EP4147189A1 - Electronic system for farmers and agronomists comprising a server - Google Patents
Electronic system for farmers and agronomists comprising a serverInfo
- Publication number
- EP4147189A1 EP4147189A1 EP21721606.8A EP21721606A EP4147189A1 EP 4147189 A1 EP4147189 A1 EP 4147189A1 EP 21721606 A EP21721606 A EP 21721606A EP 4147189 A1 EP4147189 A1 EP 4147189A1
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- EP
- European Patent Office
- Prior art keywords
- data
- server
- agronomic
- type
- user
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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
Definitions
- the present invention relates to an electronic system comprising a server which is intended for use by farmers and agronomists, but which may also be particularly useful in general to producers, distributors, sellers and consumers of agricultural products.
- the farmer and/or agronomist is often unable to obtain the information in the time, quality and quantity so as to make the best decisions.
- the general object of the present invention is to provide an electronic system which facilitates the activities and decisions of an operator (to a farmer or an agronomist) without sacrificing the results from an agronomic point of view, but rather improving them compared to the case of not using the electronic system.
- the important data generated by the electronic system according to the present invention include the agronomic-type probabilistic data that refer to plant pathologies and/or plant physiologies.
- probabilistic data to be highly accurate and reliable they are advantageously estimated starting from environmental parameters and/or crop parameters and/or from biological parameters; of course, if both environmental parameters and crop parameters and biological parameters are used, the accuracy and reliability are maximized.
- probabilistic data to be highly accurate and reliable they are advantageously estimated thanks to mathematical formulae derived from physical and/or agronomic models, and/or thanks to mathematical formulae derived from algorithms based on experimental evidence; of course, if both physical models and agronomic models and experimental evidence are used, the accuracy and reliability are maximized.
- FIG. 1 shows a simplified block diagram of an embodiment example of a system according to the present invention.
- the electronic system 1000 of Fig. 1 serves to facilitate the activities and decisions of a farmer and/or agronomist dealing with an area 10 (cultivated), but can also be useful for other parties, e.g. a distributor of agricultural products who distributes products coming from the area 10 or a reseller (wholesale or retail) of agricultural products who sells products coming from the area 10, and, more generally, to producers, distributors, sellers and consumers of agricultural products.
- the system 1000 is conceptually divided into two parts: a first part that is "in the field” and essentially comprises a plurality of electronic apparatuses 100, and a second part that is "away from the field” and essentially comprises a server 200.
- the first part of the system is mainly for collecting data.
- the second part of the system part is mainly for processing data.
- the first part of the system transmits the collected data to the second part of the system which receives them; as will be described later, the first part may also, advantageously, but not necessarily, collect data and information from one or more users who are "in the field".
- Data communications between the first part of the system and the second part of the system take place through a computer network 300 (e.g., in a manner described below); such a network may be variously composed and arranged, and may comprise various types of connections, for example, via radio and/or cable (electrical and/or optical); a typical component of such a network is the Internet.
- a gateway (which may be associated with the reference 400 but is not shown in the figure) is connected between the apparatuses 100 and the server 200.
- the gateway can be connected to the Internet.
- the choice to use a WLAN is particularly suitable in cases where it is too complicated and/or expensive to lay cables in the area of interest; for example, a greenhouse may be easier to wire, but it can still be convenient to use a WLAN even in the case of greenhouses.
- the second part of the system is adapted to provide information supporting the farmer and/or agronomist decisions.
- the farmer and/or the agronomist connect to the server 200 or to another data processor (which could be associated to the reference 600 but is not shown in the figure) associated to the server 200, through the computer network 300 (in particular the Internet) using, for example, a special "human-machine interface" or HMI which can be of HW or SW type, for example a so-called "app"; in the example of Fig. 1, a software module 260 is highlighted that is conceptually adapted to manage the human-machine interface.
- a “data processor” can be, for example, a computer (desktop or laptop), a server, a tablet, a smartphone.
- the area 10 of interest to, for example, the farmer or agronomist is subdivided into sub-areas; in the figure, three sub-areas 10-1 and 10-2 and 10-3 are shown (typically separated from each other, i.e., not overlapping), but typically the number of sub- areas will be greater.
- an electronic apparatus 100 that is installed in a place of the sub-area so as to "cover" the sub-area, i.e., so that the pedoclimatic/agronomic-type data detected locally by the relevant electronic apparatus 100 are reasonably attributable to the entire sub-area.
- Pedoclimatic/agronomic data detected locally are for example air temperature, soil temperature, air humidity, soil moisture, air pressure, wind speed, solar radiation, leaf wetness.
- the electronic apparatus 100 is adapted to transmit data to the server 200 through the network 300; in the figure, a circuit 140 is shown that has at least the capability of transmitting data, but may also have the capability of receiving data; the transmitted data are at least the pedoclimatic/agronomic data detected locally by the apparatus, but may also be data calculated by the apparatus, in particular calculated pedoclimatic/agronomic data reasonably attributable to the entire sub-area.
- the possible capability of communicating via radio makes the apparatus 100 easily and quickly installable anywhere in the area 10; therefore, it can also be easily and quickly moved by the farmer and/or agronomist.
- the apparatus 100 can allow its firmware and/or algorithms (deterministic and/or probabilistic) and/or pedoclimatic/agronomic parameters to be updated.
- the possible capability of transmitting and receiving (other types of data) makes the apparatus 100 easily maintainable; for example, it may allow diagnostics of the apparatus 100 to be performed remotely.
- the circuit 140 may be for example a radio transmitter or transceiver, a GSM/UMTS/LTE/... modem, a LAN card (for wired or wireless connection); it may also be envisaged that the same apparatus 100 integrates more than one circuit so as to adapt more easily to various installation conditions.
