CROP MONITORING AND PROTECTION

Abstract

The present teachings relate to a computerimplemented method comprising: receiving, at a processing means, a crop data indicative of the type of a crop; receiving, at the processing means, a region data indicative of at least one harmful organism that may be present at or around the location of the crop; selecting from a database, using the processing means, a sensor arrangement suitable for selectively genotyping at least one relevant harmful organism; wherein the at least one relevant harmful organism is among the at least one harmful organism, and wherein the selection is performed in response to the crop data and the region data. The present teachings also relate to: an electronic device comprising a processing means configured to execute the method steps disclosed; and, a computer software product.

Claims

1. A computer-implemented method comprising: receiving, at an electronic device, a crop data indicative of the type of a crop; receiving, at the electronic device, a region data indicative of at least one harmful organism that may be present at or around the location of the crop; and selecting from a database, using the electronic device, a sensor arrangement suitable for selectively genotyping at least one relevant harmful organism; wherein the at least one relevant harmful organism is among the at least one harmful organism, and wherein the selection is performed in response to the crop data and the region data.

2. The method according to claim 1, wherein the sensor arrangement is suitable for in-situ genotyping the at least one relevant harmful organism.

3. The method according to claim 1, wherein the method is applied for detecting a DNA-sequence that is indicative of a pesticide resistance, and wherein the sensor arrangement is configured to detect the DNA-sequence that is indicative of the pesticide resistance.

4. The method according to claim 1, wherein the method further comprises outputting, via a human-machine interface functionally coupled to the electronic device, information related to the suitable sensor arrangement.

5. The method according to claim 1, wherein the method further comprises performing, in-situ using the sensor arrangement connected at a sensor interface, genotyping of the at least one relevant harmful organism from an environmental sample obtained from a crop site, and generating a result signal via the sensor arrangement at the sensor interface; and processing the result signal, using the electronic device, for determining whether the one or more of the at least one relevant harmful organism is present in the environmental sample.

6. The method according to claim 5, wherein the method further comprises processing the result signal, using the electronic device, for also determining whether the at least one benign organism is present in the environmental sample.

7. The method according to claim 5, wherein the method further comprises: generating a site result data, using the electronic device, by combining the processed result signal and the location data of the crop site where the environmental sample was collected; storing, in at least one database, the site result data; combining data from a plurality of the site result data to obtain a field map representative of the territorial spread of any of the one or more relevant organisms.

8. The method according to claim 7, wherein the method further comprises determining, by analyzing the field map via the electronic device, at least one site where another measurement or detection is required.

9. The method according to claim 5, wherein the method further comprises: determining using the electronic device, in response to any of the crop data, the region data, and the selected sensor arrangement; a sampling method for collecting the environmental sample.

10. The method according to claim 5, wherein the method further comprises: determining, in response to the result signal or the processed result signal, a treatment method for controlling at least one of the at least one relevant harmful organism present on the crop site.

11. The method according to claim 7, wherein the method further comprises: determining, by analyzing the field map via the electronic device, a treatment method for controlling at least one or more harmful organisms present on the crop site.

12. The method according to claim 11, comprising the step of providing, using the electronic device, the information on the treatment method as a recommendation to the user.

13. The method according to claim 11, wherein the method further comprises: performing a treatment in accordance to the treatment method to control the at least one of the at least one relevant harmful organism detected on the crop site.

14. An electronic device configured to perform the steps of claim 1.

15. A non-transitory computer-readable medium having instructions encoded thereon that, when executed by a processing device, cause the processing device to carry out the method steps of claim 1.

Description

[0196] Example embodiments are described hereinafter with reference to the accompanying drawings.

[0197] FIG. 1 shows a block diagram representation of a portable electronic device deployed in a crop field.

[0198] FIG. 2 shows a spectrum sketch for illustrating the targeted approach of detection

[0199] FIG. 3 shows a flowchart of a sensor arrangement selection method

[0200] FIG. 4 shows a flowchart of a selective genotyping performed after selection

