generated spatial representation as new input data is received.
  • the system further comprises at least one acquisition device configured to acquire the input data, the at least one application executable by the processor for determining a spatial position of the at least one acquisition device and to dynamically update the spatial representation in accordance with the spatial position, the spatial position one of directly obtained from the at least one acquisition device and determined on the basis of a digital elevation model.
  • the system further comprises at least one acquisition device configured to acquire the input data, the at least one application executable by the processor for determining a spatial position of the at least one acquisition device and to dynamically update the spatial representation in accordance with the spatial position, the spatial position one of directly obtained from the at least one acquisition device and determined on the basis of a digital elevation model.
  • the program code is executable for receiving in real-time input data related to a water status of a soil provided within a planting area, determining at least one critical irrigation threshold specific to a crop provided within the planting area, determining a forecasted evapotranspiration demand of the crop over a predetermined period, and providing a recommendation of irrigation amount for the planting area.
  • Figure 1 is a flowchart of a method for real-time water management, in accordance with one embodiment
  • Figure 2 is a flowchart of the step of Figure 1 of obtaining input data
  • Figure 3 is a screen capture of a map of the soil water available capacity for a given planting area, in accordance with one embodiment
  • Figure 4 is a flowchart of the step of Figure 1 of computing specific threshold(s) for irrigation of a given crop and soil;
  • Figure 5 is a flowchart of the step of Figure 4 of determining critical irrigation threshold(s);
  • Figure 6 is a schematic diagram of the step of Figure 4 of adjusting the critical irrigation threshold(s) to meet the forecasted evapotranspiration demand;
  • Figure 7 is a flowchart of the step of Figure 1 of causing irrigation according the computed irrigation threshold(s);
  • Figure 8 is a flowchart of the step of Figure 7 of establishing a real-time irrigation sequence for the crop;
  • Figure 9 is a schematic diagram illustrating an exemplary system for realtime water management, in accordance with one embodiment
  • Figure 10 is a schematic diagram of an application running on the processor of Figure 9;
  • Figure 1 1 is a schematic diagram of the critical irrigation threshold computation module of Figure 10;
  • Figure 12 is a screen capture of a map of the current soil water availability for the planting area of Figure 3;
  • Figure 13 is a screen capture of a map of the end of day soil water availability for the planting area of Figure 3 and for the current day;
  • Figure 14 is a screen capture of a map of the end of day soil water availability for the planting area of Figure 3 and for the next day;
  • Figure 15 is a screen capture of a map of the end of day soil water availability for the planting area of Figure 3 and for two days after the current day.
  • FIG. 1 illustrates an exemplary method 100 for real-time water management in accordance with one embodiment.
  • the proposed water management method 100 dynamically combines different layers of information to determine in real-time a recommended irrigation (amount of irrigation water, time and location of the irrigation) to be prescribed to a given crop provided within a planting area of a soil.
  • a recommended irrigation amount of irrigation water, time and location of the irrigation
  • the soil hydric conditions, the soil water reserves, the evapotranspiration (ETP) demand, the crop schedule, and weather data relevant for the planting area are combined in real-time in order to provide a recommended irrigation that avoids hydric stress for the crop, according to the crop's growth stage, thereby improving crop production and quality.
  • ETP evapotranspiration
  • the soil may be any suitable type of soil including, but not limited to, a mineral soil, and a muck soil.
  • the crop is a vegetable crop (e.g., celery, spinach, lettuce). It should however be understood that other crop types may apply.
  • the method 100 may also be used to manage frost irrigation or drainage in real-time.
  • frost irrigation it may be necessary to irrigate crops and soils may then remain waterlogged for an extended period of time and it may be desirable to adjust drainage and supplemental irrigation accordingly. This can be achieved using the system and method described herein.
  • irrigation may be managed (e.g. the duration of irrigation adjusted) in real-time, as proposed herein, in order to correct any issue with drainage of the soil. Therefore, as used herein, the term "water management” should be understood to refer to management of irrigation, frost control irrigation, and/or drainage.
  • the method 100 comprises, at step 102, obtaining in real-time input data related to a given crop and soil (e.g. water status) in which the crop is planted.
  • the next step 104 is to compute specific threshold(s) for irrigation of the given crop and soil, the irrigation threshold(s) computed at step 104 such that their level optimizes water usage and maintains plant health.
  • a real-time spatial representation (or distribution) of the forecasted soil water available capacity may then generated at step 106.
  • the next step 108 is to cause irrigation of the given crop according to the irrigation threshold(s) computed at step 104.
  • the step 102 of obtaining real-time input data related to the crop and soil water status comprises receiving crop characteristic(s) (step 202), soil characteristic(s) (step 204), receiving one or more reference soil water available capacity maps (step 205), and weather characteristic(s) (step 206). It should be understood that other relevant data may be received at step 102.
  • the data received at step 102 is stored in memory (e.g. a database) for subsequent access.
  • the crop characteristics may be user-defined and dictate irrigation steps according to root penetrating depth and crop development.
  • Such crop characteristics may comprise relevant data about the crop, such as the crop type, variety, crop growing schedule (seeding and harvesting dates), growth stage, and data about an area of the soil in which the crop is planted (e.g. a delimitation and size of the planting area).