- the circuit 140 will be managed by special software installed in the apparatus 100 (in particular stored in the memory 120A).
- the apparatus 100 comprises a processor 110, e.g., a microcontroller, associated with memory 120 (program memory “A” and data memory “B"), and with sensors 130 (e.g., an air temperature sensor “A”, a solar radiation sensor “B”, a leaf wetness sensor “C”).
- processor 110 e.g., a microcontroller
- memory 120 program memory "A” and data memory “B”
- sensors 130 e.g., an air temperature sensor "A”, a solar radiation sensor “B”, a leaf wetness sensor "C”
- the pedoclimatic/agronomic data calculated by the apparatus 100 may be, for example, average, minimum, maximum temperature over a predetermined period of time, VPD (or "vapour-pressure deficit"), DP (or “dew point”), DD (or “degree days”).
- the data transmitted by the apparatus 100 are pedoclimatic/agronomic data (detected and possibly calculated) and are associated with the transmitting electronic apparatus; said in other words, the server 200 is able to establish from which apparatus 100 the received data originate and therefore, if the association between apparatuses and sub-areas is known to the server, the server 200 is able to establish from which area or, even better, from which sub-area the received data originate.
- the data transmitted by the apparatus 100 are "deterministic” or “actual” pedoclimatic/agronomic data and are detected and, if necessary, calculated by means of mathematical formulae (suitably stored); as will be better understood later, they differ from “probabilistic” or “predicted” data processed by the server 200.
- the apparatuses 100 of the system 1000 comprise a device 150 adapted to detect the geographic position of the apparatus; the determination of the position may be done, for example, thanks to the GPS system and/or thanks to a cellular telephone system (e.g., GSM or UMTS) and triangulation and/or thanks to another type of telecommunications system and triangulation and/or thanks to the Internet.
- a cellular telephone system e.g., GSM or UMTS
- the data transmitted by the apparatus 100 are pedoclimatic/agronomic data (detected and possibly calculated) and can be associated with the geographic position of the transmitting apparatus; said in other words, the server 200 is able to establish from which geographic position the received data originates and therefore, if the association between geographic positions and sub-areas is known to the server, the server 200 is able to establish from which area or, even better, from which sub-area the received data originates.
- each of the apparatuses 100 is identified by its own identity (e.g., its electronic identifier) and by its geographic position; thus, the positioning of the apparatuses 100 (and possibly the subdivision of the area 10 into sub-areas) can be automatically determined by the system 1000 and thus, if necessary, the farmer and/or agronomist can independently move the apparatuses 100 without the need for the intervention of other technicians.
- its own identity e.g., its electronic identifier
- geographic position e.g., its geographic position
- the data are subject to "time- stamping” and "position-stamping" by the apparatus and/or by the server.
- data are transmitted from the apparatus to the server.
- each of the apparatuses 100 is adapted to encrypt the data before transmission to the server 200; in fact, the Applicant has realised that data detected so precisely (in time and space) are of considerable value and a farmer and/or agronomist do not want them to be used by others.
- each of the apparatuses 100 may be adapted to encrypt by means of a key uniquely associated with the farmer and/or agronomist, which key is stored in particular apparatuses 100 (e.g., in memory 120B) of the system 1000 and known only to the server 200 to be able to decrypt them.
- the key may change from time to time with appropriate timing and/or at the server's request.
- each of the apparatus 100 comprises: an electric energy source 160 (e.g. a photovoltaic panel or a wind generator), a rechargeable-type electric accumulator 170 adapted to provide power supply to the apparatus, a charger circuit 180 connected at the input with the electric energy source 160 and at the output with the electric accumulator 170; in this way, there is no need to lay a plurality of long electrical cables when setting up the system 1000, and the system 1000 is fully functional in all lighting conditions (both at night and when it is very cloudy).
- an electric energy source 160 e.g. a photovoltaic panel or a wind generator
- a charger circuit 180 connected at the input with the electric energy source 160 and at the output with the electric accumulator 170; in this way, there is no need to lay a plurality of long electrical cables when setting up the system 1000, and the system 1000 is fully functional in all lighting conditions (both at night and when it is very cloudy).
- one or more or all of the electronic apparatuses 100 comprises: a user interface 190, hardware and/or software, adapted to locally receive user data from a user; and is adapted to transmit said user data to the server 200, in particular by machine learning of an artificial intelligence system (more on this later).
- This user interface can be realised in many different ways; it can provide only the input (e.g. through a small keyboard) by the user or both the input (e.g. through a small keyboard) and the output (e.g. through LEDs and/or a small display and/or a loudspeaker); the input and/or the output can be realised through devices inside the apparatus or devices external to the apparatus (e.g. a small dedicated user terminal or a smartphone) and connected to the apparatus via cable and/or radio.
- Such user data are or comprise agronomic-type data, in particular they refer to plant pathologies and/or plant physiology (in particular the phenological state) detected locally by the user; this operation can be performed for example by an agronomist during his "field" inspections.
- Such user data may also comprise images detected locally by the user.
- an agronomist can not only communicate the health state of plants to the server (e.g.: "good”, “medium”, “low”), in particular in relation to a specific pathology, but he may also (eventually) take pictures and send them to the server; these images might be stored and/or processed by the server. It may be provided that the user interface allows the user to choose which parameter or which parameters to enter into the system locally through the electronic apparatus.
- the user interface will only allow the user to choose from a set of predetermined values (e.g: 0, 1, 2, and 3); the predetermined values can be of the qualitative type (e.g.,: "good",
- the set may depend on the parameter; this is particularly advantageous if the user interface is used for machine learning of an artificial intelligence system.
