Patent ID: 12236373

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Reference is now made toFIG.1, which is a simplified schematic illustration of a crop management system10constructed and operative in accordance with a preferred embodiment of the present invention.

As seen inFIG.1, the crop management system10comprises at least one crop monitoring subsystem comprising at least one crop sensor assembly20for sensing at least one crop growth parameter in a predetermined region of growing plants. Preferably, the predetermined region extends up to a radius of 400 meters from the sensor assembly20. Preferably, the sensor assembly20comprises a multi-spectral sensor assembly covering visible and non-visible spectral ranges, preferably extending across the range of 400-1000 nm and 8000-14,000 nm. A preferred embodiment of a sensor assembly20is a FLIR700, commercially available from Flir Systems, Inc. of Wilsonville, OR, USA, which has a thermal deviation resolution of 0.05 degrees Celsius. Another preferred embodiment of a crop sensor assembly20is an Altum™ sensor, commercially available from Micasense, Inc of Seattle, WA, USA.

The sensor assembly20is preferably mounted onto a raised stabilized platform assembly30, which preferably provides 360 degree rotation about a vertical axis. A preferred embodiment of a stabilized platform assembly30is a CINEMA PRO, commercially available from Gyro-Stabilized Systems LLC of Nevada City, CA, USA.

Stabilized platform assembly30is preferably fixedly mounted onto a raisable platform40and is maintained at a height of approximately 30 meters above the ground. A preferred embodiment of a raisable platform40is a QEAM-HD, commercially available from the Will-Burt Company of Orrville, OH, USA, or a BOSS100′, commercially available from Bossltg, Inc. of Baton Rouge, LA, USA, which are portable telescopic raised platforms, or portable masts, commercially available from Total Mast Solutions Ltd of Leicestershire, UK.

An output of the sensor assembly20is preferably supplied, via the cloud or alternatively in any other manner, to an analysis and reporting engine50, such as a server, which analyzes the output of the sensor assembly and provides an output indication of plant growth anomalies within the predetermined region. An output indication of such anomalies preferably includes an indication of variation in temperature changes over time between adjacent plants, which exceeds a predetermined threshold.

Preferably the output indication is communicated to a hand-held communicator60, such as a smartphone having installed thereon a suitable app, which enables it to display alerts as to anomalies including an indication of the anomaly and its location, preferably in GIS coordinates. The smartphone may be carried by a grower who is thus enabled to walk directly to the location of the anomaly and examine the plants.

FIG.1illustrates three examples of temperature change anomalies that can be reported in near real time:

At A, it is seen that among three adjacent grape vines, one of them has a temperature change of 0.5 degrees Celsius between 6:00 am and 6:40 am, while the two vines adjacent thereto have temperature changes of 0.2 degrees Celsius or less. This may be indicative of a watering problem or an incipient disease. An output indication indicating the anomalous temperature change and the location of the plant in question appears as an alert on the smartphone60of the grower.

At B, it is seen that two adjacent rows of grape vines, designated R1and R2, have a temperature change of 1.5 degrees Celsius between 11:10 am and 1:50 pm, while another two adjacent rows of grape vines, designated R3and R4, have a temperature change of 0.5 degrees Celsius between 11:10 am and 1:50 pm. This may be indicative of a watering problem or an incipient disease. An output indication indicating the anomalous temperature change and the location of the plant in question appears as an alert on the smartphone60of the grower.

At C, it is seen that alternative rows of grape vine, designated P1, P3and P5, have a temperature change of 1.5 degrees Celsius between 11:10 am and 13:50 pm, while the rows in between them, designated P2and P4, have a temperature change of 0.5 degrees Celsius between 11:10 am and 13:50 pm. This may be indicative of a watering problem. An output indication indicating the anomalous temperature change and the location of the plant in question appears as an alert on the smartphone60of the grower.

In another example, the temperature of rows of corn is monitored vis-á-vis the ambient temperature. It is known that healthy rows of corn have a temperature generally below ambient temperature. Monitoring of a temperature of the rows of corn where the temperature is less than a predetermined threshold, typically 1.0-1.5 degree Celsius, below the ambient temperature may be indicative of a watering problem or an incipient disease. An output indication indicating the anomalous temperature and the location of the plant in question may appear as an alert on the smartphone60of the grower.