  • the soil characteristics may be indicative of current water status condition(s) of the soil, e.g. of the amount of water in the soil that is available to the crop, with such soil water status conditions varying spatially and depending on the soil's local heterogeneity, the crop type, and the crop's growth stage.
  • the soil characteristics may comprise data indicative of a matric potential, a water content, a salinity, a dielectric constant, and/or a hydraulic conductivity of the soil.
  • the soil characteristics are obtained in real-time using any suitable means, such as one or more sensor probes provided in the planting area.
  • the soil matric potential may be measured using one or more tensiometers (e.g. wireless electronic tensiometers) each inserted at a given depth (e.g. between 15 cm and 45 cm) in the planting area and configured to record matric potential readings at predetermined intervals (e.g. every fifteen (15) minutes).
  • the soil water content may be obtained from data received from suitable sensors (configured to measure soil moisture) provided throughout the planting area.
  • Time Domain Reflectometry (TDR) may be used to measure soil water content.
  • the soil's hydraulic conductivity may be determined using a vertical constant-head soil core method.
  • the soil and the air temperatures may also be measured (e.g. using one or more thermometers provided in the planting area) and received at step 204. It should be understood that other measurements may be acquired in real-time using any suitable device and received at step 204.
  • the acquisition device or instrument is spatially located (e.g. using the Global Positioning System (GPS) or any other suitable localization technique) and automatically transmits the data to be received at step 204.
  • GPS Global Positioning System
  • Step 102 may further comprise receiving at step 205 one or more reference soil water available capacity maps (see Figure 3), which provide a spatial representation of the range of available water that can be stored in the soil and made available to growing crops.
  • Such maps may be obtained from remote sensing information including, but not limited to, radar, multi-spectral imagery, soil hydrodynamic characterization by systematic sampling, pedotransfer functions, and other information from soil characteristics.
  • Figure 3 illustrates an exemplary reference soil water available capacity map, which may be obtained at step 205.
  • Figure 3 shows a screen capture 300 of a map of the soil water available capacity expressed in equivalent height for a given planting area 302 comprising a plurality of polygons as in 304.
  • the soil water available capacity may then be computed in real-time on the basis of the remote sensing information, by calculating the difference between the soil water content at field capacity and the water content at the permanent wilting point, adjusted for soil profile depth, crop roots penetrating depth and soil saturated hydraulic conductivity.
  • the weather characteristics received at step 206 may comprise a measurement of the real-time on-site air temperature at the crop level as well as meteorological data for actual conditions and forecasts obtained over a predetermined time period (e.g. over a three (3)-day period, i.e. the current day and the next two (2) days) for the geographical area encompassing the planting area.
  • the weather characteristic(s) may include, but are not limited to, precipitations, minimum and maximum temperatures, relative humidity, and wind speed for any given day of the predetermined time period.
  • the weather characteristic(s) may be received from a local weather station provided on the facility (e.g. farm) where the planting area is located or from a remote weather station that is in close proximity to the farm.
  • a weather station may be identified and related weather data obtained by accessing a suitable weather service, such as the Weather Underground® website, that provides real-time weather information over a network (e.g. the Internet).
  • a suitable weather service such as the Weather Underground® website
  • the planting area i.e. the within field area in which the crops are planted
  • the planting area is spatially delimited using the crop characteristics, soil characteristics, and soil water available capacity maps received in real-time at steps 202, 204, and 205.
  • this is achieved by automatically delineating (using any suitable input means, such as a user interface, mouse, keyboard, or the like, that may be operated by a user) planting sub-polygons on the soil water available capacity maps and accordingly creating vectorial information for each spatially delimited sub-polygons.
  • a given crop type, seed date, and harvest date corresponding to the crop to be grown in the delineated area of the field is then associated with each plantation polygon.
  • the crop characteristic(s), soil characteristic(s), soil water available capacity maps, and/or weather characteristic(s) received at steps 202, 204, 205, and 206 are further used to compute a soil water balance and forecasted ETP demand for the crop.
  • the water balance computation may indeed combine information from soil measurements (e.g. runoff) and meteorological data (e.g. precipitation) in order to determine the storage of water for the given soil, and the duration of the storage in response to the forecasted evapotranspi ration crop. Any suitable computation technique may be used to determine the water balance. For example, the general water balance equation may apply.
  • the forecasted duration of the storage of water in the soil may then be computed at step 210 based on the soil water status conditions and the forecasted crop demand received at step 204.
  • the soil expected water amount to be used by the crop throughout the planting area and over a predetermined time period may be determined using any suitable technique.
  • the Food and Agriculture Organization (FAO) Penman-Monteith equation may be used, in which a reference evapotranspiration is multiplied by predetermined crop coefficients.
  • the reference evapotranspiration may be computed from weather data collected and forecasted within a given geographical area encompassing the planting area and received at step 206.
  • the predetermined crop coefficients may be received at step 202 and may vary by plant species, leaf area characteristics, and density of the crop canopy. It should be understood that techniques other than the Penman-Monteith method might be used to compute the forecasted evapotranspiration demand. For example, the Thornthwaite (1948) method, the Shuttleworth-Wallace (1985) method or the Priestley-Taylor (1972) method may apply.