DETAILED DESCRIPTION

[0201] FIG. 1 shows a block diagram 100 illustrating a scenario in which some of the present teachings are applied. In a non-limiting sense, a terrestrial geographical section 101 is shown which may represent a geographical area on land. On the section 101, a crop comprising of plants 103 is planted. The crop, or plants 103, is planted on a field 102, or a geographical area, defined by a boundary 102a-d. On the left side 102d of the field 102 is shown planted another crop which comprises another plants 104 which are different from the plants 103 on the field 102. A detection system 105 pursuant to an aspect of the present teachings is shown located at a site 106a on the field 102. A zoomed-in view 106b is also shown of the site 106a showing an example of the detection system 105 in more detail. The detection system 105 comprises an electronic device 109, which is shown as a handheld device. The detection system 105 also comprises a plurality of sensors or sensor arrangement 112, which for example, may be stored in a kit 110. Any of the sensors 112, for example, a selected sensor or a sensor arrangement 113 can be connected to the electronic device 109 via a sensor interface. The sensor interface can be wireless or wired. The electronic device 109 comprises a human-machine-interface (“HMI”), which here is shown to include a stylus 114 and a display 111 for a user to interact with the device 109. The HMI may even include an audio device such as a loudspeaker. The electronic device 109 also comprises a processing means or computer processor.

[0202] The detection system 105, or more specifically, the electronic device 109 may comprise a geolocation means or a geolocation module for receiving location data. The location data is representative of the geographical location of the site 106a. For example, the device 109 may receive the location data through one or more satellites 118. Geolocation systems such as GPS and GALILIO are available as modules that can be integrated for example in the device 109, and/or even in the sensors 112, 113. Alternatively, or in addition, the location data may be obtained from one or more cellular network 109. Alternatively, or in addition, the location data may be obtained from one or more wireless stations or beacons 120a-b. The system 105 or preferably the electronic device 109 may connect to at least one network, for example, via a data connection 129 with the cellular network 119, and/or via a wireless connection with any of the wireless stations 120a and/or 120b.

[0203] The system 105 or the device 109 also has a functional connection 125 to at least one database 115. In some cases, the database 115 can be at least partially a local database, i.e., comprised within a memory of the system 115 or more specifically that of the device 109. In such cases, the database connection is at least partially internal to the system 105 or the device 106. In some cases, the database 115 can be at least partially a remote database, i.e., located at another location as compared to the location 106a of the system 105. In such cases, the database connection 125 is at least partially external to the system 105. Hence the database connection 125 may be established via the cellular network 119 and/or via one or more wireless station 102a, b. The at least one database 115 may even at least partially be on a cloud service 116. In some cases, the external database connection 125 can be established away from the test location 106a. This is relevant when for example, a cellular network or a wireless connection cannot be made from the test site. In such cases, the database 115 can be an internal database that can be synchronized with a remote database when a network connection is available. The synchronization can either be made directly via a cellular connection 129 and/or a wireless network 120 when it is available, or the synchronization can be done indirectly by a connection 127 to a server 117. In some cases, the server connection 127 is a wired connection between the device 109 and the server 117, while in other cases it can even be a wireless connection, for example through the data connection 129 or wireless 120. In some cases, the remote database 115 may at least partially reside on the server 117, or even the server 117 may provide access to the remote database 115 that may be on another location.

[0204] When the system 105 is deployed at a crop site, for example the site 106a, the processing means receives crop data which is indicative of the type of crop, i.e., the type of plants 103. The crop data may either be entered by the user, for example using the HMI, and/or it may be acquired automatically via a crop sensor, and/or even via previous measurements. The crop sensor may, for example, be a part of an image recognition system. The processing means also receives region data which is indicative of at least one harmful organism that may be present at or around the location of the crop 103. For this purpose, the region data may provide information on which organisms are or have been present within a certain distance of the field 102. In some cases, the region data may be at least partially obtained from the server 117, and/or the database 115, and or the cellular network 119, and/or the wireless network 120. The region data may even comprise the location data. In some cases, the region data is available from one or more public databases or an internet service. The region data may even comprise weather data, for example intensity and direction of wind 131.

[0205] If an infected area 140 lies between the path of the wind 131 upstream of the field 102, then there can be a higher probability that harmful organisms from infected plants 130 have been transported to at least a portion of the crop 103. The certain distance of the field 102, which is to be evaluated for relevance of detection of the harmful organism may thus be adapted according to the prevailing conditions, such as the wind 131 or the history thereof.

[0206] In some cases, the other crop 104 may even have been infected by another harmful organism. In such cases, the region data may also comprise information related to the another harmful organism. It is however possible that the another harmful organism does not infect the crop plants 103. It can therefore be useless to treat the crop 103 for the another harmful organism that only affects the other crop 104, or more specifically, that does not affect the crop 103. Such an organism can be considered an irrelevant harmful organism for the crop 103.

[0207] Similarly, there may be further organisms that are harmful to the crop 103, however such organisms have not been detected within the certain distance of the field 102 or even the site 106a. It thus may not be sensible to detect such further organisms either. Such organisms can also be considered irrelevant harmful organisms for the crop 103.