- the user interface is used for machine learning of an artificial intelligence system, only "selected users” should be allowed to use it (and thus to enter data). For this reason, the user interface may provide for user authentication; for this purpose, the "selected users” may have appropriate (typically personal) credentials. For the same reasons, it is advantageous for user data to be transmitted associated with an identity of the authenticated user who entered them.
- a single electronic apparatus having one or more of the technical features described above constitutes an independent aspect of the present invention.
- the technical features related to its user interface are particularly advantageous if the electronic apparatus is adapted to interface with an artificial intelligence system with machine learning; not all of them are strictly indispensable, but all of them are advantageous.
- the server 200 could be used to implement multiple electronic systems like the one just described; that is, the same server could be shared by multiple electronic systems.
- the electronic apparatuses 100 and possibly the gateway belong to a single electronic system only.
- a gateway may be shared by multiple electronic systems or belong to a single electronic system only. If the server is shared by multiple electronic systems, there is one database in the server subdivided into multiple sections, one for each system, or there are multiple databases, one for each system.
- the server 200 comprises a processor 210, e.g., a microprocessor, associated with memory 220 (program memory "A" and data memory "B"), at least one database 230, and a circuit 240 that has at least the capability of receiving data, but also typically has the capability of transmitting data.
- the server 200 transmits and receives data to and from the Internet thanks to the circuit 240 and this is used to both communicate with the apparatuses 100 and to communicate with the users.
- communication with the apparatuses and communication with the users may follow different paths, and typically, the server comprises distinct and different circuits.
- the circuit 240 may be, for example, a radio transmitter or transceiver, a GSM/UMTS/LTE/... modem, a LAN card (for wired or wireless connection); it may also be envisaged that the server 200 integrates more than one circuit so as to adapt more easily to various installation conditions.
- the circuit 240 will be managed by special software installed in the server 200 (specifically stored in the memory 220A); In the example of Fig.
- a software module 260 is highlighted that is conceptually adapted to interact with the user whether the user is “near” the server 200 (i.e., local user) or “far” from the server 200 (i.e., remote user) and “near” a data processor 600 or “near” an electronic apparatus 100; the latter two cases involve communication among electronic computers.
- a software module 250 is highlighted that is conceptually adapted to interact (and therefore communicate) with remote electronic computers 500 adapted to provide data and information to the server.
- the server 200 of the system 1000 is adapted to receive (in particular thanks to the circuit 240) pedoclimatic/agronomic-type data detected (and possibly pedoclimatic/agronomic-type data calculated) by the apparatuses 100 of the system 1000, and is adapted to store the data received in the database 230.
- the server 200 is adapted to provide information supporting the farmer and/or agronomist decisions at least based on data stored in the database 230.
- the Applicant has realised that it is highly appropriate that the information provided refers to only one species cultivated in the area of interest (indicated by 10 in the figure), i.e. that the system performs specialised calculations and processing for each species cultivated in the area of interest.
- the Applicant has realised that it is highly appropriate that the information provided takes into consideration the type of substrate in the area of interest (indicated by 10 in the figure), in particular the type of substrate in each sub-area (indicated by 10-1, 10-2, 10-3 in the figure) covered by the electronic apparatuses, respectively (indicated by 100 in the figure).
- the server 200 may be adapted to receive and possibly store (for example in the memory 220B) meteorological data (past and/or present and/or future) received from one or more other computers (which may be associated with the reference 500 but are not shown in the figure) by means of a computer network, in particular the Internet. Such data may also be used by the server 200 to provide information supporting the farmer and/or agronomist decisions.
- meteorological data past and/or present and/or future
- Such data may also be used by the server 200 to provide information supporting the farmer and/or agronomist decisions.
- the server 200 is typically adapted to process received data, to generate processed data, and to store the processed data (e.g., in the memory 220B and/or the database 230); the processed data may be pedoclimatic/agronomic-type "deterministic” (or “actual”) data and/or agronomic-type “probabilistic” (or “predicted”) data.
- agronomic-type "probabilistic” data that can refer to plant pathologies and/or plant physiologies are of particular interest for the farmer and/or agronomist decisions.
- Pedoclimatic/agronomic-type “deterministic” data can be, for example, average, minimum, maximum temperature over a predetermined period of time, VPD (or “vapour-pressure deficit”), DP (or “dew point”), DD (or “degree days”).
- the agronomic-type "probabilistic” data can refer to plant pathologies, e.g. "mildews”; in this case, the system will provide information about the probability that the culture will get mildews in the future.
- the agronomic-type probabilistic data may refer to plant physiologies, for example "growth level at harvest” and/or "harvest date”; in this case, the system will provide information about the probable growth level of the culture at the harvest time and/or the probable (optimum) harvest date of the culture. It is not excluded that the system may (try to) predict future physiological needs of the plant.
- the "probabilistic" data may be estimated starting from environmental parameters (whose data may be detected by the sensors of the electronic apparatuses, calculated by the electronic apparatuses and/or the server of the system, or received by computers external to the system), biological variables (whose data may be for example provided by a farmer or agronomist), and/or from crop parameters (whose data may be for example provided by a farmer or agronomist), and/or from biological parameters (whose data are typically provided to the server during the installation step of the system).
- environmental parameters whose data may be detected by the sensors of the electronic apparatuses, calculated by the electronic apparatuses and/or the server of the system, or received by computers external to the system
- biological variables whose data may be for example provided by a farmer or agronomist
- crop parameters whose data may be for example provided by a farmer or agronomist
- biological parameters whose data are typically provided to the server during the installation step of the system.
