WO2018107245A1 - Détection de conditions environnementales - Google Patents

Détection de conditions environnementales Download PDF

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Publication number
WO2018107245A1
WO2018107245A1 PCT/AU2017/051403 AU2017051403W WO2018107245A1 WO 2018107245 A1 WO2018107245 A1 WO 2018107245A1 AU 2017051403 W AU2017051403 W AU 2017051403W WO 2018107245 A1 WO2018107245 A1 WO 2018107245A1
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WIPO (PCT)
Prior art keywords
rainfall
plant growth
indicator
soil water
water content
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PCT/AU2017/051403
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English (en)
Inventor
Anthony Clark
Ian Mcgowen
Jason CREAN
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The Crown In The Right Of The State Of New South Wales Acting Through The Department Of Primary Industries, An Office Of The Nsw Department Of Industry, Skills And Regional Development
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Application filed by The Crown In The Right Of The State Of New South Wales Acting Through The Department Of Primary Industries, An Office Of The Nsw Department Of Industry, Skills And Regional Development filed Critical The Crown In The Right Of The State Of New South Wales Acting Through The Department Of Primary Industries, An Office Of The Nsw Department Of Industry, Skills And Regional Development
Priority to AU2017376837A priority Critical patent/AU2017376837A1/en
Publication of WO2018107245A1 publication Critical patent/WO2018107245A1/fr

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Classifications

    • 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
    • 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
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/10—Devices for predicting weather conditions
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01W—METEOROLOGY
    • G01W1/00—Meteorology
    • G01W1/14—Rainfall or precipitation gauges

Definitions

  • the present disclosure relates to an environmental monitoring system and method of detecting environmental conditions of a specified area.
  • Monitoring environmental conditions such as drought can assist stakeholders such as farmers and governments prepare for, and manage resources for these conditions.
  • Known monitoring systems and methods may provide general environmental conditions on a larger regional scale, but lack the granularity to provide stakeholders at their respective areas information that is accurate to their local area of interest.
  • a region may be in severe drought, but the impact may be lower along the banks of a river passing through that region.
  • An environmental monitoring system for detecting an environmental condition of a specified area, the system comprising: one or more remote sensors and/or a climate data source to provide sensor data indicative of soil water content, plant growth and rainfall associated with the specified area; a database to store historical data based on historical soil water data, historical plant growth data, and historical rainfall data; and a processing device.
  • the processing device is configured to: determine soil water content, plant growth and rainfall based on sensor data from the one or more remote sensors and/or climate data source and historical data; determine a drought direction associated with the specified area that indicates a trend in precipitation based on the determined rainfall and historical rainfall data; determine respective indicators for soil water content, plant growth and rainfall during drought conditions and normal conditions associated with the specified area that are based on historical data; and determine a warning environmental condition associated with the specified area. Determining a warning environmental condition is based on determination of:
  • - determined rainfall is greater than the indicator for rainfall during drought conditions but less than the indicator for rainfall during normal conditions.
  • the processing device is further configured to send, over a communications network, a notification to a communication device indicating a warning environmental condition associated with the specified area.
  • the processing device may be further configured to: determine the indicators for soil water content, plant growth and rainfall during early recovery conditions associated with the specified area based on historical data; and determine an early recovery environmental condition associated with the specified area. Determining an early recovery environmental condition may be based on determination of:
  • the processing device may be further configured to send, over a communications network, a notification indicating an early recovery environmental condition associated with the specified area.
  • the processing device may be further configured to: determine the indicators for soil water content, plant growth and rainfall during strong recovery conditions associated with the specified area based on historical data; and determine a strong recovery environmental condition associated with the specified area. Determining a strong recover environmental condition may be based on determination of:
  • - determined plant growth is within the indicator for plant growth strong recovery conditions; and - determined rainfall is within the indicator for rainfall during strong recovery conditions.
  • the processing device may be further configured to send, over a communications network, a notification indicating a strong recovery environmental condition associated with the specified area.
  • the processing device may be further configured to: determine a drought environmental condition associated with the specified area based on determination of at least one of:
  • the processing device may be further configured to send, over a communications network, a notification indicating a drought environmental condition associated with the specified area.
  • the processing device may be further configured to: determine a non drought environmental condition associated with the specified area based on determination of:
  • the processing device may be further configured to send, over a communications network, a notification indicating a non drought environmental condition associated with the specified area.
  • the system may provide notifications to stakeholders (such as farmers) more accurate information indicative of the current state of their specified area of interest as well as the trend. For example, where the environmental conditions are between normal conditions and drought conditions it may be difficult for the stakeholder to objectively determine the trend (i.e. are conditions deteriorating further into drought or recovering).
  • the present system provides an indicator that captures the trend. This may allow the stakeholders to more effectively act and prepare for present and future conditions.
  • previous systems of drought monitoring may include looking at climate at the larger level and area (e.g. meteorological monitoring run by national or state level organisations) which, although may be temporally relevant to farmers (due to resources to provide regular updates to weather forecast), lack resolution, granularity and accuracy.
  • meteorological monitoring run by national or state level organisations
  • such monitoring may not take into account local conditions that may be relevant.
  • farmers may have various sensors and apparatus to monitor some factors indicative of the local conditions, this may be on an ad-hoc basis and such tests may be deficient in factoring other variables (because of lack of resources, coordination of information with other farmers or organisations, etc.).
  • the present disclosure may ameliorate or overcome these issues.
  • the processing device may be further configured to determine the soil water content, plant growth and rainfall as an aggregation of respective sensor data from a specified preceding period.
  • the processing device may be further configured to:
  • the processing device may be further configured to:
  • the range or threshold value may be expressed as a percentile range or percentile threshold value.
  • the processing device may be further configured to determine the soil water content, plant growth and rainfall as normalised data values based on historical soil water content, historical plant growth and historical rainfall.
  • the system may be further configured to detect an environmental condition of a geographic parish that includes an associated plurality of specified areas, wherein the processing device is further configured to:
  • the notification to the communication device includes the processing device configured to send the environmental condition associated with the geographic parish.
  • the associated plurality of specified areas may include specified areas that the geographic parish overlap.
  • the associated plurality of specified areas may further include specified areas that are proximal to the boundary of the geographic parish.
  • the one or more remote sensors may comprise one or more of the following:
  • the one or more remote sensors may comprise one or more of the following:
  • the system may further include one or more aerial drones, wherein the aerial drones have one or more of the remote sensors on board.
