WO2024057015A1

WO2024057015A1 - Soil analysis system and method

Info

Publication number: WO2024057015A1
Application number: PCT/GB2023/052366
Authority: WO
Inventors: Simon Shepherd; Amy SCOTT; Frank COLWILL
Original assignee: Enableiot Limited
Priority date: 2022-09-14
Filing date: 2023-09-13
Publication date: 2024-03-21
Also published as: GB202213505D0; GB2617646A

Abstract

A soil analysis system and method is provided, the system including: a probe configured to determine one or more soil environment characteristics and to transmit the or each soil environment characteristic to a remote device; and the remote device, wherein the remote device is configured to determine a soil organic carbon content using the one or more received soil environment characteristics.

Description

SOIL ANALYSIS SYSTEM AND METHOD

FIELD

The disclosed technology includes systems and methods for soil analysis. For example, the disclosed technology may be used to determine soil organic carbon content. This may, in turn, allow the impact of a carbon offset project to be measured (e.g. for use in the determination of carbon credits) and/or enable more sustainable land management.

BACKGROUND

Climate change is widely regarded as one of the greatest challenges facing humanity in the 21^st century. The impacts of climate change are extensive, including increased global temperatures, increasing water scarcity, and decreasing food security.

Soil-based carbon offsetting, sometimes known as soil carbon sequestration, is one technique that can be used to mitigate the effects of climate change. This type of carbon offsetting generally includes increasing the level of carbon contained in soil, which may counteract the effect of greenhouse gas emissions, for example.

Furthermore, improving soil quality is an important step towards improving global food security. There is also a need, generally, for more sustainable land management around the world.

There is a need, therefore, for soil analysis techniques that provide accurate information regarding soil properties.

BRIEF DESCRIPTION OF THE INVENTION

The disclosed technology includes a soil analysis system, including: a probe configured to determine one or more soil environment characteristics and to transmit the or each soil environment characteristic to a remote device; and the remote device, wherein the remote device is configured to determine a soil organic carbon content using the one or more received soil environment characteristics.

The probe may be a soil probe configured to be at least partially inserted into soil to determine the one or more soil environment characteristics.

The remote device may be configured to determine the soil organic carbon content based at least partly on a set of training data. The remote device may be configured to determine the soil organic carbon content using a stochastic global search optimization algorithm.

The remote device may be configured to determine the soil organic carbon content using a simulated annealing technique.

The system may further include a user device, and the remote device may be configured to output the determined soil organic carbon content to the user device.

The one or more soil environment characteristics may include an electrical characteristic of the soil.

The one or more soil environment characteristics may include at least one of: precipitation amount, soil temperature, soil capacitance, soil resistance, and soil conductance.

The probe may be configured to determine and transmit the one or more soil environment characteristics at least once per hour.

The system may include a plurality of the probes, each configured to determine one or more soil environment characteristics and to transmit the or each soil environment characteristic to the remote device, and the remote device may be configured to use the one or more soil environment characteristics received from each probe to determine the soil organic carbon content.

Also provided is a soil analysis method, the method including: determining, using a probe, one or more soil environment characteristics; transmitting the or each soil environment characteristic from the probe to a remote device; and determining, using the remote device, a soil organic carbon content using the one or more soil environment characteristics.

The probe may be a soil probe at least partially inserted into soil.

The soil organic carbon content may be determined based at least partly on a set of training data.

The soil organic carbon content may be determined using a stochastic global search optimization algorithm.

The soil organic carbon content may be determined using a simulated annealing technique.

The method may further include outputting the determined soil organic carbon content to a user device. The one or more soil environment characteristics may include an electrical characteristic of the soil.

The method may include: determining, using a plurality of the probes, the one or more soil environment characteristics; and transmitting the or each soil environment characteristic from each probe to the remote device, wherein the remote device is configured to use the one or more soil environment characteristics received from each probe to determine the soil organic carbon content.

BRIEF DESCRIPTION OF THE FIGURES

In order that the present disclosure may be more readily understood, preferable embodiments thereof will now be described, by way of example only, with reference to the accompanying drawings, in which:

Fig. 1 is a schematic illustration of a system, embodying the present disclosure;

Fig. 2 is a schematic illustration of a probe, embodying the present disclosure;

Fig. 3 is a schematic illustration of a remote device, embodying the present disclosure; and Fig. 4 is a schematic illustration of a user device, embodying the present disclosure.