- the estimation can be carried out thanks to mathematical formulae (suitably stored) derived from physical models and/or agronomic models and/or algorithms (suitably stored) based on experimental evidence.
- the system can provide information that also takes into consideration the expected climate and/or the past climate.
- the "current water requirement” (which is a current physiological need of the plant) can be determined starting from the “evapotranspiration”; the “evapotranspiration” can be estimated starting from environmental variables, biological variables, crop parameters.
- the system can provide information on the "current water requirement” that takes into consideration, in particular, the expected rainfall; for example, if the water requirement were 10 l/m2 and rainfall corresponding to 4 l/m2 is expected in the short term, it would be advisable for the farmer or agronomist to supply the plant with only 6 l/m2.
- the server 200 is adapted to encrypt the data prior to storage in the database 230; in fact, the Applicant has realised that data detected so precisely (in time and space) as well as those calculated and processed are of considerable value and a farmer and/or agronomist does not want them to be used by others.
- the server 200 may be adapted to encrypt by means of a key uniquely associated with the farmer and/or agronomist, which key is stored in particular in the server 200 (for example in the memory 220B).
- the key can change from time to time with appropriate timing.
- the server 200 is adapted to allow users access to data stored in the database 230 selectively, in particular through rules linking access rights to identities of accessing users.
- the server 200 may provide a user authentication system for example based on "credentials" preferably adapted to provide differentiated user rights.
- the server 200 may comprise an (internal) artificial intelligence module 270 or be associated with an (external) artificial intelligence system 700.
- the artificial intelligence module or system may, for example, cause agronomic and/or biological algorithms and/or models included in the server 200 to evolve and optimize; in particular, the server 200 begins its operation based on "standard" agronomic and/or biological algorithms and/or models (or rather developed by the server provider).
- Such optimization may be derived from data and/or information received from other electronic computers.
- Such optimization may derive from "user data" that may be received, in particular, from the apparatuses 100.
- Such optimization may derive from the human-machine software interface 260.
- this second case is defined as “machine learning” and, in the way in which it can be implemented in the system according to the present invention, it allows a precise optimization not only in relation to the area (indicated by 10 in Fig. 1), but even in relation to the various sub-areas (indicated by 10-1, 10-2, 10-3 in Fig. 1).
- optimization can continue over time. This is advantageous because, for example, climate and plant varieties and pathologies evolve over time.
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Abstract
The electronic system (1000), in particular for a farmer or agronomist, comprises a server (200); the server (200) is adapted to receive, in particular via radio (240) and/or cable, pedoclimatic/agronomic-type data detected and possibly pedoclimatic/agronomic-type data calculated by means of a computer network (300), in particular the Internet, by said plurality of electronic apparatuses (100), data received being associated to the transmitting electronic apparatus; the server (200) is adapted to store data received in a database (230); the server (200) is adapted to provide information supporting the farmer or agronomist decisions based on data stored in the database (230); the server (200) is adapted to process received data, to generate processed data and to store processed data (220B, 230); the processed data comprise agronomic-type probabilistic data that refer to plant pathologies and/or plant physiologies.
Description
ELECTRONIC SYSTEM FOR FARMERS AND AGRONOMISTS
COMPRISING A SERVER
DESCRIPTION
FIELD OF THE INVENTION The present invention relates to an electronic system comprising a server which is intended for use by farmers and agronomists, but which may also be particularly useful in general to producers, distributors, sellers and consumers of agricultural products.
STATE OF THE ART Electronics (particularly IT and telecommunications) are still not widely used in the agricultural sector.
Farmers, but also agronomists (at least to some extent), are disinclined to use electronic tools. One reason for this reluctance is the absence of electronic tools dedicated to the agricultural sector. In general, in this area, activities and decisions are based on the presence and intervention of a farmer and/or agronomist in a cultivated area as well as the specific knowledge and experience of the farmer and/or agronomist in charge of this area. Both activities and decisions become more difficult when the size of the area increases; in particular, there may be different pedoclimatic conditions in the same area that may require different activities and/or decisions.
The farmer and/or agronomist is often unable to obtain the information in the time, quality and quantity so as to make the best decisions.
ABSTRACT
The general object of the present invention is to provide an electronic system which facilitates the activities and decisions of an operator (to a farmer or an agronomist) without sacrificing the results from an agronomic point of view, but rather improving them compared to the case of not using the electronic system.
This general object and other more specific objects are achieved thanks to what is expressed in the appended claims that form an integral part of the present
description.
The important data generated by the electronic system according to the present invention include the agronomic-type probabilistic data that refer to plant pathologies and/or plant physiologies. For such probabilistic data to be highly accurate and reliable, they are advantageously estimated starting from environmental parameters and/or crop parameters and/or from biological parameters; of course, if both environmental parameters and crop parameters and biological parameters are used, the accuracy and reliability are maximized. For such probabilistic data to be highly accurate and reliable, they are advantageously estimated thanks to mathematical formulae derived from physical and/or agronomic models, and/or thanks to mathematical formulae derived from algorithms based on experimental evidence; of course, if both physical models and agronomic models and experimental evidence are used, the accuracy and reliability are maximized.
LIST OF FIGURES
The present invention shall become more readily apparent from the detailed description that follows to be considered together with the accompanying drawings in which: Fig. 1 shows a simplified block diagram of an embodiment example of a system according to the present invention.