  • the aerial drones may include a communication module to send data to the processing device.
  • the climate data source may be associated with one or more of the remote sensors that collect sensor data associated with the specified area.
  • the processing device is further configured to generate, at a display, a graphical representation of the notifications as an overlay of a map that includes at least the specified area.
  • a computer-implemented method for detecting an environmental condition of a specified area comprising:
  • - determined rainfall is greater than the indicator for rainfall during drought conditions but less than the indicator for rainfall during normal conditions
  • the method may further comprise: - determining the indicators for soil water content, plant growth and rainfall during early recovery conditions associated with the specified area based on historical data;
  • the method may further comprise:
  • - determined soil water content is within the indicator for soil water content strong recovery conditions
  • - determined plant growth is within the indicator for plant growth strong recovery conditions
  • the method may further comprise:
  • the method may further comprise:
  • - determined soil water content is within the indicator for soil water content during normal conditions; and - determined plant growth is within the indicator for plant growth during normal conditions;
  • the method may further comprise generating, at a display associated with the communications device, a graphical representation of the notification as an overlay on a map that includes at least the specified area.
  • FIG. 1 is a schematic of a system for detecting environmental conditions
  • FIG. 2 is a flow diagram of a method performed by a processing device in the system
  • Fig. 3 is a diagram of an example phases of wet and dry conditions
  • Figs. 4a to 4d are additional examples of wet and dry conditions
  • FIG. 5 is a schematic example of determining soil water content
  • Fig. 6 is a schematic of an optimisation workflow for determining model and model parameters
  • Fig. 7 is a graph illustrating verification of the model and model parameters
  • Fig. 8 is a map of a region including notification of environmental conditions overlayed
  • Fig. 9 is shows a map of a subset of a region that includes a geographic parish, where the geographic parish overlaps a plurality of gridded specified areas;
  • Fig. 10 illustrates a user interface at a display showing a map of a state divided into regions with notification of environmental conditions overlayed
  • Fig. 1 1 illustrates the user interface at the display after selection of a region and showing shires within the region
  • Fig. 12 illustrates the user interface at the display after selection of a shire and illustrating details of a selected geographic parish in the selected shire;
  • Fig. 13 illustrates a schematic of one variation of the system
  • Fig. 14 illustrates a schematic example of a processing device.
  • the present disclosure includes an environmental monitoring system 1 for detecting environmental conditions of a specified area 3 as illustrated in Fig. 1.
  • the system 1 includes remote sensors 4 that detect information associated with the specified area 3, and sends respective data to a processing device 13.
  • a climate data source 9 also sends data associated with the specified area 3 to the processing device 13.
  • the remote sensors 4 may include rain gauges 7, soil water sensors 5, temperature sensors, plant growth monitoring devices, and other sensors.
  • the climate data source 9 may include meteorological data sources and sensors that provide rainfall data. In some examples, the climate data source 9 may be, or receive, data from remote sensors 4, such as rain gauges, associated with the specified area 3.
  • a database 1 1 stores historical data that is based on historical information on soil water, plant growth and rainfall associated with the specified area.
  • the processing device 13 receives data from the remote sensors 4 and climate data source 9 to determine 110 the soil water content, plant growth and rainfall associated with the specified area 3.
  • the processing device 13 further determines 120 a drought direction associated with the specified area 3 that indicates a trend in precipitation based on the rainfall data from the climate data source 9 and the historical rainfall from the database 11.
  • the processing device 13 may also determine 130 respective indicators for soil water content, plant growth, and rainfall during conditions such as drought conditions, normal conditions, early recovery conditions, strong recovery conditions. Such indicators may include threshold values. This may be based on historical data associated with the specified area 3.
  • the processing device 13 may then determine 140 one or more environmental conditions based on the drought direction, soil water, plant growth, rainfall and the determined indicators.
  • environmental conditions may include one of:
  • the processing device 13 may then send, over a communications network 15, a notification to a communication device 17 indicating the determined environmental condition associated with the specified area 3.
  • a communication device 17 may be a mobile communication device or a computer of a farmer who is then alerted to the environmental condition their property and may, in turn, take appropriate action to prepare for, take advantage or and/or mitigate the effects of the environmental condition. Since this notification is for the specified area 3, this may provide the farmer (or other stakeholder) more granular and accurate information than other systems.
  • One technical advantage of the system 1 is that it can provide an indication on whether conditions are deteriorating or improving at parts of the phase between drought and non-drought. This is illustrated in Fig. 3 whereby the "warning" and “strong recovery” on the curve may have similar absolute wet/dry characteristics but should be categorised as different environmental conditions to take into account a trend in improving or deteriorating conditions. This is in contrast to the technical problem of other systems that may show levels of absolute wet or dry characteristics without indicating deterioration or improvement. Such information may be of significant importance to stakeholders such as farmers and
  • this may include using the notification as an input to an automated system such as automated watering and feeding systems based on the environmental condition.
  • Figs. 4a to 4d show other examples of phases where the warning and recovery take various shapes.
  • the system may advantageously provide an indication of the environmental condition of the specified area 3 in such other, non-idealised, phases of variability.
  • the remote sensors 4 may be used to collect sensor data used to determine soil water data, plant growth data and rainfall data associated with the specified area 3. It is to be appreciated that in some examples, the specified area 3 may have the remote sensors 4 located therein to provide direct measurements and data from the specified area.
  • a specified area 3 may not have sensors 3 located therein and therefore sensor data associated with the specified area may be determined by interpolating data directly measured from other areas. For example, if a type of remote sensor 4 is not physically located in the specified area 3, but adjacent areas have such remote sensors 4, then the information from such remote sensors may provide data that can be used for determining the environmental condition of the specified area 3. In one example, say the specified area 3 is in between two other areas that have remote sensors, the interpolated value) may be used to provide that sensor data for the specified area 3.
  • the remote sensors 4 may include individual sensors that are connected to the processing device 13 via a communications network 15. In other examples, the sensors may send data to a data logger, which in turn, is connected to the communications network 15. In some other examples, the remote sensors 4 may be part of a meteorological (weather) station.
  • the remote sensors 4 may include sensors operated by, or with the permission of, property owners or lessors. For example, this may include remote sensors 4 used by farmers on the property in the specified area 3. In some examples, from sent from such sensors are aggregated to the specified area 3 (or larger area) to reduce privacy or confidentiality concerns. Therefore the remote sensors 4 may include a network of sensors from various including individuals, business, organisations, or other stakeholders and service providers that in other circumstances may not wish to share data or collaborate with one another.