  • the data received at step 102 is used to produce irrigation recommendations considering the soil-plant water processes defined by expert knowledge, experiments, analytical solutions, and/or numerical solutions.
  • irrigation threshold(s) for irrigation of the given crop and soil are first computed at step 104.
  • the information computed at step 104 is organized in a multidimensional matrix in which a decision is taken according to the value of the threshold(s) computed at step 104.
  • step 104 comprises determining at step 402 one or more critical irrigation thresholds specific to the given crop and soil.
  • this threshold is adjusted at step 404 to meet the forecasted evapotranspiration demand, growth stages and soil characteristics computed at step 210 of Figure 2. Irrigation of the given crop is then caused (at step 108 of Figure 1 ) according to the adjusted irrigation threshold.
  • the step 402 of determining one or more critical irrigation thresholds comprises determining a soil water potential threshold for irrigation at step 502 or determining a soil moisture threshold for irrigation at step 504.
  • Soil water potential (expressed in length units (L)) refers to an energy state characterizing the effect of the force exerted on soil water by the soil.
  • Soil moisture refers to the volumetric water content in the soil, which is a volume of liquid water per volume of soil (L 3 L ⁇ 3 ).
  • Data for a plurality of locations, crop growth stages and soil types may be accessed and queried to compute the critical irrigation threshold(s) of a crop. As discussed above, the data may have been previously received (e.g.
  • step 402 then comprises computing the crop-specific critical irrigation threshold(s) in real time on the basis of data retrieved from the database. It should however be understood that, in other embodiments, the critical irrigation threshold(s) may have been previously received (or previously computed) and may already be stored in memory. In this case, step 402 would comprise querying the database to retrieve the critical irrigation threshold(s) for the parameters relevant to the case at hand, i.e. relevant for the soil and crop in question.
  • the soil water potential threshold may comprise a matric potential threshold computed using any suitable method including, but not limited to, using algorithms, a numerical model, or experimental or professional evaluation.
  • computing the soil water potential threshold may comprise computing a matric potential threshold as follows:
  • ht h res h oids is the matric potential threshold
  • MWD is a maximum water deficit for the crop
  • ⁇ , ⁇ , and ⁇ are empirical parameters.
  • the soil moisture threshold for irrigation may be computed using any suitable method including, but not limited to, using algorithms, a numerical model, or experimental or professional evaluation.
  • the soil moisture threshold may be computed as follows from the relation between soil water content and soil matric potential:
  • n, and m are empirical parameters.
  • the soil water potential threshold and the soil moisture threshold can be computed on the basis of data obtained from professional evaluation and expert knowledge (e.g. a review of relevant literature and stored in memory).
  • the soil water potential threshold and the soil moisture threshold can also be computed on the basis of experiments performed in the field. In this case, various thresholds may be compared on site to determine the optimal irrigation threshold for the crop and soil.
  • the soil water potential threshold or the soil moisture threshold can be computed on the basis of an analytical solution to the transfer equation (Rekika et al., 2014) or a numerical model, such as HYDRUS (PC-Progress, 2006-2014). It should be understood that the choice of the technique used to compute the soil water potential threshold depends on the application and on the data available for the case at hand.
  • a crop-specific critical threshold may be adjusted at step 404 (i.e. either increased or decreased) to meet the forecasted evapotranspiration demand. For example, it may be determined from the forecasted evapotranspiration demand that the crop's water needs will increase over the next three (3) days. A more severe irrigation threshold (i.e. as for crop establishment irrigation threshold) will therefore be used in real-time in order to anticipate the upcoming water needs of the crop. The prescribed irrigation for the crop will also be determined according to the adjusted irrigation threshold, thereby allowing for a more accurate and prioritized response to the crop's changing needs.
  • the critical threshold(s) may be adjusted at step 404 using any suitable method including, but not limited to, algorithms, numerical models, or experimental or professional evaluation. Adjusting the irrigation thresholds implies to consider one or all, but not limited to, of the following properties: the evapotranspirative demand, the growth stage (i.e. the depth of the root zone), and soil hydraulic characteristics as unsaturated hydraulic conductivity in function of soil matric potential.
  • the spatial representation i.e. a map of the real-time soil water availability can be generated at step 106.
  • This may comprise dynamically updating the soil water available capacity maps (see Figure 3) previously received (e.g. at step 205 of Figure 2).
  • each pixel of the original map is actualized in real-time, according to the updated threshold(s).
  • Any suitable technique including, but not limited to local estimation and spatial interpolation, may be used to actualize the maps.
  • the map generated at step 106 is a local-scale map, which provides the spatial representation in real-time at the field or plot level.
  • a spatial position of the acquisition device providing the input data received at step 102 of Figure 1 may be determined. This may be achieved by determining (or directly obtaining from the acquisition device itself) a spatial location (e.g. a GPS position including a latitude, longitude, and altitude) of the device.
  • a spatial interpolation within a digital elevation model (DEM) may also be used and the DEM crossed with the GPS position to reach centimeter-scale precision of the device's position.
  • DEM digital elevation model
  • step 108 of causing irrigation of the crop according to the computed irrigation thresholds allows maintaining the soil at an optimal humidity level.
  • step 108 illustratively comprises establishing at step 702 a real-time irrigation sequence for the crop by predicting an amount of irrigation water to be prescribed to the soil in order to remain within the computed critical irrigation thresholds and avoid hydric stress for the crop.