In a further example, the health of avocado or mango trees is monitored by measuring the temperature of the buds and leaves vis-à-vis the ambient temperature and surrounding trees. Monitoring of a temperature of the buds and leaves where the temperature is less than a predetermined threshold, typically 1.0-1.5 degrees Celsius, below the ambient temperature or where the temperature differs from the surrounding trees by more than a predetermined threshold may be indicative of a watering problem or an incipient disease or an incipient infestation. An output indication indicating the anomalous temperature and the location of the plant in question may appear as an alert on the smartphone60of the grower.

It is appreciated that the analysis and reporting engine50may include artificial intelligence analytics for suggesting causes of the anomalies and recommending steps for ameliorating same.

For example, as noted above, different temperature pattern changes may be indicative of different problems. Problems may be one or more of system problems, such as dehydration, which may be indicative of a problem in the watering system, environmental problems, such as soil salinity or soil nutrient problems, biological problems, such as air borne or soil borne fungal attacks, and infestation problems.

Reference is now made toFIG.2, which is a simplified schematic illustration of a crop management system100constructed and operative in accordance with a preferred embodiment of the present invention.

As seen inFIG.2, the crop management system100comprises at least one crop monitoring subsystem comprising at least one crop sensor assembly102for sensing at least one crop growth parameter in a predetermined region. Preferably, the predetermined region extends up to a radius of 400 meters from the sensor assembly102. Preferably, the sensor assembly102comprises a multi-spectral sensor assembly covering visible and non-visible spectral ranges, preferably extending across the range of 400-1000 nm and 8000-14,000 nm. A preferred embodiment of a sensor assembly is a FLIR700, commercially available from Flir® Systems, Inc. of Wilsonville, OR, USA, which has a thermal deviation resolution of 0.05 degrees Celsius. Another preferred embodiment of a sensor assembly102is an Altum™ sensor, commercially available from Micasense®, Inc of Seattle, WA, USA.

The sensor assembly102may be movably mounted on a fixed platform104during operation by aerial vehicle, such as a drone108, as indicated at reference numeral109, or may be mounted on drone108, as indicated at reference numeral110. The crop monitoring subsystem may also include a sample collector112, which may be mounted onto drone108.FIG.2shows a plurality of fixed elevated platforms104distributed in a crop growing region.

Crop management system100also preferably includes at least one field monitoring subsystem comprising at least one field sensor assembly114for sensing at least one field parameter in the predetermined region. One example of a field sensor assembly114is a scanning radar assembly, for detection of human or animal intruders, vehicles and rain and for monitoring activity of drones108, which preferably form part of the crop management system100. Field sensor assembly114may be assembled together with crop sensor assembly102. Additionally, field sensor assembly114and crop sensor assembly102may be mounted on the same fixed elevated platforms104.

Additionally, crop management system100preferably includes at least one environmental monitoring subsystem, such as a weather station, as indicated at reference numeral116, which provides data such as ambient temperature, humidity, wind speed, solar radiation, barometric pressure, as well as soil probes which provide data regarding soil temperature as well as chemical and biological analysis of the soil.

Crop management system100preferably also includes an analysis engine120, receiving an output from at least one of the at least one crop monitoring subsystem and the at least one field monitoring subsystem and being operative to identify at least one anomaly in at least one of the parameters.

Examples of anomalies which can be detected and preferably ameliorated by the crop management system preferably include:Crop growth anomalies, including fungal diseases, such as mildew, bacterial diseases, such as fire blight in pears, viral diseases, such as tomato yellow leaf virus (TYLV), insect infestation, such as white fly in tomatoes, and nematodes;Field anomalies, such as under irrigation, over irrigation, under fertilization, birds and groundhogs; andEnvironmental anomalies, such as frost, extreme high temperature and extreme high humidity.

The analysis engine120is preferably remotely located from the field being managed and preferably resides on a server122which may communicate wirelessly with the remainder of the crop management system. It is appreciated that crop sensor assembly102and field sensor assembly114may also be operative to perform analysis of the parameters sensed and to detect anomalies therein.

It is appreciated that analysis engine120may include multiple different methodologies for detecting anomalies, including correlating data received from multiple ones of crop sensor assemblies102and field sensor assemblies114at a given time, correlating data from a single one of crop sensor assemblies102and field sensor assemblies114over time and correlating data from multiple ones of crop sensor assemblies102and field sensor assemblies114over time. It is also appreciate that once analysis engine120has determined that an anomaly exists, that analysis engine120may employ a variety of analysis tools, including artificial intelligence driven tools, for defining the nature of the anomaly and the appropriate amelioration process. It is further appreciated that the analysis engine120may correlate data received from multiple ones of crop sensor assemblies102and field sensor assemblies114located in the same field or in multiple fields.