  • remote sensors 4 that may be used to determine soil water content, plant growth and rainfall will now be described. It is to be appreciated that this determination may include using data from such remote sensors together with models in the processing device 13. Rain gauge
  • the remote sensors 4 may include rain gauges 7 to determine rainfall associated with the specified area 3.
  • Various types of rain gauges may be used including a tipping bucket rain gauge that tips after a specified amount of precipitation is collected, and wherein the numbers of tips are counted to determine the total precipitation.
  • Other examples may include a rain gauge based on weighing the collected precipitation.
  • the rain gauge may provide rainfall data of the specified area as well as providing data that may be used by models to determine the soil water content and plant growth.
  • Soil water sensors 5 provide data that can assist in determining water content in the soil.
  • a known method of determining soil water content may include gravimetric analysis of a soil sample, which as an example may include obtaining and weighing the soil sample, drying the soil sample and weighing the dried soil sample. The weight difference may then be used to determine the water content of the soil sample.
  • Other methods for determining (which includes estimating) the water content may include using a soil capacitance probe. Changes to the water content of the soil can vary the capacitance and this change can be measured to determine changes (or the value) of the water content.
  • An example of a soil water sensor includes the OTTO family of soil moisture probes offered by TOIP Pty Ltd.
  • Another example includes the AQUACHECK SUB-SURFACE PROBE offered by AQUACHECK (PTY) LTD.
  • the soil type, composition, crop, etc. may affect the readings from the soil water sensors. Therefore the sensors may be calibrated by using other methods of determining soil water content.
  • the soil capacitance probe may be calibrated by measuring soil capacitance with a soil sample, whereby the soil sample further undergoes gravimetric analysis to determine the water content.
  • data from other sensors may be used to infer the soil water content. These may include information from rain gauges, temperature sensors, evaporation sensors, radiometers (e.g. light sensors), etc. Such data may be used in models (discussed in further detail below) to determine the soil water content in the soil.
  • the remote sensor 4 may also include a temperature sensor, such as a thermometer, associated with the specified area 3. This may include a digital thermometer located at the specified area 3 to measure air and/or soil temperature that is sent, over the communications network 15, to the processing device 13.
  • a temperature sensor such as a thermometer
  • remote sensors may include electromagnetic sensors in the visible and non-visible ranges (such as the infrared spectrum).
  • thermography system may be used to determine temperatures associated with the specified area 3, and may include a heat map of a geographical region.
  • a thermography camera may be mounted on observation aircraft, aerial drones 16 or satellites 18.
  • Temperature data may be used in models to determine soil water content and plant growth in the specified area.
  • the temperature including maximum temperature, minimum temperature, average temperature, median temperature
  • This may be further based on historical data to determine the correlation and parameters for the model.
  • the temperature may also be used to model the plant growth, with the temperature as factors that affects plant growth, such as regulation of photosynthesis, crop development, frost and decay of plant material.
  • the remote sensors may also include a light sensor to determine solar radiation associated with the specified area 3. In some examples, this may include a radiometer to measure the radiant flux of sunlight. In other examples, this may include a pyranometer. In some examples, this may be located at the specified area 3. In other examples, this may include sensors away from the surface of the specified area 3, such as from an aircraft, aerial drone 16 or satellite 18.
  • the remote sensors 4 may also include an evaporation gauge to assist determination of evaporation in the specified area 3.
  • the evaporation gauge may include an evaporation pan with water, whereby the change in level of water in the pan may be used to determine evaporation in the specified area 3.
  • the evaporation rate may be used in a model to determine the amount of water evaporation in the soil, and hence assist determination of the soil water content. As the amount of water also affects plant growth, this may also be used to model plant growth.
  • the remote sensors 4 may also include hydrometer(s) to measure moisture content in the atmosphere associated with the specified area 3.
  • the hydrometer may use one or more changes in capacitance, resistance and/or thermal conductivity of air to determine the humidity.
  • the remote sensors 4 may also include anemometers to measure wind speed associated with the specified area 3.
  • the wind speed may be used to provide parameters for the models that relate to convection that may affects evaporation and plant growth.
  • the wind speed (and direction) may also be used to determine changes in temperature at the specified area 3, or other areas.
  • the direction and speed of air may be modelled to determine the temperature of a specified area 3 downwind.
  • the remote sensors 4 may also include barometers to measure pressure associated with the specified area 3. This may be used to assist modelling climate conditions that affect the specified area.
  • Examples of data that is collected from remote sensors 4 that may be used as indicators of plant growth include:
  • NDVI Vegetation Index
  • Leaf Area Index This measures the number of equivalent layers of leaves relative to a unit of ground area
  • These plant growth monitoring devices and systems provide plant growth data that may be used to determine plant growth This may include evaluating the photochemical reflectance index to assist in determining plant growth. This may include a system that uses such plant growth data in a model (discussed in further detail below) to determine the plant growth. This may include evaluating the photochemical reflectance index to assist in determining plant growth.
  • sensors or methods may be used to receive indicators of plant growth.
  • paddock monitoring with such as active optic techniques (for example the GreenSeeker (hand held or vehicle mounted) product offered by TRIMBLE INC. that emit light towards target plants and measures reflected light), visual (RGB) cameras or Mutispectral and Hyperspectral sensors for determining the crop and pasture yield that may be indicative of plant condition in the specified area 3.
  • active optic techniques for example the GreenSeeker (hand held or vehicle mounted) product offered by TRIMBLE INC. that emit light towards target plants and measures reflected light
  • RGB visual
  • Mutispectral and Hyperspectral sensors for determining the crop and pasture yield that may be indicative of plant condition in the specified area 3.
  • Further information about crop and pasture growth may be determined by a variety of non-destructive and or destructive methods. Examples of non-destructive methods include standardised visual estimation of pasture and crop biomass, physical measurements of plant height with a ruler, laser and sonar device and quadrant based counts of pasture species composition (BOTANAL). Destructive methods include harvesting of crop
  • one or more of the remote sensor(s) may be mounted on mobile platforms. This may allow the remote sensors 4 to be easily shared with multiple specified areas compare to permanent or semi-permanent sensors.