  • An indication of the number of days before hydric stress is generated can therefore be obtained at step 702.
  • the time and location of the prescribed irrigation event(s) may also be determined at step 702.
  • the established irrigation sequence is then output at step 704 to cause prescribed irrigation event(s) to occur.
  • step 704 may comprise outputting one or more control signals comprising instructions for causing the irrigation system to deliver irrigation water to the crop in accordance with the established irrigation sequence.
  • establishing the irrigation sequence at step 702 comprises generating at step 802 a local recommendation for irrigation of the crop at the current time (e.g. for the current day), generating at step 804 a local recommendation for irrigation of the crop at the end of the current day, and generating at step 806 a local recommendation for irrigation of the crop over a predetermined period of time, e.g. the two or more upcoming days.
  • a report may also be generated at step 1 10 to provide an indication of the usage of the water resources.
  • the report can be used to improve water management at the local (e.g. farm) scale.
  • the water usage efficiency for the crop may be computed as a ratio of the crop's marketable yield to an amount of water received for the crop, the amount of water including an amount of irrigation water prescribed to the soil (e.g. as part of the irrigation sequence established at step 108) and an amount of rainfall.
  • the report may be generated at step 1 10 in any suitable format, such as tabular, graphical, or the like.
  • the system 900 may be used to implement the steps of method 100 of Figure 1 , thereby allowing for dynamic and real-time water management based on soil water storage, soil properties, hydric status, and optimal irrigation thresholds.
  • the system 900 illustratively comprises a plurality of devices as in 902 adapted to communicate over a network 904 with one or more server(s) accessible via the network 904.
  • server(s) accessible via the network 904.
  • server 906 may comprise, amongst other things, a plurality of applications 908a ... 908n running on a processor 910 coupled to a memory 912. It should be understood that while the applications 908a ... 908n presented herein are illustrated and described as separate entities, they might be combined or separated in a variety of ways.
  • the devices 902 may comprise any device, such as a personal computer, a tablet computer, a personal digital assistant, a smart phone, or the like, which is configured to communicate over the network 804, such as the Internet, the Public Switch Telephone Network (PSTN), a cellular network, or others known to those skilled in the art.
  • PSTN Public Switch Telephone Network
  • the water management system might be separate and remote from the devices 902 or integrated with the devices 902, either as a downloaded software application, a firmware application, or a combination thereof. It should also be understood that several devices as in 902 might access the system at once.
  • One or more databases 914 may be integrated directly into the memory 912 or may be provided separately therefrom and remotely from the server 906 (as illustrated). In the case of a remote access to the databases 914, access may occur via any type of network 904, as indicated above.
  • the various databases 914 described herein may be provided as collections of data or information organized for rapid search and retrieval by a computer.
  • the databases 914 may be structured to facilitate storage, retrieval, modification, and deletion of data in conjunction with various data-processing operations.
  • the databases 914 may consist of a file or sets of files that can be broken down into records, each of which consists of one or more fields. Database information may be retrieved through queries using keywords and sorting commands, in order to rapidly search, rearrange, group, and select the field.
  • the databases 914 may be any organization of data on a data storage medium, such as one or more servers.
  • the databases 914 are secure web servers and Hypertext Transport Protocol Secure (HTTPS) capable of supporting Transport Layer Security (TLS), which is a protocol used for access to the data.
  • HTTPS Hypertext Transport Protocol Secure
  • TLS Transport Layer Security
  • Communications to and from the secure web servers may be secured using Secure Sockets Layer (SSL).
  • SSL Secure Sockets Layer
  • Identity verification of a user may be performed using usernames and passwords for all users.
  • Various levels of access rights may be provided to multiple levels of users.
  • any known communication protocols that enable devices within a computer network to exchange information may be used. Examples of protocols are as follows: IP (Internet Protocol), UDP (User Datagram Protocol), TCP (Transmission Control Protocol), DHCP (Dynamic Host Configuration Protocol), HTTP (Hypertext Transfer Protocol), FTP (File Transfer Protocol), Telnet (Telnet Remote Protocol), SSH (Secure Shell Remote Protocol)
  • IP Internet Protocol
  • UDP User Datagram Protocol
  • TCP Transmission Control Protocol
  • DHCP Dynamic Host Configuration Protocol
  • HTTP Hypertext Transfer Protocol
  • FTP File Transfer Protocol
  • Telnet Telnet Remote Protocol
  • SSH Secure Shell Remote Protocol
  • the memory 912 accessible by the processor 910 may receive and store data.
  • the memory 912 may be a main memory, such as a high speed Random Access Memory (RAM), or an auxiliary storage unit, such as a hard disk, flash memory, or a magnetic tape drive.
  • the memory 912 may be any other type of memory, such as a Read-Only Memory (ROM), Erasable Programmable Readonly Memory (EPROM), electrically-erasable programmable read-only memory (EEPROM), Ferroelectric RAM (FRAM), or optical storage media such as a videodisc and a compact disc.
  • ROM Read-Only Memory
  • EPROM Erasable Programmable Readonly Memory
  • EEPROM electrically-erasable programmable read-only memory
  • FRAM Ferroelectric RAM
  • optical storage media such as a videodisc and a compact disc.
  • the processor 910 may access the memory 912 to retrieve data.
  • the processor 910 may be any device that can perform operations on data. Examples are a central processing unit (CPU), a front-end processor, a microprocessor, and a network processor.
  • the applications 908a ... 908n are coupled to the processor 910 and configured to perform various tasks as explained below in more detail. An output may be transmitted to the devices 902.