DETAILED DESCRIPTION OF THE DISCLOSURE

The disclosed technology includes a system 1 , which may be a soil analysis system 1 . The system 1 may be used to provide information regarding soil properties, which may be measured directly or may be determined indirectly (e.g. using measurements of related soil characteristics). For example, the system 1 may be used to determine soil organic carbon content (see below), which may enable a user to manage an area of land effectively and sustainably.

The system 1 may include a probe 10, which may be a soil probe 10. The probe 10 may be configured to determine one or more characteristics associated with soil, which may be referred to as soil environment characteristics. The probe 10 may, therefore, be configured for at least partial insertion into soil. The probe 10 may be configured for at least partial insertion into a ground medium. The soil may generally be any type of soil, including for example a clay soil, a sand soil, a silt soil, a loam soil, a peat soil, and/or a chalk soil. The probe 10 may, therefore, be configured to determine one or more characteristics associated with an upper or surface layer of earth, which may define a ground surface. One or more of the soil environment characteristics may be determined by a part of the probe 10 (e.g. a sensor 100) that is located underground in use. In some versions, one or more soil environment characteristics may be determined by a part of the probe 10 (e.g. a sensor 100) that is located underground in use, and one or more other soil environment characteristics may be determined by a part of the probe 10 (e.g. a sensor 100) that is located aboveground in use.

The probe 10 may include at least one sensor 100, and may include a plurality of sensors 100. The or each sensor 100 may be configured to determine a corresponding soil environment characteristic. The probe 10 may, therefore, be configured to determine at least one soil environment characteristic.

The soil environment characteristics may include: solar radiation level, light spectrum, aboveground NOx (nitrogen oxides) level, soil NOx level, aboveground CO2 level, soil CO2 level, aboveground volatile gases level, soil volatile gases level, aboveground noise level, soil noise level, aboveground vibration level, soil vibration level, soil colour, location, soil conductance, soil conductivity, soil resistance, soil capacitance, soil moisture, soil pH, soil temperature, air temperature, air humidity, air pressure, and/or precipitation level.

The soil environment characteristics may, therefore, include soil electrical characteristics. The probe may be configured to determine an electrical characteristic of the soil.

Accordingly, the probe 10 may include: a solar radiation level sensor 101 , a light spectrum sensor 102, an aboveground NOx (nitrogen oxides) level sensor 103, a soil NOx level sensor 104, an aboveground CO2 level sensor 105, a soil CO2 level sensor 106, an aboveground volatile gases level sensor 107, a soil volatile gases level sensor 108, an aboveground noise level sensor 109, a soil noise level sensor 110, an aboveground vibration level sensor 111 , a soil vibration level sensor 112, a soil colour sensor 113, a location sensor 114, a soil conductance sensor 115, a soil conductivity sensor 116, a soil resistance sensor 117, a soil capacitance sensor 118, a soil moisture sensor 119, a soil pH sensor 120, a soil temperature sensor 121 , an air temperature sensor 122, an air humidity sensor 123, an air pressure sensor 124, and/or a precipitation level sensor 125.

In some versions at least one sensor 100 of the probe 10 may sense a plurality of soil environment characteristics (e.g. may be a multi-purpose sensor 100). The total number of sensors required to determine a chosen number of soil environment characteristics may, therefore, be minimised, and may in particular be less than the number of soil environment characteristics to be determined. The probe 10 may include a power source 151 . The power source 151 may include one or more cells or batteries, such as a lithium-ion cell or battery, for example. The power source 151 may include one or more capacitors. The power source 151 may include one or more solar cells (which may be operably coupled to the or each cell, battery, or capacitor). The power source 151 may include one or more wind turbines (which may be operably coupled to the or each cell, battery, or capacitor). The power source 151 may be configured to power the operation of the probe 10 (e.g. to power one or more sensors or other components of the probe 10 as described herein).

In some versions a single power source 151 may provide power to a plurality of probes 10. For example, a solar cell or wind turbine may be operably coupled to a plurality of probes 10.