  • This may include aircraft, ground vehicles, satellites 18 etc. This may also include mobile remote sensor stations that are containerised and/or trailer mounted. In further examples, one or more of the remote sensors may be mounted on an aerial drone. Such aerial drones may be autonomous, semi-autonomous or remotely piloted. Aerial drones 16 may allow the remote sensors 4 to be more mobile and cover a greater area compared to fixed sensors whilst being more cost effective that manned aircraft and satellites. Furthermore, aerial drones 16 may safely fly lower than manned aircraft and satellites 18 which may allow the drones to captures data at a higher resolution. Therefore, the higher resolution data may be processed to allow individual farmers to monitor environmental conditions to a lower level - such as a paddock level or lower.
  • the data from the aerial drones may be private data for the farmer that is used in conjunction with the environmental monitoring system 1 to determine environmental conditions for specific areas of their land.
  • the aerial drones may include a communication module to provide real-time or near real-time data.
  • the remote sensors 4 may include sensors utilised by stakeholders and others in the specified area 3. However it is to be appreciated that some remote sensors 4 may be operated by government, university, or other research organisations such as meteorological organisations. Such organisations may receive information from a large number of remote sensors 4 and combine them to provide climate data for an area. In some examples, such remote sensors may include weather radar and meteorological sensors on board aircraft, aerial drones 16 and/or satellites 18. The information may therefore be compiled and stored at a meteorological bureau as a climate data source (9).
  • the processing device 13 may receive at least some of the data indirectly from the remote sensors 4, as the data is procured from the climate data source (9).
  • the database 1 1 may receive data from various sources, including remote sensors 4, farmers, stakeholders, weather bureau, other organisations, and the processing device.
  • the database 11 may store historical data based on sensor data received from remote sensors 4 in the past. This may include historical data in relation to soil water data, plant growth data, and rainfall data.
  • the historical data may also be based on historical records from other sources, such as historical records of a meteorological bureau, data from other research (such as from universities, scientists or other organisations). Forecast data may also be received by the data base from meteorological organisations like the Bureau of
  • the database 1 1 may be distributed and include multiple databases 11 that are in communication with the processing device 13.
  • categories of data may be stored separately based on the respective information source (e.g. historical rainfall data may be stored at the meteorological bureau).
  • This uses Application Programming Interfaces, high speed data transfer platforms (e.g. Aspera transfer platform offered by ASPERA INC.) and standard File Transfer Protocols. It is to be appreciated that the historical data may also be stored on cloud storage.
  • the processing device 13 may be operated centrally, or distributed to multiple processing devices across the network 15. An example of a processing device is discussed in further detail below.
  • the processing device 13 performs the method 100 shown in Fig. 2 of: determining 110 the soil water content, plant growth and rainfall; determining 120 the drought direction; and determining 130 indicators for soil water content, plant growth and rainfall during various conditions such as drought, non drought and recovery conditions. Furthermore, the processing device 13 evaluates these factors to determine 140 the environmental condition. In response to determining the environmental condition, the processing device 13 sends 150 notifications to communication devices that may be used to alert stakeholders affected by the environmental condition.
  • the notifications may include generating, at a communications device 17, a map overlay showing specified area(s) affected by one, or more, environmental condition.
  • the database 11 may be collocated with the processing device 13, whereby the system 1 is operated by an organisation such as a government agency or primary industries organisation.
  • the soil water content is indicative of water in the soil which is an indicator of the water readily available to plants in the specified area 3.
  • the soil water content may be normalised and expressed as a percentile compared to historical soil water content. For example, expressing the soil water content between 1-10 may indicate that there is little or no water in the soil, whereas expressing the soil water content close to 100 may indicate it is close to the historical maximum amount of moisture in the soil.
  • the soil water content may also be expressed as a summation of the soil water content over a preceding time period. For example, as a summation of a preceding twelve month period. This summation may provide a better general trend of the soil water content of the specified area 3 over the preceding time period as opposed to a snapshot at a specific point in time.
  • the plant growth and rainfall discussed below may also be expressed as a summary and normalised in a similar manner.
  • the soil water content will differ between layers of soil.
  • the very top layer of soil may be drier after sunshine than lower layers.
  • the relevant layers of soils are layer 1 (0 to 10 centimetres from the top) and layer 2 (1 1 to 45 centimetres from the top).
  • the determination of water content may include an aggregation of these two layers.
  • some stakeholders due to their requirements, find other layers of soil relevant. For example, a farmer may have crop that has shallow roots such that the soil water content of layer 1 is more important.
  • the soil water may be measured directly by soil water sensors measuring the soil. However in some circumstances this may not be practical to measure on a daily basis due to time, required number of samples, and volume of data.
  • the soil water content is determined based on receiving sensor data that is then input into a model to determine the soil water content for the specified area 3. From time to time, this model may be calibrated using data directly measured from the soil water sensor(s) 5 to improve accuracy.
  • the inputs to the model may include rainfall, temperature, humidity, winds and other meteorological variables to derive potential evapotranspiration.
  • the model 61 may include determining the soil water content at respective layers based on:
  • the model takes into account factors that affect the soil water content to provide a value of the soil water content for the specified area.
  • the runoff may be determined as:
  • Run cover is the fractional cover modifier on the process of runoff
  • yldcover50 is the biomass value at which the rate of runoff begins to decline runoff powet is a parameter governing the shape of the runoff-cover relationship
  • the infiltration and drainage may be determined as:
  • Drainagel is the drainage through soil layer 1
  • SW1 is the soil water content of soil layer 1
  • Drainage2 is the drainage through soil layer 2 (which is below soil layer 1)
  • SW2 is the soil water content of soil layer 2
  • Drainage3 is the drainage through soil layer 3 (which is below soil layer 2)
  • SW3 is the soil water content of soil layer 1
  • FC1, FC2 and FC3 are field capacity of soil layers 1, 2 and 3 respectively [0112]
  • potential evaporation may be determined as:
  • Transcover is Term to convert fractional (green) into the transpiration rate modifier Dead C o V er is Term to convert fractional (dead) into the transpiration rate modifier P ttrans is potential transpiration rate of a grass sward or crop
  • the wail water supply may be determined as:
  • WP1, WP2 and WP3 are the wilting points of plants accessing soil water at the respective layers (layers 1, 2 and 3).
  • roots layers is the proportion of roots in respective layers
  • Profile_swi is the soil water ratio for the whole profile
  • potential transpiration may be determined as:
  • soil evaporation may be determined as:
  • ADl is a parameter to define the tension at which the water is held on the soils matrix Plant growth
  • Plant growth may be determined by inspection of individual plants in the specified area 3. However, this may be time consuming and subjective thereby leading to inaccuracies and inconsistencies.