  • real-time input data e.g. crop characteristic(s), soil characteristic(s), and weather characteristic(s)
  • the processor 910 may communicate with the sensing device(s) 916 over the network 904.
  • the received input data may then be stored in the memory 912 and/or databases 914 for subsequent access.
  • the processor 910 may also communicate over the network 904 with an irrigation system 918 comprising any suitable irrigation means (e.g. an automated overhead sprinkler system) that may be fixed and automated and configured to irrigate the crop.
  • the processor 910 may output one or more control signals to the irrigation system 918 to cause the irrigation system 918 to irrigate the crop according to a recommended irrigation sequence determined on the basis of one or more irrigation thresholds computed in real-time, as discussed above.
  • Figure 10 is an exemplary embodiment of an application 908a running on the processor 910 of Figure 9.
  • the application 908a may comprise an input module 1002, a soil water balance and ETP demand computation module 1004, a critical irrigation threshold computation module 1006, an irrigation sequence- establishing module 1008, a polygon delineation module 1010, a spatial representation generating module 1012, a report-generating module 1014, and an output module 1016.
  • the input module 1002 illustratively receives real-time input data comprising crop characteristic(s), soil characteristic(s), and weather characteristic(s), as discussed above with reference to Figure 2.
  • the received data may then be stored in the memory 912 and/or databases 914 for subsequent access.
  • the soil water balance and ETP demand computation module 1004 may then use the received information to compute the soil water balance and evapotranspiration for the specific soil and crop using any suitable computation technique, as discussed above with reference to step 210 of Figure 2.
  • the polygon delineation module 1010 may further be used to automatically delineate polygons that spatially delimit the planting area where the crop is grown.
  • the polygon delineation module 1010 may use user input data received upon a user interacting with a suitable input device (e.g. a touchscreen, mouse, keypad, user interface, or the like) provided with their device 902 to delimit the planting area.
  • a suitable input device e.g. a touchscreen, mouse, keypad, user interface, or the like
  • the user may draw the polygon on a map of the planting area presented on their device's screen.
  • Other embodiments may apply.
  • the critical irrigation threshold computation module 1006 may then use data obtained from the input module 1002 to compute, using an irrigation threshold computation module 1 102, one or more critical irrigation thresholds for the crop, as discussed above with reference to Figure 4 and Figure 5.
  • the irrigation threshold(s) may comprise a soil water potential threshold or a soil moisture threshold determined using any suitable method including, but not limited to, using algorithms, numerical models, experimental evaluation, and professional evaluation.
  • an irrigation threshold adjusting module 1 104 provided at the critical irrigation threshold computation module 1006 in order to meet the forecasted ETP demand computed by the water balance and ETP demand computation module 1004. This is achieved by increasing or decreasing the critical irrigation threshold in order to anticipate the upcoming water needs of the crop.
  • the spatial representation generating module 1012 can be used to generate a real-time visual representation (e.g. a map) of the forecasted soil water availability for the planting area delineated using the polygon delineation module 1010.
  • the spatial representation generating module 1012 may update previously-received maps (e.g. retrieved from the memory 912 and/or databases 914) in real-time.
  • the map generated by the spatial representation generating module 1012 may then be sent to the output module 1016 for rendering on an output device (e.g. a screen) provided with the user's device (reference 902 in Figure 9).
  • Figures 12, 13, 14, and 15 illustrate examples of maps that can be generated for rendering to a user.
  • Figure 12 shows a screen capture 1200 of a map of the current soil water availability for the planting area 302 of Figure 3. It can be seen that different polygons 304 have different soil water availability.
  • Figure 13 shows a screen capture 1300 of a map of the end of day soil water availability for the planting area 302 and for the current day.
  • Figure 14 shows a screen capture 1400 of a map of the end of day soil water availability for the planting area 302 and for the next day.
  • Figure 15 shows a screen capture 1500 of a map of the end of day soil water availability for the planting area 302 and for two days after the current day.
  • the irrigation sequence-establishing module 1008 is used to establish a real-time irrigation sequence for the crop. As discussed above, the established irrigation sequence predicts an amount of irrigation water to be prescribed to the soil in order to remain within the computed critical irrigation thresholds and avoid hydric stress for the crop. The established irrigation sequence also provides an indication of a time and location for the prescribed irrigation water. The irrigation sequence-establishing module 1008 may then send the established irrigation sequence to the output module 1016, which may in turn generate one or more control signals comprising instructions to cause the irrigation system (reference 918 in Figure 9) to irrigate the crop according to the prescribed irrigation.
  • the report-generating module 1014 may be used to generate a report indicative of water use efficiency for the crop.
  • the water use efficiency may be computed as a ratio of a marketable yield to an amount of water received by the crop, the amount of water including an amount of irrigation water prescribed to the soil and an amount of rainfall.
  • the report may then be sent to the output module 1016 for transmission to the user devices (reference 918 in Figure 9) and rendering thereon using any suitable means.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Water Supply & Treatment (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Environmental Sciences (AREA)
  • Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Strategic Management (AREA)
  • Primary Health Care (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Agronomy & Crop Science (AREA)
  • Mining & Mineral Resources (AREA)
  • Animal Husbandry (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Public Health (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