The probe 10 may include a transmitter 152. The transmitter 152 may be operably coupled to the power source 151. The transmitter 152 may be configured to transmit data from the probe 10 (e.g. to transmit soil environment characteristics) to a remote location (e.g. to a remote device 20). The transmitter 152 may be configured to transmit the data (e.g. soil environment characteristics) using a network 40, such as a cellular or mobile network 40 (e.g. a 2G, 3G, 4G, 5G, 6G etc. mobile network 40). The transmitter 152 may be configured to transmit the data (e.g. soil environment characteristics) directly to the remote location (e.g. directly to the remote device 20) in some versions. The transmitter 152 may, therefore, be configured to transmit the data (e.g. the soil environment characteristics) using a wireless communications link, such as a Bluetooth, Bluetooth Low Energy (BLE), Zigbee, or similar communications link.

The transmitter 152 may transmit the soil environment characteristics and may additionally or alternatively transmit other data such as a power level, a probe identifier, a time, and/or one or more alerts. The power level may include a state of charge (e.g. for probes 10 including a battery 151). The probe identifier may include a unique identifier to identify the transmitting probe 10. Each probe 10 may have a unique probe identifier. Accordingly, the probe identifier may identify from which probe 10 a particular message or data has been transmitted (e.g. in systems 1 including a plurality of probes 10). The alert may include an alert that one or more sensors 100 are inoperative or are producing inaccurate measurements, for example. The alert may include a low power alert, which may for example indicate that a battery 151 of the probe 10 needs replacing.

The probe 10 may include a receiver 153. The receiver 153 may be operably coupled to the power source 151. The receiver 153 may be configured to receive data, such as an instruction or request, from the remote location (e.g. the remote device 20). The receiver 153 may be configured to operate using a network 40, such as a cellular or mobile network 40 (e.g. a 2G, 3G, 4G, 5G, 6G etc. mobile network 40). The receiver 153 may be configured to receive data directly from the remote location (e.g. directly from the remote device 20) in some versions. The receiver 153 may, therefore, be configured to operate using a wireless communications link, such as a Bluetooth, Bluetooth Low Energy (BLE), Zigbee, or similar communications link. The network 40 may include the Internet.

In some versions a transceiver 154 may perform the functions of both the transmitter 152 and receiver 153.

The probe 10 may include a processor 155, which may be operably coupled to the power source 151 and/or transmitter 152 and/or receiver 153 and/or transceiver 154 and/or the or each sensor 100. The probe 10 may include a memory 156, which may be operably coupled to the power source 151. The memory 156 may be operably coupled to the processor 155. The memory 156 may be a non-transitory computer readable medium. The memory 156 may store one or more rules governing the operation of the processor 155.

The data transmitted by the probe 10 may be encrypted (e.g. by the processor 155). The probe 10 (e.g. the processor 155) may, therefore, be configured to encrypt the data, such as the soil environment characteristics, before transmission. The encryption algorithm may be stored in the memory 156. For example, the data may be encrypted using a triple DES (Data Encryption Standard), AES (Advanced Encryption Standard), and/or RSA (Rivest-Shamir-Adleman) algorithm.

The data transmitted by the probe 10 may be transmitted with an associated hash value, also known as a hash code or simply as a hash, which may be determined using a corresponding hash function. The processor 155 may be configured to determine the hash value for the transmitted data. The hash function may include SHA-256 or SHA-512, for example. The association of the hash value with the transmitted data may provide certainty that the transmitted data has not been tampered with (e.g. may provide certainty that the soil characteristics received at the remote location correspond to those determined by the probe 10). This may, therefore, inhibit fraudulent data reporting and ensure reliability of the received data. The hash function may be stored in the memory 156.

The probe 10 may be configured to determine and/or transmit the soil environment characteristics regularly (e.g. at least once a month, at least twice a month, at least three times a month, at least four times a month, at least five times a month, at least six times a month, at least seven times a month, at least eight times a month, at least nine times a month, at least ten times a month, at least eleven times a month, at least twelve times a month, at least thirteen times a month, at least fourteen times a month, at least fifteen times a month, at least sixteen times a month, at least seventeen times a month, at least eighteen times a month, at least nineteen times a month, at least twenty times a month, at least twenty-one times a month, at least twenty -two times a month, at least twenty-three times a month, at least twenty-four times a month, at least twenty-five times a month, at least twenty-six times a month, at least twenty-seven times a month, at least twenty-eight times a month, at least twenty-nine times a month, at least thirty times a month, at least once a week, at least twice a week, at least three times a week, at least four times a week, at least five times a week, at least six times a week, at least once a day, at least five times a day, at least ten times a day, at least twenty times a day, at least once an hour, at least twice an hour, at least three times an hour, at least once every thirty minutes, at least once every ten minutes, at least once every five minutes, or at least once a minute).