  • the plant growth may be determined by sensor data that is indicative of plant growth that may include the environmental conditions of the specified area 3, during the preceding time period that may indicative of the plant growth.
  • sensor data that is indicative of plant growth that may include the environmental conditions of the specified area 3, during the preceding time period that may indicative of the plant growth.
  • favourable conditions for plants may be indicative of favourable plant growth.
  • This model may be supplemented by and calibrated with other data objective data, such as data from the plant growth sensors described above.
  • the plant growth may be determined as gross primary production (GPP) or net primary production (NPP). This may be determined as the assimilation of carbon by plant growth. This may require determination of factors that affect plant growth including available soil water (that may include the results of determining soil water content described above), temperature and the associated constraints on photosynthesis, and use of radiation by the plant (which may include determination of light to plants in the specified area based exposure to sunlight, time of year, etc.). Agricultural practices such as grazing pastures and harvesting crops also modify plant growth through removal of the growth apparatus (leaves and stems) of the plant.
  • GPP gross primary production
  • NPP net primary production
  • Example 1 of determining plant growth (Eco-physiological model driven by climate data and sensor data )
  • climate data may include information such as rainfall, temperatures, wind, etc. that may be sourced from a meteorological station or climate data source 9. This may also take into account remotely sensed information about the status of radiation use by the plant (e.g. by reference to the leaf area index (LAI)).
  • LAI leaf area index
  • the model may be based on the following equation for Gross Primary Production (GPP):
  • the left set of terms describe energy use-carbon assimilation, where a is the maximum pasture or crop production governed by nutrient levels and plant physiology, Q is photosynthetically active solar radiation derived from monitored radiometer data (i.e. light sensor data), / is the canopy area term derived from remotely sensed DVI and v is stomatal function.
  • T The rate of energy use-carbon assimilation is limited firstly by the thermal environment described by the term T.
  • T is a function of the daily temperature regime where monitored maximum and minimum temperatures are constrained with crop and pasture specific parameters defining the minimum, maximum and optimum response thresholds as well as the photosynthetic pathway (for C3 or C4 species).
  • T also includes a temperature damage term which limits carbon assimilation under frost conditions for some species.
  • PAW water limitation
  • W is a ramp function based on plant available water
  • the above described model may be further refined in an "assimilation mode" whereby parameters of the model may be adjusted by remotely sensed information.
  • remote sensed information about the actual status of radiation received at the specified area 3 may be used in place of, or to calibrate relevant parameters, of the leaf area index (LAI) used in the equation above.
  • LAI leaf area index
  • the GPP equation above may also contain an additional term for calculation of carbon assimilation of crops.
  • This describes the development [D] of annual wheat based on the work of Angus et al. (Angus J, Mackenzie D, Morton R and Schafer C (1981). Phasic development in field crops II. Thermal and photoperiodic responses of spring wheat. Field Crops Research 4, 269-283) , with the structure defined by Lui (Liu, D.L., 2007.
  • the development coefficient [D] is a function of temperature and photoperiod through germination (G), emergence to floral initiation (S I), pre-flowering (S2) and flowering to harvest (S3) stages. G is determined by NDVI changes in the months April-June, while harvest is undertaken at 18 December thereby resetting the [D] value to 0 annually.
  • the harvest date assumption is suitable when constructing drought indicators which are tracking potential climate limitations, but a specific date can be estimated to improve the prediction actual yields. It is to be appreciated that [D] may be selected for characteristics of other plants (or combination of other plants). In some examples, D may represent idealised models of a collection of plants.
  • the model at least in part, may be based on crop harvest.
  • actual crop harvest may be recorded in the database 11, and this information may be recorded to verify and/or optimise the model parameters.
  • the plant growth may be a combination of the features described in the above examples of determining plant growth.
  • the determined rainfall is indicative of the rainfall at the specified area 3. In some examples, this includes rainfall over a specified time period since for practical purposes it is not the rainfall over a given hour or day that is relevant, but for a longer time period.
  • the rainfall is provided as a percentile rank of daily rainfall aggregated over a twelve month summation period. From herein the summation period is termed the 'aggregation window' .
  • the previous 12 months' (365 days) data are aggregated then ranked within the baseline range (for example historical rainfall data obtained from 1985-2015). This is repeated for every day from 1915 to build an historical data base of 100 years.
  • the rainfall may be expressed as a value between 0 and 100, where, for any given climatic environment, values approaching 0 are close to the driest on the historical record and those approaching 100 are close to the wettest.
  • determining the soil water content, plant growth and/or rainfall involves modelling of the specified area 3.
  • this may include using inputs such as using MODIS derived gross primary productivity data and/or MODIS derived actual evaporation data (which may in themselves be outputs of a model). This may also include field based data such as crop yield measurements, pasture yield growth measurements and soil water data from soil water sensors and sensor data from other remote sensors described above. Thus development of the model may include a technique of data assimilation. This developmental data may include historical data described above.
  • the model may also be calibrated for the specified area 3. This may include using historical data associated with a specified area 3, so that the model(s) are calibrated with parameters relevant local conditions and factors relevant to the specified area 2.
  • a technique of data assimilation is used and embedded into the model so that the model can be optimised with an automated procedure so that parameters can be continually tuned to improve accuracy.
  • This may include tuning parameters by receiving additional information to calibrate and optimise the models.
  • additional information may include sensor data, field data, climate data etc., as the system 1 is in use.
  • parameters may need to be estimated (for example if no sensor data is available for a specified area). In some examples, this may include providing parameters that are regionally based as a substitute for such parameters. An example of parameter estimation will now be described.
  • Pasture communities grade from subtropical in the north with greater prevalence of C4 grasses through to western land area where rangelands pastures dominate, through to temperate C3 grasslands. In the high rainfall zone micro-meteorological variation is significant given the effects of aspect and altitude.
  • An automated optimisation procedure 63 was developed to solve the modelling framework and yield a set of state wide parameters as illustrated in the example in Fig. 6.
  • An objective function was formed as the weighted sum of squares (taking the Jacobian) between simulated values and the GPP values from 2000-2012, derived from the application of the diffuse model (see Donohue, Randall; Hume, Iain; Roderick, Michael; McVicar, Tim;
  • the drought direction indicates a trend in precipitation based on the determined rainfall.