L'invention concerne un système et un procédé de gestion d'eau en temps réel. Des données d'entrée associées à un état d'eau d'un sol situé à l'intérieur d'une zone de plantation sont reçues en temps réel. Au moins un seuil critique d'irrigation spécifique à une culture située dans la zone de plantation est déterminé. Une demande d'évapotranspiration prévue de la culture sur une période prédéfinie est également déterminée. Une recommandation de quantité d'irrigation pour la zone de plantation est ensuite fournie.
PCT/CA2016/051334 2015-11-17 2016-11-16 Système et procédé de gestion d'eau en temps réel WO2017106962A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US201562256432P 2015-11-17 2015-11-17
US62/256,432 2015-11-17

Publications (1)

Publication Number Publication Date
WO2017106962A1 true WO2017106962A1 (fr) 2017-06-29

Family

ID=59088752

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CA2016/051334 WO2017106962A1 (fr) 2015-11-17 2016-11-16 Système et procédé de gestion d'eau en temps réel

Country Status (1)

Country Link
WO (1) WO2017106962A1 (fr)

Cited By (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019033158A1 (fr) 2017-08-14 2019-02-21 Rubicon Research Pty Ltd Procédé et système de distribution d'eau et de détermination d'humidité d'un sol
CN109934400A (zh) * 2019-03-08 2019-06-25 河北工程大学 基于改进神经网络的集雨调亏作物需水量预测方法
CN110781591A (zh) * 2019-10-23 2020-02-11 软通动力信息技术有限公司 城市排水防涝仿真模拟系统、方法、设备和存储介质
CN111563661A (zh) * 2020-04-14 2020-08-21 水利部交通运输部国家能源局南京水利科学研究院 一种节水减排量的确定方法、装置及设备
CN112487005A (zh) * 2020-11-30 2021-03-12 广西慧云信息技术有限公司 一种茎类作物生长实时数据管理方法
KR20210045253A (ko) * 2019-10-16 2021-04-26 대한민국(농촌진흥청장) 작물의 수분스트레스 진단을 이용한 관개 시스템 및 방법
CN113780858A (zh) * 2021-09-17 2021-12-10 兰州大学 一种考虑雨水资源承载力的植被恢复规划方法
CN113780897A (zh) * 2021-10-26 2021-12-10 广州极飞科技股份有限公司 一种给排水管理方法及系统
CN113994868A (zh) * 2021-09-27 2022-02-01 上海易航海芯农业科技有限公司 一种基于植物生长周期的自动灌溉方法及系统
CN114355481A (zh) * 2021-12-16 2022-04-15 西安酷美易天智能科技有限公司 一种基于短临降水预测的城市内涝预警方法及系统
CN114493011A (zh) * 2022-01-27 2022-05-13 武汉大学 考虑膜下滴灌与暗管排盐协同调控运行模型的构建方法
EP3846611A4 (fr) * 2018-09-05 2022-05-25 Rubicon Research Pty Ltd Procédé et système de détermination de stress de plante et irrigation basée sur ce dernier
CN114544874A (zh) * 2022-02-21 2022-05-27 北京京东尚科信息技术有限公司 一种农作物水份检测方法和装置
CN115655385A (zh) * 2022-12-27 2023-01-31 武汉旭思科技有限公司 一种基于灌区的信息化智能监测系统及其方法
CN115762649A (zh) * 2022-10-19 2023-03-07 北京爱科农科技有限公司 一种基于土壤物理、化学性质的肥料淋失量计算方法
CN116368984A (zh) * 2023-04-10 2023-07-04 中国水利水电科学研究院 协同解决干旱区灌溉绿洲缺水与盐渍化的方法
CN116369175A (zh) * 2023-04-10 2023-07-04 宁夏大学 一种提升番茄风味品质的灌溉决策方法及装置
CN116686689A (zh) * 2023-08-01 2023-09-05 中山大学 考虑土壤和大气双重胁迫作用的灌溉控制方法、系统及介质
CN118160616A (zh) * 2024-05-16 2024-06-11 潍坊市农业科学院(山东省农业科学院潍坊市分院) 一种用于农业生产的智能化浇水控制方法及系统
CN118355824A (zh) * 2024-06-20 2024-07-19 贵州省林业科学研究院 基于区域网格分析的山桐子土壤灌根方法
CN118469093A (zh) * 2024-07-10 2024-08-09 北京市农林科学院智能装备技术研究中心 灌区作物需水量确定方法和装置