The system 1 may include a plurality of probes 10. In some versions each probe 10 may be equipped with the same combination of sensors 100, whereas in other versions different probes 10 may be equipped with different combinations of sensors 100. The number of probes 10 included in the system 1 may vary depending on the size of the area to be analysed. The number of probes 10 included in the system 1 may increase as the size of the area to be analysed increases. The number of probes 10 included in the system 1 may be proportional to the size of the area to be analysed.

The system 1 may include the remote device 20. The remote device 20 may be remote from the probe or probes 10. The remote device 20 may, therefore, be located in a location that is remote from the location of the or each probe 10. As described herein, the remote device 20 may be configured to receive data from the or each probe 10. The received data may include the soil environment characteristics and/or other data as described herein. The remote device 20 may, therefore, be directly or indirectly connected (i.e. communicatively coupled) to the or each probe 10 (e.g. via the network 40). The remote device 20 may be communicatively coupled to the or each probe 10 by a wired or wireless communications link.

The remote device 20 may include any type of computing device. For example, the remote device 20 may include a server, desktop computer, laptop, tablet, mobile computing device (e.g. smartphone), or similar. The remote device 20, or a part thereof, may be configured to act as a server. The remote device 20 may include a cloud-based remote device 20. The remote device 20 may be connected to the network 40 and at least a part of the remote device 20 may be accessible using the network 40. At least a part of the remote device 20 may, therefore, be accessible using the Internet.

The remote device 20 may be implemented as a single computing device. In some versions, the remote device 20 may include components that are spread or distributed across a plurality of physical locations (e.g. in cloud-based implementations of the remote device 20). The functions and/or components of the remote device 20 may therefore be distributed over a plurality of locations. The remote device 20 may, therefore, be referred to as a distributed remote device 20. The remote device 20 may include a memory 201 configured to store the received data. The memory 201 may therefore include a non-transitory computer-readable medium. As described, the memory 201 may in some versions be distributed across a plurality of locations. The received data may be stored in a database, for example. Accordingly, the remote device 20 may include a data acquisition system, which may be configured to receive and/or store data from the or each probe 10.

The remote device 20 may be configured to analyse the received data. The remote device 20 may, therefore, include one or more processors 202, and the data analysis may be performed using the one or more processors 202. The one or more processors 202 may be distributed across a plurality of locations.

The remote device 20 may be configured to perform calculations on the received data. The remote device 20 may be configured to output one or more results. The remote device 20 may, therefore, be configured to determine one or more properties associated with the received data, such as one or more properties associated with the soil environment characteristics. The remote device 20 may, therefore, be configured to determine one or more properties associated with the soil with which the soil environment characteristics are associated.

The remote device 20 may be configured to determine a soil organic carbon content. The remote device 20 may be configured to determine the soil organic carbon content using the received data. The remote device 20 may, therefore, be configured to determine the soil organic carbon content using the soil environment characteristics. The soil organic carbon content may be representative of an amount of organic carbon present in the soil from which the soil environment characteristics were obtained. The soil organic carbon content may be provided in different formats, such as a percentage amount or an absolute amount. For example, the soil organic carbon content may be determined as a percentage w/w (weight per weight).

Accordingly, the remote device 20 may be configured to receive the soil environment characteristics from the or each probe 10 and to determine the one or more properties associated with the soil environment characteristics (e.g. the soil organic carbon content) using the received soil environment characteristics. In versions in which there are a plurality of probes 10, the remote device 20 may be configured to collate the received soil environment characteristics and to use the collated soil environment characteristics to determine the one or more properties (e.g. the soil organic carbon content). In some versions, the remote device 20 may be configured to determine the one or more properties (e.g. the soil organic carbon content) separately for the data (e.g. soil environment characteristics) received from each probe 10, and to combine the determined properties to determine a combined output for the area covered by the probes 10. For example, the determined soil organic carbon contents corresponding to a set of probes 10 covering an area (e.g. a field) may be averaged to determine a soil organic carbon content corresponding to the area covered by the probes 10 (e.g. the field). The average may be a mean average or a median average, for example. Determining the one or more properties (e.g. the soil organic carbon content) for an area covered by a set of probes 10 in this manner may mitigate the effect of any anomalous or local fluctuations in the determined properties, and may therefore provide a more balanced view of the properties of the soil in that area. This may, therefore, provide an advantage compared to soil sampling, which is often carried out by sampling a single spot in an area (e.g. a field), which may not necessarily be representative of the area as a whole.