  • An example of determining 120 a drought direction will now be described. For each day of the determined rainfall a robust linear regression, which is not overtly sensitive to outliers, is fitted to the previous 90-days, and the slope of this function retained to form the drought direction. The value is rescaled to a range between -100 and +100 to visualise the drought direction. The key information is not the magnitude of the DDI across this scale, but the sign of the drought direction. If the value is negative there is a drying trend, and if it is positive the area is getting wetter.
  • the drought direction is used as a categorical index only.
  • the drought direction provides information about trends in seasonal climate for the 'Warning' and 'Recovery' phases (e.g. early recovery and strong recovery). It was developed because early trial and error tests highlighted issues in reliably distinguishing between the 'warning' environmental condition from the two 'recovery' phases. In particular, for "warning” and “recovery, there may be circumstances where the rainfall, soil water content, and plant growth may be similar, but where the trend in environmental condition is trending towards different conditions. Accordingly, the drought direction allows evaluation of the trend and to distinguish between conditions that is in "warning" versus the "recovery” environmental conditions. Determining 130 indicators for drought conditions, normal condition, and recovery conditions
  • Determining the various environmental conditions require the processing device to evaluate various inputs, in particular the soil water content, plant growth, rainfall and drought direction.
  • the absolute values for these metrics may be normalised to assist in this evaluation process. This may also assist in improving accuracy of this determination normalising these values, for the specified area, would provide a more relevant indication of the state of the specified area 3.
  • the soil water content may be expressed as a value between 0 and 100 whereby the 0 is the driest and 100 is the wettest for that specified area 3. Similarly for plant growth, 0 may indicate the lowest plant growth and 100 may indicated the highest plant growth.
  • the method may then assist in identifying the indicators of respective conditions. This may be based on the percentile or percentile bands for various conditions. For example, an indicator of "normal conditions" for that metric may include values that are at or above a normal threshold (an in this example, the 50 th percentile). That is:
  • This normal condition may, of course, be expressed as a percentile band. For example where normal conditions are between the 50 th and 100 th percentile. It is to be appreciated that for each of the metrics, a different value or range for the "normal threshold" may be used. For example, the normal threshold for soil water content and rainfall may be the 50 th percentile, whilst the plant growth normal threshold may be the 60 th percentile.
  • an indicator of "drought conditions" for each metric may include values that are at or below a drought threshold (and in this example, the 10 th percentile). This may also be expressed as a percentile band between 0 and 10 th percentile for drought conditions.
  • the values may be between the drought conditions and normal conditions. Continuing from the above example values, this would be between the 10 th percentile and 50 th percentile.
  • an early recovery condition may be between the drought threshold (e.g. 10 th ) and an intermediate recovery threshold (for example the 30 th percentile).
  • a strong recovery condition may be between the intermediate recovery threshold and the normal threshold (e.g. between the 30 th and 50 th percentile).
  • thresholds and percentile bands are examples. It is to be appreciated that the actual threshold value and/or bands may be different in other examples. Furthermore, it is to be appreciated that in some examples, “greater than” or “less than” the threshold may be adapted to "greater than or equal to” or “less than or equal to” and vice versa.
  • the above mentioned indicators of normal conditions, drought conditions, and intermediate recovery thresholds and/or percentile bands may be determined based on historical data. In some examples, these indicators may be specified (such as by an individual or organisation). Thus these indicators may be stored in the database 11 and determining the indicators may include receiving these indicators from the database 11. In yet other examples, these indicators may be determined by receiving the indicators, via the
  • the determined indicators are based on a percentile of normalised data.
  • the absolute values may be used for the metrics.
  • the rainfall metric may be expressed in average millimetres per month over a preceding 12 month period.
  • the indicator may be determined and expressed with a corresponding base.
  • the threshold for normal conditions may be where the average is 60mm per month or above, and the drought threshold may be where the average is 10 mm per month or below. It is to be appreciated that such threshold values may be determined based on historical data for each specified area 3 (or for a region including a plurality of specified areas). Such determination may be advantageous as these absolute values may be different for temperate regions versus, for example, semi-arid regions. Determining 140 the environmental conditions
  • the above metrics may be evaluated by the processing device 13 to determine an environmental condition of the specified area 3.
  • the warning environmental condition is between drought and non drought conditions. Accordingly, the processing device 13 may determine this to occur where any one of the following metrics are between the drought threshold and normal threshold. That is:
  • Drought threshold e.g. 10 th percentile
  • Soil water content e.g., water content
  • Drought threshold e.g. 10 th percentile
  • Plant growth ⁇ normal threshold e.g. 50 th percentile
  • Drought threshold e.g. 10 percentile
  • Rainfall ⁇ normal threshold e.g. 50 percentile
  • the warning environmental condition is also indicative of a downward trend. Therefore, in addition, the processing device 13 must also determine that the drought direction is indicating a trend towards precipitation deficit. In one example (where a negative number indicates a drying trend), this may be expressed as:
  • the advantage of incorporating the drought direction is that the processing device 1 1 can differentiate the warning environmental condition from the recovery environmental conditions where such latter conditions may have metrics that overlap with the warning environmental condition. This is important as this result affects the response action of a farmer or other stakeholder.
  • the early recovery 37 environmental condition is between drought and non drought environmental conditions that are starting to improve and out of drought (but may have a chance of re-entering drought). Accordingly, the processing device 13 may determine this to occur where all of the following metrics are between the drought threshold and intermediate recovery threshold. That is:
  • Drought threshold e.g. 10 th percentile
  • Soil water content e.g. 30 th percentile
  • Drought threshold e.g. 10 th percentile
  • threshold e.g. 30 th percentile
  • Drought threshold e.g. 10 th percentile
  • Rainfall e.g. 30 th percentile
  • the warning environmental condition is also indicative of an improving trend and where rainfall has occurred. Therefore, in addition, the processing device 13 must also determine that the drought direction is indicating a trend towards precipitation surplus. In one example, this may be expressed as:
  • the strong recovery 39 environmental condition is between drought and non drought environmental conditions but strongly indicating an improvement towards non drought conditions. Accordingly, the processing device 13 may determine this to occur where all of the following metrics are between the intermediate recovery threshold and normal threshold. That is:
  • the processing device 13 must also determine that the drought direction is indicating a trend towards precipitation surplus. In one example, this may be expressed as:
  • the drought environmental condition is where any of the metrics indicate a drought condition. Accordingly, the processing device 13 may determine this to occur where any one of the following metrics are at or below the drought threshold. That is:
  • Drought threshold e.g. 10 th percentile
  • Plant growth e.g. 10 th percentile
  • the processing device 11 may not need to take into account the drought direction.