As is easily understandable, there are various ways of implementing in practice the present invention which is defined in its main advantageous aspects in the claims. DETAILED DESCRIPTION The electronic system 1000 of Fig. 1 serves to facilitate the activities and decisions of a farmer and/or agronomist dealing with an area 10 (cultivated), but can also be useful for other parties, e.g. a distributor of agricultural products who distributes products coming from the area 10 or a reseller (wholesale or retail) of agricultural products who sells products coming from the area 10, and, more generally, to
producers, distributors, sellers and consumers of agricultural products.
The system 1000 is conceptually divided into two parts: a first part that is "in the field" and essentially comprises a plurality of electronic apparatuses 100, and a second part that is "away from the field" and essentially comprises a server 200. The first part of the system is mainly for collecting data.
The second part of the system part is mainly for processing data.
The first part of the system transmits the collected data to the second part of the system which receives them; as will be described later, the first part may also, advantageously, but not necessarily, collect data and information from one or more users who are "in the field".
Data communications between the first part of the system and the second part of the system take place through a computer network 300 (e.g., in a manner described below); such a network may be variously composed and arranged, and may comprise various types of connections, for example, via radio and/or cable (electrical and/or optical); a typical component of such a network is the Internet. Advantageously but not necessarily, a gateway (which may be associated with the reference 400 but is not shown in the figure) is connected between the apparatuses 100 and the server 200.
The gateway may create a LAN, for example a WLAN (= Wireless LAN) or a "wired LAN", between the apparatuses 100 of the area 10 and may be considered as a component of the first part of the system; in general, the apparatuses of the LAN do not need to communicate with each other; advantageously, the apparatuses simply need to have the capability of communicating with the gateway, and the gateway may have greater transmission and/or transceiving capabilities. Of course, the gateway can be connected to the Internet. The choice to use a WLAN is particularly suitable in cases where it is too complicated and/or expensive to lay cables in the area of interest; for example, a greenhouse may be easier to wire, but it can still be convenient to use a WLAN even in the case of greenhouses.
The second part of the system, in particular the server 200, is adapted to provide
information supporting the farmer and/or agronomist decisions. In order to make use of this service, the farmer and/or the agronomist connect to the server 200 or to another data processor (which could be associated to the reference 600 but is not shown in the figure) associated to the server 200, through the computer network 300 (in particular the Internet) using, for example, a special "human-machine interface" or HMI which can be of HW or SW type, for example a so-called "app"; in the example of Fig. 1, a software module 260 is highlighted that is conceptually adapted to manage the human-machine interface.
It may be provided that other parties will connect to the server 200 or to another data processor (which may be associated with the reference 600 but is not shown in the figure) associated with the server 200, through the computer network 300 (in particular the Internet) to use other services. It may be provided that this further connection is made using the same HMI used by the farmer and/or agronomist, in this case, the differentiation of the usable services can be made, for example, on the basis of different access credentials, alternatively, it may be provided that this further connection is made using a different HMI.
A "data processor" can be, for example, a computer (desktop or laptop), a server, a tablet, a smartphone.
The area 10 of interest to, for example, the farmer or agronomist is subdivided into sub-areas; in the figure, three sub-areas 10-1 and 10-2 and 10-3 are shown (typically separated from each other, i.e., not overlapping), but typically the number of sub- areas will be greater.
For each sub-area, there is provided an electronic apparatus 100 that is installed in a place of the sub-area so as to "cover" the sub-area, i.e., so that the pedoclimatic/agronomic-type data detected locally by the relevant electronic apparatus 100 are reasonably attributable to the entire sub-area. Pedoclimatic/agronomic data detected locally are for example air temperature, soil temperature, air humidity, soil moisture, air pressure, wind speed, solar radiation, leaf wetness.
The electronic apparatus 100 is adapted to transmit data to the server 200 through the network 300; in the figure, a circuit 140 is shown that has at least the capability of transmitting data, but may also have the capability of receiving data; the transmitted data are at least the pedoclimatic/agronomic data detected locally by the apparatus, but may also be data calculated by the apparatus, in particular calculated pedoclimatic/agronomic data reasonably attributable to the entire sub-area. The possible capability of communicating via radio makes the apparatus 100 easily and quickly installable anywhere in the area 10; therefore, it can also be easily and quickly moved by the farmer and/or agronomist. The possible capability of receptions makes the apparatus 100 easily maintainable; for example, it can allow its firmware and/or algorithms (deterministic and/or probabilistic) and/or pedoclimatic/agronomic parameters to be updated. The possible capability of transmitting and receiving (other types of data) makes the apparatus 100 easily maintainable; for example, it may allow diagnostics of the apparatus 100 to be performed remotely. Depending on the implementation of the present invention, the circuit 140 may be for example a radio transmitter or transceiver, a GSM/UMTS/LTE/... modem, a LAN card (for wired or wireless connection); it may also be envisaged that the same apparatus 100 integrates more than one circuit so as to adapt more easily to various installation conditions. Typically, the circuit 140 will be managed by special software installed in the apparatus 100 (in particular stored in the memory 120A).
The apparatus 100 comprises a processor 110, e.g., a microcontroller, associated with memory 120 (program memory "A" and data memory "B"), and with sensors 130 (e.g., an air temperature sensor "A", a solar radiation sensor "B", a leaf wetness sensor "C").
The pedoclimatic/agronomic data calculated by the apparatus 100 may be, for example, average, minimum, maximum temperature over a predetermined period of time, VPD (or "vapour-pressure deficit"), DP (or "dew point"), DD (or "degree days").
The data transmitted by the apparatus 100 are pedoclimatic/agronomic data (detected and possibly calculated) and are associated with the transmitting electronic apparatus; said in other words, the server 200 is able to establish from which apparatus 100 the received data originate and therefore, if the association between apparatuses and sub-areas is known to the server, the server 200 is able to establish from which area or, even better, from which sub-area the received data originate.