The remote device 20 may include a calculator module 203, and the calculator module 203 may be configured to perform the data analysis described herein. For example, the determination of the one or more properties associated with the received data and/or soil environment characteristics and/or soil with which the soil environment characteristics are associated may be performed by the calculator module 203.

At least a part of the remote device 20 may be configured to operate as a virtual device. For example, the remote device 20 may include a virtual server. The virtual server may include the calculator module 203 (i.e. the calculator module 203 may be implemented using the virtual server). The virtual server may be configured such that no static files reside on the server. This may therefore inhibit an attacker from accessing sensitive information. The virtual server may be configured such that files are created on-the-fly, for example by a Node server, which may run hidden executable processes. The Node server may be a Node.js server written in JavaScript.

At least a part of the remote device 20 may be protected by encryption and/or by one or more firewalls. The virtual device, such as the virtual server, which may as described include the calculator module 203, may be protected by encryption and/or by one or more firewalls. The firewalls may include a software firewall and/or a hardware firewall. The encryption may include triple DES (Data Encryption Standard), AES (Advanced Encryption Standard), and/or RSA (Rivest- Shamir-Adleman) encryption. The encryption may in particular include AES encryption using 128-, 192-, or 256 -bit keys. 192- or 256-bit AES encryption may be particularly suitable.

As described herein, at least a part of the remote device 20 may be accessible remotely, for example using a network, e.g. the network 40. At least a part of the remote device 20 may be accessible using the Internet. The part of the remote device 20 that is accessible remotely may include the virtual device (which, as described, may include the virtual server and may include the calculator module 203). Therefore, the virtual server and/or calculator module 203 may be accessible remotely. A user attempting to access the remote device 20 may need to pass a credential check to gain access (for example, by providing an approved username and password). A user attempting to access the remote device 20 may require a license (and evidence of this license may need to be provided as part of the credential check).

The remote device 20 may be configured to determine the one or more properties associated with the received data (which, as described, may be one or more properties associated with the soil environment characteristics and/or one or more properties associated with the soil with which the soil environment characteristics are associated) using a machine learning technique. The calculator module 203 may, therefore, use a machine learning technique, and may in particular use the machine learning technique to determine the one or more properties associated with the received data (which, as described, may be one or more properties associated with the soil environment characteristics and/or one or more properties associated with the soil with which the soil environment characteristics are associated). More specifically, the machine learning technique may be used in the determination of the soil organic carbon content. It will be appreciated that the machine learning technique may be a part of a wider process used to determine the one or more properties (e.g. the soil organic carbon content), and therefore other techniques or other steps may be used in addition to the machine learning technique.

The machine learning technique may include the use of a stochastic global search optimization algorithm. The machine learning technique may include a simulated annealing technique. The remote device 20 (e.g. the calculator module 203 thereof) may, therefore, be configured to determine the one or more properties associated with the received data, such as the soil organic carbon content, using the simulated annealing technique.

Simulated annealing may, therefore, be used to approximate the global optimum of an objective function including one or more variables. The objective function may include a plurality of variables. The objective function may be subject to one or more constraints, and may therefore be subject to a plurality of constraints. The objective function may be configured such that the soil organic carbon content is determinable using simulated annealing.

The machine learning technique may include a training phase and an analysis phase. In the training phase, the remote device 20 may be trained using training data to determine the relationship between the received data and the one or more properties associated with the received data. In other words, the remote device 20 may be trained to determine the relationship between the soil environment characteristics and the one or more properties associated with the soil environment characteristics, such as the soil organic carbon content. The training data may, therefore, include a set of soil environment characteristics and a corresponding set of known properties (e.g. soil organic carbon contents). In some versions, therefore, the training phase may be used to establish the optimum parameters for use in the machine learning (e.g. stochastic global search optimization and/or simulated annealing) technique to determine the properties associated with the received data (e.g. to determine the soil organic carbon content associated with the received soil environment characteristics). In the case of soil organic carbon content, the set of known soil organic carbon contents used in the training data may be determined by known methods (e.g. loss on ignition or Dumas soil testing, also known as dry combustion testing). The remote device 20 may, therefore, be trained to determine the soil organic carbon content from a set of soil environment characteristics using a simulated annealing technique.