  • the non drought 31 environmental condition is where all of the metrics indicate that the specified area is in a non drought condition. Accordingly, the processing device 13 may determine this to occur where all one of the following metrics are at or above the normal threshold. That is:
  • the processing device 1 1 may not need to take into account the drought direction.
  • the processing device 13 in response to determining one or more of the
  • the environmental conditions sends a notification over a communications network to a communication device to indicate the environmental condition.
  • the processing device may send a notification to a stakeholder of a specified area 3 (such as a farmer) to alert them that the specified area in a warning environmental condition. This may allow the stakeholder to take action to ameliorate the effects of the environmental condition (for example diverting water from alternative sources).
  • the notification may be sent based on a change of environmental condition. For example a change from a drought condition to early recovery condition may trigger a notification to a stakeholder that conditions are improving. For example, this change may alert a farmer to being sowing crop.
  • the notifications may be sent to a communications device that is in communication with machinery and equipment.
  • the notification may be used to activate or deactivate irrigation equipment based on the determined environmental condition.
  • the notification may be used to activate or deactivate livestock feeding equipment.
  • the notification may be generated on a display associated with the communication device 17.
  • the notification may be generated graphically on a map overlay.
  • Fig. 8 shows a map of a region (New South Wales) with notifications of environmental conditions that are overlayed. For example, specified areas that are in warning, early recovery, strong recovery environmental conditions have respective differentiating colours. Areas in drought and non drought may also have respective colours. It is to be appreciated patterns or other visual differentiators may be used to indicate respective notifications.
  • privacy or confidential information concerns may require adjustment in the granularity of the data, yet still require accurate representation of the data for a particular area.
  • the specified area may be calculated at a 5 km by 5 km grid, and this resolution may allow third parties (other than say, a farmer of that area) from obtaining commercially sensitive information on the environmental condition of that land (for example, using the information to derive information on specific productivity of that land that may be affected by other factors).
  • the environmental conditions may be sent publicly to third parties
  • the information in relation to the environmental condition of the specified area 3 may be aggregated with other nearby areas 3.
  • some governments classify areas by "parish". Such a parish may contain multiple specified areas 3 in the vicinity of each other.
  • sending notification of the environmental condition 2 may include sending information in relation to the geographic parish that the specified area 3 is located within This may be useful for public reporting to maintain privacy and confidentiality.
  • FIG. 9 shows a subset 53 of a geographic region 51 whereby the subset is divided into grids 55 for modelling and calculation purposes.
  • a parish 57 partially overlaps a plurality of the grids 55.
  • the grids 55 each represent a respective specified area 3 in which the environmental conditions and metrics are individually calculated).
  • the environmental condition and metrics are aggregated from the individually calculation scale (of 5km by 5 km) grids 55 to provide a result at a parish 57 level.
  • These scales of aggregation are shown in Fig. 9, outlining the parishes in relation to the underlying grids 55.
  • New South Wales (NSW) parishes provide a convenient reporting unit for a spatial process like drought, as they are fine enough to capture the spatial variability associated with major drought events across the State, but make it difficult to identify individual farms. This is an important requirement under privacy considerations where individual farm level information is not publicly disclosed.
  • NSW There are 7378 parishes in NSW with the larger parishes located to the west of the State reflecting sparser settlement patterns.
  • the algorithm performs aggregation by determining the grids 55 (i.e. a plurality of specified areas 3) that are associated with the parish 57. In some examples this association is determined by grids 55 that are overlapped wholly or in part by the parish 57.
  • the aggregation algorithm finds the relevant centroids 59 of the underlying grid 55, with a 4km 2 buffer around the parish boundary to account for uncertainty in the underlying gridded data. That is, associating grids 55 that are proximal to the boundary of the geographic parish 57. This is shown in the example for the parish "Athol" 57 in Fig. 9.
  • the processing device may determine the environmental condition of each of the associated grids 55 (specified areas 3) and determine the environmental condition that occurs the most frequently (i.e. the mode). This may then sent, over the communications network, as a notification of the environmental condition of the geographic parish 57. This aggregation at the parish level may alleviate privacy concerns.
  • the metrics such as the soil water content, plant growth, and rainfall
  • this calculation for the parish 57 may be performed by determining the average values for the metrics from the associated grids 55.
  • modelling may be performed and repeated in other scales.
  • the grids may be smaller (e.g. 2 metre by 2 metre grids) and then aggregated to a field (or other defined area) to support farm level decisions.
  • Fig. 10 illustrates a display 1 112 with a user interface 200 to provide a visual representation of the notification to a user.
  • the darker shade in the middle to western area 207 on the map 201 indicates that these areas are in the drought environmental condition.
  • a pie chart 209 shows the various percentages of the environmental condition for the state.
  • the map 201 of the state is further divided into smaller geographic regions. For example the "WESTERN”, “CENTRAL WEST”, “NORTH WEST” regions.
  • the user interface 200 may allow a user to select one of these regions for more in depth (and granular) details. For example, the user may use a cursor 213 to select the CENTRAL WEST region 211.
  • Fig. 1 1 illustrates the display 1 112 after selection of the CENTRAL WEST region 211. This illustrates the various environmental conditions within this region 21 1 with greater detail.
  • a pie chart 219 is provided to show the percentages of the environmental conditions in the region 211.
  • Fig. 1 1 also illustrates the boundaries of various smaller sub regions, which in this case are shires.
  • Fig. 1 1 there are eleven shires in the CENTRAL WEST region 21 1.
  • a user may select one of the shires for further detail, for example the PARKES shire 215.
  • Fig. 12 illustrates the display 1 112 after selection of the PARKES shire 215. This shows the various parishes within the PARKES shire 215.
  • each parish has a respective environmental condition (as represented by the shade/colour overlayed in the parish boundary).
  • the aggregation of environmental conditions to a parish level may be useful to alleviate privacy concerns where such information is publicly available.
  • Fig. 12 also illustrates a selection of the MINGELO parish 221 with the cursor 213.
  • the parish 221 is provided in a box 223. This includes the name of the parish and relevant information including the environmental condition (in this case a "warning" environmental condition), the drought direction, plant growth, rainfall and soil water content.