[0208] Pursuant to the present teachings, by combining the crop data with the region data, the processing means is thus able to automatically determine the harmful organisms of interest, or at least one relevant harmful organism if both the crop data and the region data indicate the same harmful organism. It will be appreciated that if the at least one harmful organism indicated by the region data does not correspond to those organisms that can affect the crop, none of the at least one harmful organism will be the relevant harmful organism, hence no detection will be required to be performed. Similarly, if all of the at least one harmful organism indicated by the region data correspond to the respective organisms that can affect the crop, all of the at least one harmful organisms will be the relevant harmful organisms, and hence be recommended for detection. So, when at least one relevant harmful organism is indicated by combining the crop data and the region data, the processing means can thus automatically select a sensor arrangement comprising one or more sensors, each of which sensors are targeted to detect a specific individual organism of interest different from the organisms that other sensors in the sensor arrangement can detect. It can thus be made possible to determine a sensor arrangement that is optimized to detect the organisms of interest. or relevant organisms. The user is neither required to be an expert in organism, crops, or their detection.

[0209] When the selected sensor arrangement 113 is used to perform the detection, the device 109 can guide the user via HMI through the sampling process. The sampling process may differ dependent upon the crop and/or the relevant organism to be detected. The sampling process may even depend upon the selected sensor arrangement 113. The user is hence not required to be an expert or a biologist. It will also be appreciated that the method explained above is also suitable to be implemented as an autonomous process, for example, the system 105 may be a robotic system that after automatically selecting the sensor arrangement 113, interfaces the arrangement 113 to the processing unit, acquires one or more environmental samples from the site 106a, selectively genotypes the environmental sample, generates a result signal via the sensor arrangement, processes the result signal using the processing means to determine whether the one or more harmful organisms are present in the environmental sample obtained from the crop site 106a. The robotic system may then pick another site for detection at the another site. The present teachings are thus suitable both for detection performed by a portable device operated by a user, or by an at least partially autonomous process.

[0210] In response to a positive detection of the harmful organism which has been detected at a site, the processing means may determine a suitable treatment for controlling the harmful organism. If more than one relevant harmful organisms are detected at the site, the processing means may determine a treatment that is optimized for treating more than one organism harmful organisms on the site. This can help minimize the amount and number or active ingredients for controlling the organisms rather than treating each harmful organism separately. Such an optimization of treatment can also be a significant advantage for an inexperienced user. This can thus have cost and environmental advantages. The treatment can for example be a spray program than involved spraying the infected plants with a specific active ingredient or mixture thereof. The processing means can thus select a treatment that is suited according to the crop and organism. Furthermore, the processing means can even select the appropriate treatment that is optimized according to the weather and/or season. The system 105 or the device 109 can even guide the user through the treatment process, which may include any of the: name of the suitable treatment product, time of start of the treatment using the product, dose rate, volume of application, number of applications of the product or even a combination of treatment products dependent upon the one or more result signals from the site or the neighboring or adjacent sites. Rather than risking too much or too scarce application of the product, the user can be guided through an optimized application according to the result data from one or more result signals. Again, it will be appreciated that rather than guiding the user, an autonomous treatment system can also be used that is operated similar to as outlined above, in response to the result data, and even location data. The treatment system may either be the same system 105, or it may be a separate system such as at least one drone 166. The drone 166 may also comprise a location detection means such as a geolocation module that acquires a location signal 138 from one or more satellites 118. The drone may even obtain location data via the detection system 105, or via cellular network 119 or wireless network 120, as was explained earlier in context of the system 105.

[0211] In FIG. 1 it is also shown an infected portion 150 of the crop plants 103. The portion 150 may have become infected by pathogens which were borne by the wind 131 from the infected area 140. Such portion 150 may, for example, be determined/detected by combining the result signals from a plurality of crop sites. By further combining the result signal with the location data of the respective sites, a map representing geographical distribution of the one or more relevant organisms may be obtained. The infected portion 150 may even be called a hot-zone of the organism that is present in the portion 150. Such a map may be useful in targeted treatment of the infected plants within, and in some cases also around, the hot-zone 150. Subsequent detections can be made on the same or similar sites on the field 102 for tracking the effectiveness of the treatment and/or spread. Further treatments can thus be optimized. This can also have cost and ecological benefits by avoiding treatment in response to the harmful organism being detected controlled. These benefits are synergistically linked by making it possible to perform a targeted detection in a quick way such that frequent and higher density measurements can be afforded.