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1995022799A1 (fr) * 1994-02-17 1995-08-24 Waterlink Systems, Inc. Systeme de gestion de l'irrigation base sur les previsions d'evapotranspiration
WO2001050212A1 (fr) * 2000-01-04 2001-07-12 Aqua Conservation Systems, Inc. Controleur d'irrigation a accumulation
WO2005032240A1 (fr) * 2003-10-03 2005-04-14 James Dunstone Townsend Technique de commande d'evapotranspiration pour certains types de systemes de programmation d'irrigation
US20120290140A1 (en) * 2011-05-13 2012-11-15 Groeneveld David P Method and System to Control Irrigation Across Large Geographic Areas Using Remote Sensing, Weather and Field Level Data
US20150005963A1 (en) * 2013-07-01 2015-01-01 Skydrop, Llc Backup watering instructions and irrigation protocols when connection to a network is lost

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO1995022799A1 (fr) * 1994-02-17 1995-08-24 Waterlink Systems, Inc. Systeme de gestion de l'irrigation base sur les previsions d'evapotranspiration
WO2001050212A1 (fr) * 2000-01-04 2001-07-12 Aqua Conservation Systems, Inc. Controleur d'irrigation a accumulation
WO2005032240A1 (fr) * 2003-10-03 2005-04-14 James Dunstone Townsend Technique de commande d'evapotranspiration pour certains types de systemes de programmation d'irrigation
US20120290140A1 (en) * 2011-05-13 2012-11-15 Groeneveld David P Method and System to Control Irrigation Across Large Geographic Areas Using Remote Sensing, Weather and Field Level Data
US20150005963A1 (en) * 2013-07-01 2015-01-01 Skydrop, Llc Backup watering instructions and irrigation protocols when connection to a network is lost

Cited By (36)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2019033158A1 (fr) 2017-08-14 2019-02-21 Rubicon Research Pty Ltd Procédé et système de distribution d'eau et de détermination d'humidité d'un sol
US11297785B2 (en) 2017-08-14 2022-04-12 Rubicon Research Pty Ltd Method and system for water distribution and soil moisture determination
CN111163629B (zh) * 2017-08-14 2022-08-19 鲁比康研究有限公司 用于水分配和土壤水分确定的方法和系统
EP3668305A4 (fr) * 2017-08-14 2021-04-28 Rubicon Research Pty Ltd Procédé et système de distribution d'eau et de détermination d'humidité d'un sol
CN111163629A (zh) * 2017-08-14 2020-05-15 鲁比康研究有限公司 用于水分配和土壤水分确定的方法和系统
EP3846611A4 (fr) * 2018-09-05 2022-05-25 Rubicon Research Pty Ltd Procédé et système de détermination de stress de plante et irrigation basée sur ce dernier
CN109934400B (zh) * 2019-03-08 2023-05-16 河北工程大学 基于改进神经网络的集雨调亏作物需水量预测方法
CN109934400A (zh) * 2019-03-08 2019-06-25 河北工程大学 基于改进神经网络的集雨调亏作物需水量预测方法
KR102342392B1 (ko) * 2019-10-16 2021-12-23 대한민국 작물의 수분스트레스 진단을 이용한 관개 시스템 및 방법
KR20210045253A (ko) * 2019-10-16 2021-04-26 대한민국(농촌진흥청장) 작물의 수분스트레스 진단을 이용한 관개 시스템 및 방법
CN110781591A (zh) * 2019-10-23 2020-02-11 软通动力信息技术有限公司 城市排水防涝仿真模拟系统、方法、设备和存储介质
CN111563661B (zh) * 2020-04-14 2023-06-16 水利部交通运输部国家能源局南京水利科学研究院 一种节水减排量的确定方法、装置及设备
CN111563661A (zh) * 2020-04-14 2020-08-21 水利部交通运输部国家能源局南京水利科学研究院 一种节水减排量的确定方法、装置及设备
CN112487005A (zh) * 2020-11-30 2021-03-12 广西慧云信息技术有限公司 一种茎类作物生长实时数据管理方法
CN113780858A (zh) * 2021-09-17 2021-12-10 兰州大学 一种考虑雨水资源承载力的植被恢复规划方法
CN113780858B (zh) * 2021-09-17 2023-09-26 兰州大学 一种考虑雨水资源承载力的植被恢复规划方法
CN113994868A (zh) * 2021-09-27 2022-02-01 上海易航海芯农业科技有限公司 一种基于植物生长周期的自动灌溉方法及系统
CN113780897A (zh) * 2021-10-26 2021-12-10 广州极飞科技股份有限公司 一种给排水管理方法及系统
CN114355481B (zh) * 2021-12-16 2024-05-14 西安酷美易天智能科技有限公司 一种基于短临降水预测的城市内涝预警方法及系统
CN114355481A (zh) * 2021-12-16 2022-04-15 西安酷美易天智能科技有限公司 一种基于短临降水预测的城市内涝预警方法及系统
CN114493011A (zh) * 2022-01-27 2022-05-13 武汉大学 考虑膜下滴灌与暗管排盐协同调控运行模型的构建方法
CN114493011B (zh) * 2022-01-27 2024-05-24 武汉大学 考虑膜下滴灌与暗管排盐协同调控运行模型的构建方法
CN114544874A (zh) * 2022-02-21 2022-05-27 北京京东尚科信息技术有限公司 一种农作物水份检测方法和装置
CN114544874B (zh) * 2022-02-21 2024-04-05 北京京东尚科信息技术有限公司 一种农作物水份检测方法和装置
CN115762649A (zh) * 2022-10-19 2023-03-07 北京爱科农科技有限公司 一种基于土壤物理、化学性质的肥料淋失量计算方法
CN115762649B (zh) * 2022-10-19 2023-07-11 北京爱科农科技有限公司 一种基于土壤物理、化学性质的肥料淋失量计算方法
CN115655385A (zh) * 2022-12-27 2023-01-31 武汉旭思科技有限公司 一种基于灌区的信息化智能监测系统及其方法
CN116368984B (zh) * 2023-04-10 2023-11-10 中国水利水电科学研究院 协同解决干旱区灌溉绿洲缺水与盐渍化的方法
CN116369175B (zh) * 2023-04-10 2024-05-07 宁夏大学 一种提升番茄风味品质的灌溉决策方法及装置
CN116369175A (zh) * 2023-04-10 2023-07-04 宁夏大学 一种提升番茄风味品质的灌溉决策方法及装置
CN116368984A (zh) * 2023-04-10 2023-07-04 中国水利水电科学研究院 协同解决干旱区灌溉绿洲缺水与盐渍化的方法
CN116686689B (zh) * 2023-08-01 2024-03-19 中山大学 考虑土壤和大气双重胁迫作用的灌溉控制方法、系统及介质
CN116686689A (zh) * 2023-08-01 2023-09-05 中山大学 考虑土壤和大气双重胁迫作用的灌溉控制方法、系统及介质
CN118160616A (zh) * 2024-05-16 2024-06-11 潍坊市农业科学院(山东省农业科学院潍坊市分院) 一种用于农业生产的智能化浇水控制方法及系统
CN118355824A (zh) * 2024-06-20 2024-07-19 贵州省林业科学研究院 基于区域网格分析的山桐子土壤灌根方法
CN118469093A (zh) * 2024-07-10 2024-08-09 北京市农林科学院智能装备技术研究中心 灌区作物需水量确定方法和装置