The data transmitted by the apparatus 100 are "deterministic" or "actual" pedoclimatic/agronomic data and are detected and, if necessary, calculated by means of mathematical formulae (suitably stored); as will be better understood later, they differ from "probabilistic" or "predicted" data processed by the server 200.
The apparatuses 100 of the system 1000 comprise a device 150 adapted to detect the geographic position of the apparatus; the determination of the position may be done, for example, thanks to the GPS system and/or thanks to a cellular telephone system (e.g., GSM or UMTS) and triangulation and/or thanks to another type of telecommunications system and triangulation and/or thanks to the Internet.
The data transmitted by the apparatus 100 are pedoclimatic/agronomic data (detected and possibly calculated) and can be associated with the geographic position of the transmitting apparatus; said in other words, the server 200 is able to establish from which geographic position the received data originates and therefore, if the association between geographic positions and sub-areas is known to the server, the server 200 is able to establish from which area or, even better, from which sub-area the received data originates. Preferably, each of the apparatuses 100 is identified by its own identity (e.g., its electronic identifier) and by its geographic position; thus, the positioning of the apparatuses 100 (and possibly the subdivision of the area 10 into sub-areas) can be automatically determined by the system 1000 and thus, if necessary, the farmer and/or agronomist can independently move the apparatuses 100 without the need
for the intervention of other technicians.
In general, with appropriate modes, groupings and timing, the data are subject to "time- stamping" and "position-stamping" by the apparatus and/or by the server.
In general, with appropriate modes, groupings and timing, data are transmitted from the apparatus to the server.
Preferably, each of the apparatuses 100 is adapted to encrypt the data before transmission to the server 200; in fact, the Applicant has realised that data detected so precisely (in time and space) are of considerable value and a farmer and/or agronomist do not want them to be used by others. In particular, each of the apparatuses 100 may be adapted to encrypt by means of a key uniquely associated with the farmer and/or agronomist, which key is stored in particular apparatuses 100 (e.g., in memory 120B) of the system 1000 and known only to the server 200 to be able to decrypt them. Of course, the key may change from time to time with appropriate timing and/or at the server's request.
Preferably, each of the apparatus 100 comprises: an electric energy source 160 (e.g. a photovoltaic panel or a wind generator), a rechargeable-type electric accumulator 170 adapted to provide power supply to the apparatus, a charger circuit 180 connected at the input with the electric energy source 160 and at the output with the electric accumulator 170; in this way, there is no need to lay a plurality of long electrical cables when setting up the system 1000, and the system 1000 is fully functional in all lighting conditions (both at night and when it is very cloudy).
Advantageously, one or more or all of the electronic apparatuses 100 comprises: a user interface 190, hardware and/or software, adapted to locally receive user data from a user; and is adapted to transmit said user data to the server 200, in particular by machine learning of an artificial intelligence system (more on this later).
This user interface can be realised in many different ways; it can provide only the
input (e.g. through a small keyboard) by the user or both the input (e.g. through a small keyboard) and the output (e.g. through LEDs and/or a small display and/or a loudspeaker); the input and/or the output can be realised through devices inside the apparatus or devices external to the apparatus (e.g. a small dedicated user terminal or a smartphone) and connected to the apparatus via cable and/or radio.
Such user data are or comprise agronomic-type data, in particular they refer to plant pathologies and/or plant physiology (in particular the phenological state) detected locally by the user; this operation can be performed for example by an agronomist during his "field" inspections. Such user data may also comprise images detected locally by the user.
For example, an agronomist can not only communicate the health state of plants to the server (e.g.: "good", "medium", "low"), in particular in relation to a specific pathology, but he may also (eventually) take pictures and send them to the server; these images might be stored and/or processed by the server. It may be provided that the user interface allows the user to choose which parameter or which parameters to enter into the system locally through the electronic apparatus.
It may be provided that, in the step of entering the value of a parameter, the user interface will only allow the user to choose from a set of predetermined values (e.g: 0, 1, 2, and 3); the predetermined values can be of the qualitative type (e.g.,: "good",
"medium", "low"); the set may depend on the parameter; this is particularly advantageous if the user interface is used for machine learning of an artificial intelligence system.
Especially if the user interface is used for machine learning of an artificial intelligence system, only "selected users" should be allowed to use it (and thus to enter data). For this reason, the user interface may provide for user authentication; for this purpose, the "selected users" may have appropriate (typically personal) credentials. For the same reasons, it is advantageous for user data to be transmitted associated with an identity of the authenticated user who entered them.
It should be noted that a single electronic apparatus having one or more of the technical features described above constitutes an independent aspect of the present invention. The technical features related to its user interface are particularly advantageous if the electronic apparatus is adapted to interface with an artificial intelligence system with machine learning; not all of them are strictly indispensable, but all of them are advantageous.