[0212] In some cases, the system 105 automatically places an order of one or more sensors in the selected sensor arrangement should the one or more sensors not be available in the system 105, or are expected to be exhausted for detections that are planned in the future. In some cases, the system 105 can automatically place order of the treatment product as required to control the detected one or more relevant harmful organism.

[0213] FIG. 2 shows a symbolic representation 200 of the targeted approach of selection and for detection of organisms as proposed in the present teachings. Along the entire spectrum of organism genome 250, different organisms can lie in different places in the spectrum. The organism spectrum can include organisms that can affect the crop 103, for example, the first group 201 of organisms and a second group 202 of organisms. In addition, there can be a third group 203 of organisms that cannot affect the crop 103. If the region data indicates that organisms 201 and 203 may be present at or around the location of the crop 103 or the field 102, the processing means can combine the crop data with the region data and as a result automatically exclude the organisms 203 from the scope of detection. As a result, the processing means will pick only the first group of organisms 201 for detection. and thus define them as relevant harmful organisms. Accordingly, the second group of organisms 202 also be excluded, as irrelevant organisms, from the scope of detection as these organisms have are not present at or around the location of the crop. Accordingly, the processing means, in response to the crop data and the region data, selects from the database 115 the sensor arrangement 113 suitable for selectively genotyping the relevant harmful organisms 201a— d. It will be appreciated that the term group may even mean a single organism and does not need to be a grouping of organisms. Accordingly, each specific organism may also be checked for relevance by the processing means for defining the scope of detection. The term scope of detection means the organisms that have been selected for detection by the processing means by combining the crop data with the region data. The sensor arrangement 113 is thus selected such that it is able to selectively genotype these selected organisms.

[0214] FIG. 3 shows a flowchart 300 for selecting a sensor arrangement. In an initial step 301, crop data is received at the processing means. The crop data indicative of the type of the crop. For example, crop data indicates that the crop plants 103 are wheat. The crop data may even further specify the type of crop, for example, that the crop plants 103 are of the type Durum wheat. Then is a subsequent step 302, region data is received at the processing unit. The region data is indicative of at least one harmful organism that may be present at or around the location 106a of the crop. In another subsequent step 303, the processing means selects a sensor arrangement 113 from a database 115 in response to the crop data and the region data. The sensor arrangement is suitable for selectively genotyping the at least one relevant harmful organism. It will be appreciated that the sequence of the initial step 301 and the subsequent step 302 may be interchanged. The processing means may still be able to select the sensor arrangement 113 from the database 115 by combining the crop data and the region data. In an optional subsequent step 304, information related to the selected sensor arrangement 113 is outputted via an HMI such as a graphic display.

[0215] The flowchart 300 can be implemented on a suitable electronic device 109 comprising the processing means and a sensor interface for functionally connecting the sensor arrangement 113 to the processing means. Accordingly, the detection system 105 may be realized.

[0216] FIG. 4 shows another flowchart 400, which may follow, for example the steps 301-303 of, the first flowchart 300. Step 304 may optionally be present as well. In a first next step 401, selective genotyping of an environmental sample is performed using the sensor arrangement 113 functionally connected at the sensor interface. The environmental sample obtained from the crop site 106a. A result signal is generated at the sensor interface by performing the selective genotyping via the sensor arrangement 113. In a further next step 402, the result signal is processed by the processing unit for determining whether the one or more relevant harmful organisms are present in the environmental sample. The electronic device 109 can thus detect if any of the one or more relevant harmful organisms are present or not on the crop site 106a. As an optional further next step 403, the processing means also determines from the result signal whether the at least one benign organism is present in the environmental sample. The determination can be in the form of a processed result signal generated by the processing means, the processed result signal indicating the presence of the one or more organisms in the environmental sample. As a further optional step, either with or without the step 403, the processing means generates a site result data by combining the processed result signal and the location data of the crop site 106a where the environmental sample was collected. Also optionally, the processed result signals collected from detections performed on a plurality of sites can be used to generate a map representative of the spread of any of the one or more relevant organisms over the geographical area covered by the plurality of sites. In further optional steps, determination can be made for suitable sampling steps, treatment and further measurements. Optionally, the user can be guided through each or all of the steps, or further optionally, they may be implemented in an autonomous manner.

[0217] Various examples have been disclosed above for a method suitable for on-site detection, an electronic device for on-site detection, and a computer software product implementing any of the relevant method steps herein disclosed. Those skilled in the art will understand however that changes and modifications may be made to those examples without departing from the spirit and scope of the accompanying claims and their equivalents. It will further be appreciated that aspects from the method and product embodiments discussed herein may be freely combined.