Similar Documents

Publication Publication Date Title
WO2017106962A1 (fr) Système et procédé de gestion d'eau en temps réel
US11617313B2 (en) Controlling agricultural production areas
US10477756B1 (en) Correcting agronomic data from multiple passes through a farmable region
EP3179319B1 (fr) Procédé de planification d'irrigation et système pour sa mise en oeuvre
US20150370935A1 (en) Agronomic systems, methods and apparatuses
US20180014452A1 (en) Agronomic systems, methods and apparatuses
Srivastava et al. Effect of sowing date distributions on simulation of maize yields at regional scale–A case study in Central Ghana, West Africa
CN108106676B (zh) 一种基于遥感数据的农作物晚霜冻害的监测方法和装置
CA2987761A1 (fr) Prevision de potentiel de rendement de recolte fondee sur l'eau
CN114331753A (zh) 农事智能化的方法、装置及控制设备
AU2023203307A1 (en) Controlling Agricultural Production Areas
Verdugo-Vásquez et al. Towards an empirical model to estimate the spatial variability of grapevine phenology at the within field scale
US11805740B2 (en) Systems and methods for monitoring and controlling crop irrigation schedules
CN116415704A (zh) 一种基于多数据融合与同化的区域精量灌溉方法及系统
US20230068574A1 (en) Advanced Systems Providing Irrigation Optimization Using Sensor Networks and Soil Moisture Modeling
CN110516943B (zh) 一种基于地表温度的春灌期灌溉面积动态监测遥感方法
US20220132724A1 (en) Advanced crop manager for crops stress mitigation
Pelta et al. Forecasting seasonal plot-specific crop coefficient (Kc) protocol for processing tomato using remote sensing, meteorology, and artificial intelligence
US20230349880A1 (en) Method and System for Characterising the Soil Moisture Characteristics of Management Zones in Both Time and Space, Specific to Zone-Based Management for Variable Rate Crop Inputs
Janssens et al. Numerical calculation of soil water potential in an irrigated ‘conference’pear orchard
CN112949182B (zh) 耦合地表土壤含水量的区域尺度最大气孔导度的计算方法
Todorovic et al. HYDRO-TECH: An integrated decision support system for sustainable irrigation management (i): Main algorithms and field testing
JP2021124343A (ja) 演算方法、演算装置、および演算プログラム
Kallestad et al. Development of a simple irrigation scheduling calendar for Mesilla Valley pecan growers
Vargas‐Castro et al. Using a crop water stress index to determine water use efficiency in a raspberry crop in the Mediterranean Central Chile

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 16877040

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 16877040

Country of ref document: EP

Kind code of ref document: A1