In the analysis phase, the remote device 20 may use the received data to determine the one or more properties associated with the received data (e.g. soil organic carbon content). The one or more properties may, therefore, be unknown at the time when the received data is received (i.e. the one or more properties may not be included in the received data). The remote device 20 may use the optimised machine learning technique (e.g. stochastic global search optimization and/or simulated annealing) from the training phase to analyse the received data in the analysis phase. The machine learning technique may, therefore, include a simulated annealing technique that is optimised in a training phase and used to determine the one or more properties associated with the received data (e.g. the soil organic carbon content) in an analysis phase.

The training phase and/or analysis phase may be repeated. The training phase may be repeated (e.g. as more training data becomes available) such that the optimisation of the machine learning technique continues to improve. A training phase may be performed following an analysis phase and/or following a previous training phase. The remote device 20 may be configured such the training phase is implemented regularly (e.g. training may be scheduled to occur on a regular basis such as overnight, once a week, once a month, etc.), and the frequency of implementation of the training phase may correspond to the availability of training data. In some versions training phases may be implemented on an ad hoc basis as new training data becomes available. Following a period of training, the remote device 20 may return to the analysis phase to analyse received data. In some versions previously received data that has already been analysed to determine the one or more properties associated with the received data may be re-analysed (e.g. to recalculate the one or more properties) following a period of training. This may lead to more accurate determinations of the one or more properties associated with the received data as the optimisation of the machine learning technique improves over time.

The remote device 20 may be configured to pre-process the received data in a pre-processing step before the one or more properties associated with the received data are determined. The calculator module 203 may, therefore, use the pre-processed data. More specifically, the calculator module 203 may use the pre-processed data to determine the one or more properties (e.g. the soil organic carbon content) associated with the received data (e.g. the soil environment characteristics). The pre-processing step may include selecting a subset of the received data for use in determining the one or more properties. The received data that is not selected may, therefore, not be used to determine the one or more properties. This may reduce the time and/or resources required to determine the one or more properties (e.g. the soil organic carbon content).

The pre-processing step may include scaling the received data. The received data may be scaled to ensure compatibility between the units used in the received data and the units used in the determination of the one or more properties (i.e. the units used by the calculator module 203). For example, a probe sensor may measure conductivity in milliSiemens but the calculator module 203 may be configured to use microSiemens; accordingly, the data may be re-scaled by a factor of 1 ,000.

The pre-processing step may include transforming the received data.

As described, the remote device 20 (e.g. the calculator module 203 thereof) may be configured to determine the one or more properties associated with the received data. The remote device 20 may be configured to store the one or more determined properties (e.g. to store the determined soil organic carbon content), and the determined properties may be stored in the memory 201 of the remote device 20. The remote device 20 may be configured to transmit the determined properties to another device, such as a user device 30. The remote device 20 may, therefore, be configured to determine the one or more properties associated with the received data, such as the soil organic carbon content, and to output the or each determined property to another device, such as the user device 30.

The user device 30 may be any kind of computing device, including for example a desktop computer, laptop, tablet, mobile computing device (e.g. smartphone), or similar. The user device 30 may be configured for a user to interact with the user device, and may therefore include a user interface. The user device 30 may include a screen 301 and may include one or more user interaction devices 302 (e.g. a touchscreen, keyboard, and/or mouse). The user device 30 may be configured to display data to the user.

The user device 30 may be configured to be connected to the network 40 and may therefore be configured to be connected (i.e. communicatively coupled) to the or each probe 10 and/or to the remote device 20. The user device 30 may, therefore, be configured to access the remote device 20 or one or more parts thereof (such as the memory 201 and/or calculator module 203). In some versions the user device 30 may be configured to connect to the or each probe 10 and/or remote device 20 directly (e.g. by a wired or wireless communications link).

The user device 30 may be configured to display the outputs of the remote device 20 to a user.

The user device 30 may, therefore, be configured to display the one or more determined properties (e.g. the soil organic carbon content) to the user. In some versions a single device may include the remote device 20 and user device 30. In other words, a single device may be configured to receive the data from the or each probe 10, to analyse the data to determine the one or more properties associated with the received data, and to display the determined properties to a user.