Preferably, crop management system100further includes an anomaly locator operative to provide an output indication of spatial coordinates of at least one location of the at least one anomaly. The anomaly locator is preferably embodied in one or more encoders associated with at least one of the at least one crop sensor assembly102and the at least one field sensor assembly114as well as GPS coordinate indicators associated with drones108.

Preferably, the crop management system100also includes various amelioration subsystems for amelioration of anomalies, such as those described above. One example of an amelioration subsystem is a spraying or distribution assembly, such as that indicated at reference numeral130, which can be mounted on drone108and used to deliver fungicides, bactericides, insecticides or other materials for dealing with anomalies, such as distressed crops. Another example of an amelioration subsystem is a bird harassment system, such as that indicated at reference numeral132.

The embodiment illustrated inFIG.2shows a plurality of platforms104in a region to be monitored, some of which may have sensor assemblies, such as crop sensor assembly102and field sensor assembly114, mounted thereon and are designated by reference number140, and some of which do not have sensor assemblies mounted thereon and are designated by reference number150. As described further hereinbelow with reference toFIG.4, sensor assemblies may be transportable, such as by drones108.

Reference is now made toFIG.3, which illustrates a system200for monitoring plant growth constructed and operative in accordance with a preferred embodiment of the present invention. In accordance with a preferred embodiment, as seen inFIG.3, the system includes at least one, and preferably multiple, elevated monitoring platforms201which are fixed or removably mountable at predetermined or selectable locations in or adjacent to fields in which crops are growing. The crops may be any suitable crops, such as field crops and fruit trees. The platforms201may be located between fields in which different crops are growing and enable real time or near real time monitoring of multiple different crops.

Elevated monitoring platforms201may be mounted on pre-positioned base elements202, but do not necessarily require separate base elements. The base elements may include existing posts which are used for lighting, irrigation, power transmission or communications. Platforms201preferably each include solar powered, electricity generating panels204and wireless communication antennas206as well as a payload dock208. Additionally, each of platforms201preferably includes a chargeable battery (not shown) providing backup power.

In accordance with a preferred embodiment of the present invention, monitoring platforms201may be removably insertable into base element202such that a monitoring platform201may be removed from one base element202and inserted into a different base element202, as described further hereinbelow with reference toFIG.8.

In accordance with a preferred embodiment of the present invention, at least one crop monitoring payload assembly210is removably mounted onto each of the elevated platforms201, preferably by a drone211, as described hereinbelow with reference toFIG.4. The crop monitoring payload assembly210preferably includes a sensor assembly212for sensing characteristics of crops growing in a vicinity of crop monitoring payload assembly210. The sensor assembly212preferably includes at least one sensor and at least one imager, and may additionally include at least one functionality providing assembly. The at least one sensor preferably includes some or all of the following sensors: a temperature sensor, a wind sensor, an IR sensor, an optical sensor, a UV sensor, a humidity sensor, a biological sensor and a chemical sensor. The at least one imager, preferably includes one of the following imagers: a visual imager, a thermal imager and a multispectral imager. The at least one functionality providing assembly is preferably operative to provide one or more of the following functionalities: bird harassment functionality, security functionality, such as radar, relay station functionality and drone battery charging functionality.

In a preferred embodiment, the payload assembly210includes focusing and aiming apparatus, similar to that found in crop sensor assembly102, enabling the sensor assembly212to sense characteristics of a given section of a field of growing plants, as well as azimuthal and tilt sensing apparatus, which enables the payload assembly210to pinpoint a given area in a field having an anomaly, such as insufficient watering or pest infestation. Preferably, the spatial resolution of the payload assembly is 1 meter×1 meter, more preferably the spatial resolution of the payload assembly is 0.5 meters×0.5 meters, and most preferably, the resolution of the payload assembly is 0.05 meters×0.05 meters.

Preferably, a single payload assembly210is able to monitor a crop growing area of 10 hectares. More preferably, the payload assembly210is able to monitor a crop growing area of 30 hectares. Most preferably, the payload assembly is able to monitor a crop growing area of 50 hectares.

Preferably, system200also includes an analysis engine220, receiving an output from the sensor assembly212and being operative to identify at least one anomaly in characteristics of the growing crops, within the monitoring area of the payload assembly210. Some examples of anomalies which can be identified using the system200include those described hereinabove with reference toFIG.2.