  • information on the environmental conditions are hosted on a publicly accessible server. This may include a web portal to access this information and to generate the above mentioned user interface 200 at a respective communication device of a user.
  • the information may be provided on a subscription basis, where a stakeholder makes contributions (directly, or indirectly) to receive the information on the environmental conditions.
  • the contribution may be financial and in other examples, the contribution may include information (such as data from remote sensors on a farm) for use by the system 1.
  • the information provided may be limited, for example, a farmer may subscribe to information related to their region or parish.
  • the notifications may be pushed to a communication device of the stakeholder.
  • a stakeholder may specify that they wish to be alerted to certain environmental conditions or changes to environmental conditions for specified areas.
  • This may include a message with images of a map and information as discussed above.
  • this may include a message with a URL (uniform resource locator) to allow the user to access the above information from a server.
  • URL uniform resource locator
  • the notification may be included in a report for the specified area 3, parish 57 or other geographic region. This may include the system generating a report with one or more of the above described maps and overlay. The report may also include the pie chart 209, 219 as well as details in relation to rainfall, soil water content and plant growth data. This report may be selectively sent to stakeholders in the respective specified areas 3.
  • the notification and report may be produced at the farm, paddock or sub-paddock level. This may include receiving information from multiple sources, remote sensors 4 associated with the local region. This can then be supplemented by a farmer's private data from their own remote sensors 4. Thus the farmer may be able to receive reports and notifications that are more specific to their use. This may include consideration of parameters such as type of usage, crops, livestock, and fertilizer (amongst other variables) for particular specified areas of their farm.
  • Fig. 14 illustrates another example of the system 301 with other information sources.
  • climate models 303 may be simulated in a computer (such as a forecaster 305) to adjust, and/or predict future, soil water content, plant growth and rainfall.
  • a computer such as a forecaster 305
  • the remote sensors 4 and/or climate data source 9 collecting respective data may be sampling at different rates and different times. Thus there may be a temporal mismatch (i.e. lead/lag) between the data.
  • modelling may be used to assist in predicting or interpolating data.
  • this may include (based on historical data) determining a model that can estimate an increase in soil water content for a given amount of rainfall that is received in the specified area 3. This estimated increase may then be used in conjunction with the last soil water data from the soil water sensor 5 to determine up to date soil water content.
  • the soil water data, plant growth data and rainfall data may be enhanced with information from field monitoring 307.
  • a farmer may observe characteristics indicative of plant growth and provide an input through a field monitoring device (such as a wireless communication device), and this information may be used to enhance the determined plant growth.
  • a farmer may observe crop yield and enter such information through the field monitoring device. This crop yield may be compared with historic yields and used as a factor to assist in determining plant growth.
  • the processing device 13 may provide localised information to a private monitoring community 309 so that a farmer may receive more detailed information at the farm level (instead of, for example, the parish level)).
  • This farm level reporting may provide information, for example, at a paddock level (even smaller).
  • the system 1 may include a computer that includes a processing device 1013 as shown in Fig 14.
  • the communication device 17 may also include a processing device.
  • the processing device 1013 includes a processor 1102 connected to a program memory 1104, a data memory 1106, a communication port 1 108 and a user port 1 110.
  • the program memory 1104 is a non-transitory computer readable medium, such as a hard drive, a solid state disk or CD-ROM.
  • Software that is, executable program instructions that are stored on program memory 1104 causes the processor 1 102 to perform the method 100 illustrated in Fig. 2.
  • the processing device may include multi-core processors. In further examples, multiple processing devices may be configured for multiprocessing to perform the method.
  • the processor 1 102 may receive data, such as sensor data or historical data from data memory 106 as well as from the communications port 108 and the user port 1 1 10.
  • the user port 1 110 may also be connected to a display 1112 that shows a visual representation 1114 of the notification, which may include a map overlay, as well as forming a user interface to a user.
  • communications port 1108 and user port 11 10 are shown as distinct entities, it is to be understood that any kind of data port may be used to receive data, such as a network connection, a memory interface, a pin of the chip package of processor 1 102, or logical ports, such as IP sockets or parameters of functions stored on program memory 1 104 and executed by processor 1102. These parameters may be stored on data memory 1106 and may be handled by-value or by-reference, that is, as a pointer, in the source code.
  • the processor 1 102 may receive data through all these interfaces, which includes memory access of volatile memory, such as cache or RAM, or non-volatile memory, such as an optical disk drive, hard disk drive, storage server or cloud storage.
  • volatile memory such as cache or RAM
  • non-volatile memory such as an optical disk drive, hard disk drive, storage server or cloud storage.
  • the processing device 1013 may further be implemented within a cloud computing environment, such as a managed group of interconnected servers hosting a dynamic number of virtual machines.
  • Suitable computer readable media may include volatile (e.g. RAM) and/or non-volatile (e.g. ROM, disk) memory, carrier waves and transmission media.
  • Exemplary carrier waves may take the form of electrical, electromagnetic or optical signals conveying digital data steams along a local network or a publically accessible network such as the internet.

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Abstract

La présente invention concerne un système de surveillance environnementale (1) et un procédé (100) pour détecter une condition environnementale d'une zone spécifiée (3). Le système (1) comprend un ou plusieurs capteurs à distance (4) et/ou une source de données climatiques (9) pour fournir des données de capteur indiquant une teneur en eau du sol, une croissance de plantes et une hauteur de pluie associées à la zone spécifiée. Un dispositif de traitement (13) est configuré pour : déterminer (110) une teneur en eau du sol, une croissance de plantes et une hauteur de pluie sur la base de données de capteur et de données historiques; déterminer (120) une direction de sécheresse associée à la zone spécifiée (3) qui indique une tendance de précipitation; et déterminer (130) des indicateurs pour la teneur en eau du sol, la croissance de plantes et la hauteur de pluie pendant des conditions de sécheresse et des conditions normales. Le dispositif de traitement détermine en outre une condition environnementale sur la base : (i) de la direction de la sécheresse; et (ii) de la teneur en eau du sol, de la croissance de plantes et de la hauteur de pluie déterminées avec les indicateurs respectifs déterminés. Sur la base de la condition environnementale déterminée, une notification qui indique la condition environnementale est envoyée (140).
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WO2020160605A1 (fr) * 2019-02-04 2020-08-13 The University Of Melbourne Estimation de l'humidité du sol
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