The system 1 may include one or more application programming interfaces (APIs) which may facilitate communication between the probes 10, remote device 20, and/or user device 30.

The system 1 may therefore provide accurate, reliable, real-time information regarding properties associated with the received data, and in particular regarding the environment corresponding to the received data (e.g. the soil environment). In some versions the system 1 may be used to determine accurate, reliable, real-time information regarding the soil organic carbon content of an area of soil (e.g. a field). This may provide a significant improvement over prior techniques, which may for example include taking occasional samples for laboratory analysis (e.g. once a year). The information provided by the system 1 can lead to a better understanding of the soil and therefore enable more sustainable land management.

When used in this specification and claims, the terms "comprises" and "comprising" and variations thereof mean that the specified features, steps or integers are included. The terms are not to be interpreted to exclude the presence of other features, steps or components.

The invention may also broadly consist in the parts, elements, steps, examples and/or features referred to or indicated in the specification individually or collectively in any and all combinations of two or more said parts, elements, steps, examples and/or features. In particular, one or more features in any of the embodiments described herein may be combined with one or more features from any other embodiment(s) described herein.

Protection may be sought for any features disclosed in any one or more published documents referenced herein in combination with the present disclosure.

Although certain example embodiments of the invention have been described, the scope of the appended claims is not intended to be limited solely to these embodiments. The claims are to be construed literally, purposively, and/or to encompass equivalents.

Claims

1. A soil analysis system, including: a probe configured to determine one or more soil environment characteristics and to transmit the or each soil environment characteristic to a remote device; and the remote device, wherein the remote device is configured to determine a soil organic carbon content using the one or more received soil environment characteristics.

2. A system according to claim 1 , wherein the probe is a soil probe configured to be at least partially inserted into soil to determine the one or more soil environment characteristics.

3. A system according to any preceding claim, wherein the remote device is configured to determine the soil organic carbon content based at least partly on a set of training data.

4. A system according to any preceding claim, wherein the remote device is configured to determine the soil organic carbon content using a stochastic global search optimization algorithm.

5. A system according to claim 4, wherein the remote device is configured to determine the soil organic carbon content using a simulated annealing technique.

6. A system according to any preceding claim, further including a user device, wherein the remote device is configured to output the determined soil organic carbon content to the user device.

7. A system according to any preceding claim, wherein the one or more soil environment characteristics include an electrical characteristic of the soil.

8. A system according to any of claims 1-6, wherein the one or more soil environment characteristics include at least one of: precipitation amount, soil temperature, soil capacitance, soil resistance, and soil conductance.

9. A system according to any preceding claim, wherein the probe is configured to determine and transmit the one or more soil environment characteristics at least once per hour.

10. A system according to any preceding claim, wherein the system includes a plurality of the probes, each configured to determine one or more soil environment characteristics and to transmit the or each soil environment characteristic to the remote device, and wherein the remote device is configured to use the one or more soil environment characteristics received from each probe to determine the soil organic carbon content.

11. A soil analysis method, the method including: determining, using a probe, one or more soil environment characteristics; transmitting the or each soil environment characteristic from the probe to a remote device; and determining, using the remote device, a soil organic carbon content using the one or more soil environment characteristics.

12. A method according to claim 11 , wherein the probe is a soil probe at least partially inserted into soil.

13. A method according to claim 11 or 12, wherein the soil organic carbon content is determined based at least partly on a set of training data.

14. A method according to any of claims 11-13, wherein the soil organic carbon content is determined using a stochastic global search optimization algorithm.

15. A method according to claim 14, wherein the soil organic carbon content is determined using a simulated annealing technique.

16. A method according to any of claims 11-15, further including outputting the determined soil organic carbon content to a user device.

17. A method according to any of claims 11-16, wherein the one or more soil environment characteristics include an electrical characteristic of the soil.

18. A method according to any of claims 11-16, wherein the one or more soil environment characteristics include at least one of: precipitation amount, soil temperature, soil capacitance, soil resistance, and soil conductance.

19. A method according to any of claims 11-18, wherein the probe is configured to determine and transmit the one or more soil environment characteristics at least once per hour.

20. A method according to any of claims 11-19, including: determining, using a plurality of the probes, the one or more soil environment characteristics; and transmitting the or each soil environment characteristic from each probe to the remote device, wherein the remote device is configured to use the one or more soil environment characteristics received from each probe to determine the soil organic carbon content.

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