It should be noted that the server 200 could be used to implement multiple electronic systems like the one just described; that is, the same server could be shared by multiple electronic systems. Instead, the electronic apparatuses 100 and possibly the gateway belong to a single electronic system only. Depending on the embodiment, a gateway may be shared by multiple electronic systems or belong to a single electronic system only. If the server is shared by multiple electronic systems, there is one database in the server subdivided into multiple sections, one for each system, or there are multiple databases, one for each system. The server 200 comprises a processor 210, e.g., a microprocessor, associated with memory 220 (program memory "A" and data memory "B"), at least one database 230, and a circuit 240 that has at least the capability of receiving data, but also typically has the capability of transmitting data. In the example of the figure, the server 200 transmits and receives data to and from the Internet thanks to the circuit 240 and this is used to both communicate with the apparatuses 100 and to communicate with the users. Alternatively, communication with the apparatuses and communication with the users may follow different paths, and typically, the server comprises distinct and different circuits. Depending on the implementation of the present invention, the circuit 240 may be, for example, a radio transmitter or transceiver, a GSM/UMTS/LTE/... modem, a LAN card (for wired or wireless connection); it may also be envisaged that the server 200 integrates more than one circuit so as to adapt more easily to various installation conditions. Typically, the circuit 240 will be managed by special software installed in the server 200 (specifically stored in the memory 220A);
In the example of Fig. 1, a software module 260 is highlighted that is conceptually adapted to interact with the user whether the user is "near" the server 200 (i.e., local user) or "far" from the server 200 (i.e., remote user) and "near" a data processor 600 or "near" an electronic apparatus 100; the latter two cases involve communication among electronic computers.
In the example of Fig. 1, a software module 250 is highlighted that is conceptually adapted to interact (and therefore communicate) with remote electronic computers 500 adapted to provide data and information to the server.
It is worth pointing out, with particular reference to the communication between the server 200 and the apparatuses 100, that this could also be via API (=Application Programming Interface) in cases where a "server application" runs on the server 200 and "client applications" run on the apparatuses 100.
The server 200 of the system 1000 is adapted to receive (in particular thanks to the circuit 240) pedoclimatic/agronomic-type data detected (and possibly pedoclimatic/agronomic-type data calculated) by the apparatuses 100 of the system 1000, and is adapted to store the data received in the database 230.
The server 200 is adapted to provide information supporting the farmer and/or agronomist decisions at least based on data stored in the database 230.
The Applicant has realised that it is highly appropriate that the information provided refers to only one species cultivated in the area of interest (indicated by 10 in the figure), i.e. that the system performs specialised calculations and processing for each species cultivated in the area of interest.
In addition, the Applicant has realised that it is highly appropriate that the information provided takes into consideration the type of substrate in the area of interest (indicated by 10 in the figure), in particular the type of substrate in each sub-area (indicated by 10-1, 10-2, 10-3 in the figure) covered by the electronic apparatuses, respectively (indicated by 100 in the figure).
The server 200 may be adapted to receive and possibly store (for example in the memory 220B) meteorological data (past and/or present and/or future) received
from one or more other computers (which may be associated with the reference 500 but are not shown in the figure) by means of a computer network, in particular the Internet. Such data may also be used by the server 200 to provide information supporting the farmer and/or agronomist decisions.
The server 200 is typically adapted to process received data, to generate processed data, and to store the processed data (e.g., in the memory 220B and/or the database 230); the processed data may be pedoclimatic/agronomic-type "deterministic" (or "actual") data and/or agronomic-type "probabilistic" (or "predicted") data. Obviously, agronomic-type "probabilistic" data that can refer to plant pathologies and/or plant physiologies are of particular interest for the farmer and/or agronomist decisions.
Pedoclimatic/agronomic-type "deterministic” data can be, for example, average, minimum, maximum temperature over a predetermined period of time, VPD (or "vapour-pressure deficit"), DP (or "dew point"), DD (or "degree days").
The agronomic-type "probabilistic" data can refer to plant pathologies, e.g. "mildews"; in this case, the system will provide information about the probability that the culture will get mildews in the future. Alternatively or additionally, the agronomic-type probabilistic data may refer to plant physiologies, for example "growth level at harvest" and/or "harvest date"; in this case, the system will provide information about the probable growth level of the culture at the harvest time and/or the probable (optimum) harvest date of the culture. It is not excluded that the system may (try to) predict future physiological needs of the plant.
In general, the "probabilistic" data may be estimated starting from environmental parameters (whose data may be detected by the sensors of the electronic apparatuses, calculated by the electronic apparatuses and/or the server of the system, or received by computers external to the system), biological variables (whose data may be for example provided by a farmer or agronomist), and/or from crop parameters (whose data may be for example provided by a farmer or agronomist), and/or from biological parameters (whose data are typically provided
to the server during the installation step of the system).
The estimation can be carried out thanks to mathematical formulae (suitably stored) derived from physical models and/or agronomic models and/or algorithms (suitably stored) based on experimental evidence. In order to better support the farmer and/or agronomist decisions, according to particularly sophisticated embodiment examples, the system can provide information that also takes into consideration the expected climate and/or the past climate.
The "current water requirement" (which is a current physiological need of the plant) can be determined starting from the "evapotranspiration"; the "evapotranspiration" can be estimated starting from environmental variables, biological variables, crop parameters. In order to better support the farmer and/or agronomist decisions, according to a particularly sophisticated embodiment example, the system can provide information on the "current water requirement" that takes into consideration, in particular, the expected rainfall; for example, if the water requirement were 10 l/m2 and rainfall corresponding to 4 l/m2 is expected in the short term, it would be advisable for the farmer or agronomist to supply the plant with only 6 l/m2.
Preferably, the server 200 is adapted to encrypt the data prior to storage in the database 230; in fact, the Applicant has realised that data detected so precisely (in time and space) as well as those calculated and processed are of considerable value and a farmer and/or agronomist does not want them to be used by others. In particular, the server 200 may be adapted to encrypt by means of a key uniquely associated with the farmer and/or agronomist, which key is stored in particular in the server 200 (for example in the memory 220B). Of course, the key can change from time to time with appropriate timing.
Preferably, the server 200 is adapted to allow users access to data stored in the database 230 selectively, in particular through rules linking access rights to identities of accessing users. In general, the server 200 may provide a user authentication system for example based on "credentials" preferably adapted to
provide differentiated user rights.