System200also preferably includes an anomaly locator which receives outputs from the analysis engine220and provides an output indication of one or more sensed anomalies in the growing crops as well as the spatial coordinates of at least one location of the at least one anomaly. The anomaly locator preferably employs at least one of an encoder and GPS data.

It is appreciated that analysis engine220is preferably remotely located from the field being managed and preferably resides on a server230which may communicate wirelessly with the remainder of the crop management system. It is appreciated that sensor assembly212may also be operative to perform analysis of the parameters sensed and to detect anomalies therein.

Reference is now made toFIG.4, which is a simplified pictorial illustration of one example of docking of payload210on a pre-positioned elevated platform201in the system200for monitoring plant growth of the type shown inFIG.3. As seen inFIG.4, a drone240may be employed for transporting a selected one of, preferably, a plurality of, selectable different payload assemblies210to a selected one of multiple elevated monitoring platforms201and for docking the selected payload assembly210onto the selected elevated monitoring platform201. The docking apparatus has a mechanical interface and an electrical interface. The mechanical interface guides the payload into the required position in such a way that the payload is secured and connected to the electrical connectors forming the electrical interface. The electrical connectors preferably provide power to the system and may also provide connection to a communication interface for communicating with the other components of system200, including the analysis engine220and the anomaly locator. Alternatively, the selected payload assembly210includes a communication interface for communicating with the other components of system200. The payload dock208may also provide a housing for payload assembly210based on environmental requirements. Payload dock208preferably receives power from solar powered, electricity generating panel204that is installed on elevated monitoring platform201. Backup power is preferably provided by the chargeable battery, which preferably is charged during the day time and provides power for night operation.

Reference is now made toFIG.5, which is a simplified pictorial illustration of an example of operation of payload assembly210in system200for monitoring plant growth of the type shown inFIGS.3and4. As seen inFIG.5, the payload assembly210, preferably scans a region, such as field or a portion thereof, and upon sensing an anomaly in a portion of the region, here, for example, a fungal disease infestation, automatically communicates an image illustrating the anomaly as well as the coordinates of the infested area, growth parameters and field parameters, preferably in real time or near real time.

In a preferred embodiment, the payload assembly210scans the entire field continuously, during both day and night, and employs different algorithms based on the operating parameters to sense anomalies. In one example, the payload assembly210compares the sensed thermal characteristics with historical information of average thermal measurement over time of different portions of the field, while taking into consideration growth parameters and field parameters.

Upon sensing an anomaly, as described above, the payload assembly210preferably communicates the information relating thereto, preferably via a wireless communication link, via any suitable medium, such as a line of sight, RF, satellite, internet or other link, to a computerized amelioration center245, as described hereinbelow with reference toFIG.6A, which may provide real time or near real time reports or amelioration, such as spraying. The communication may be via the cloud or a direct computer to computer or computer to human link. Alternatively, the communication to computerized amelioration center245may be via analysis engine220.

FIG.6Ashows an example of fully automatic amelioration, wherein the sensed anomaly is automatically ameliorated, as by spraying distressed plants at a location specified by the anomaly locator, using a drone-mounted computer controlled sprayer assembly250mounted on a drone260.

FIG.6Billustrates partially automatic amelioration employing the system ofFIGS.2-5which employs a human operator to control or approve the amelioration.

Reference is now made toFIG.7, which is a simplified illustration of the operation of a system for monitoring plant growth of the type shown in any ofFIGS.2-6Bwith a variety of different crops, such as peppers, carrots, corn and potatoes. It is seen that various types of sensors or payloads may be employed, such as a thermal sensor300, a multi-spectral sensor310or a radar sensor312. As indicated by arrows inFIG.7, sensors may be operative to scan in a complete 360° rotation, either in a single continuous direction or in a back a forth direction, or in a back and forth direction covering any portion thereof, such as a 120° arc, as shown in the illustrated embodiment.

Reference is now made toFIG.8, which is a simplified illustration of positioning and repositioning of elevated supports in a system for monitoring plant growth of the type shown in any ofFIGS.2-7. It is appreciated that a drone320, preferably having a high lifting capacity, may be employed for moving platforms201from place to place as needed at various stages of the growth of various crops in various seasons.

It will be appreciated by persons skilled in the art that the present invention is not limited by what has been shown and described hereinabove. Rather the present invention includes both combinations and subcombinations of various features described hereinabove and which are not in the prior art.