It should be noted that depending on the type of user, access to detected data and/or calculated data and/or processed data of the "deterministic" type and/or processed data of the "probabilistic” type may be useful.
The installation of the system 1000 requires selecting and/or setting of algorithms (deterministic and/or probabilistic) and/or pedoclimatic/agronomic parameters. Advantageously, the server 200 may comprise an (internal) artificial intelligence module 270 or be associated with an (external) artificial intelligence system 700. The artificial intelligence module or system may, for example, cause agronomic and/or biological algorithms and/or models included in the server 200 to evolve and optimize; in particular, the server 200 begins its operation based on "standard" agronomic and/or biological algorithms and/or models (or rather developed by the server provider).
Such optimization may be derived from data and/or information received from other electronic computers.
Such optimization may derive from "user data" that may be received, in particular, from the apparatuses 100.
Such optimization may derive from the human-machine software interface 260. Above all, this second case is defined as "machine learning" and, in the way in which it can be implemented in the system according to the present invention, it allows a precise optimization not only in relation to the area (indicated by 10 in Fig. 1), but even in relation to the various sub-areas (indicated by 10-1, 10-2, 10-3 in Fig. 1).
Advantageously, optimization can continue over time. This is advantageous because, for example, climate and plant varieties and pathologies evolve over time.
Claims
1. An electronic system (1000), in particular for a farmer or agronomist, comprising a server (200), wherein said server (200) is adapted to receive, in particular via radio (240) and/or cable, pedoclimatic/agronomic-type data detected and possibly pedoclimatic/agronomic-type data calculated by means of a computer network (300), in particular the Internet, by said plurality of electronic apparatuses (100), data received being associated to the transmitting electronic apparatus, wherein said server (200) is adapted to store data received in a database (230), and wherein said server (200) is adapted to provide information supporting said farmer or agronomist decisions based on data stored in said database (230); wherein said server (200) is adapted to process received data, to generate processed data and to store processed data (220B, 230), wherein said processed data comprise agronomic-type probabilistic data; wherein said agronomic-type probabilistic data refer to plant pathologies and/or plant physiologies.
2. The system (1000) according to claim 1, wherein said probabilistic data, in particular said agronomic-type probabilistic data, are estimated thanks to mathematical formulas derived from models, in particular physical and/or agronomic models.
3. The system (1000) according to claim 1 or 2, wherein said probabilistic data, in particular said agronomic-type probabilistic data, are estimated thanks to mathematical formulas derived from algorithms based on experimental evidence.
4. The system (1000) according to claim 1 or 2 or 3, wherein said probabilistic data, in particular said agronomic-type probabilistic data, are estimated starting from environmental parameters and/or crop parameters and/or biological parameters.
5. The system (1000) according to any one of the preceding claims, wherein information provided refer to a single cultivated species in an area (10) of interest of said farmer or agronomist.
6. The system (1000) according to any one of the preceding claims, wherein information provided takes into consideration the type of substrate in an area (10) of interest of said farmer or agronomist, in particular the type of substrate in each sub-area (10-1, 10-2, 10-3) respectively covered by the electronic apparatuses (100) of said plurality.
7. The system (1000) according to any one of the preceding claims, wherein said server (200) is adapted to receive (240) and possibly store (220B) in particular meteorological data received by one or more other computers by means of a computer network (300), in particular the Internet, in particular through a suitable interface (250).
8. The system (1000) according to any one of the preceding claims, wherein said processed data comprises pedoclimatic/agronomic-type deterministic data.
9. The system (1000) according to any one of the preceding claims, wherein said agronomic-type probabilistic data refer only to plant pathologies.
10. The system (1000) according to any one of the preceding claims, wherein said agronomic-type probabilistic data refer only to plant physiologies, in particular growth and/or harvest.
11. The system (1000) according to any one of the preceding claims, wherein said server (200) is adapted to encrypt data before storing in said database (230).
12. The system (1000) according to claim 11, wherein said server (200) is adapted to encrypt by means of a key uniquely associated to said farmer or agronomist, which is stored (220B) in said server (200).
13. The system (1000) according to any one of the preceding claims, wherein said server (200) is adapted to allow users to selectively access data stored in said database (230), in particular through rules linking access rights to the identity of the accessing user; said access being possible by a computer by means of a computer network (300), in particular the Internet, in particular through a suitable interface (260).
14. The system (1000) according to any one of the preceding claims, wherein
said server (200) comprises an artificial intelligence module (270) or it is associated to an artificial intelligence system (700).
15. The system (1000) according to claim 14, wherein said server (200) is adapted to receive user data by machine learning of said artificial intelligence module (270) or said artificial intelligence system (700) in particular by one or more of said electronic apparatuses (100).
16. The system (1000) according to claim 15, wherein said user data are or comprise agronomic-type data and refer to plant pathologies and/or plant physiologies, in particular the phenological state; wherein said plant pathologies and/or physiologies are detected locally by said user.
17. System (1000) according to claim 15 or 16, wherein said user data relates to at least one parameter of plant pathology or plant physiology, in particular said parameter assuming a value chosen from a set of predetermined values.
18. The system (1000) according to claim 15 or 16 or 17, wherein said user data are or comprise images locally detected by said user.
19. The system (1000) according to any one of the preceding claims, wherein said server (200) provides a user authentication system preferably adapted to provide differentiated user rights in particular through rules linking access rights to the user identity.
20. The system (1000) according to any one of the preceding claims, comprising said plurality of electronic apparatuses (100), wherein said electronic apparatuses (100) are adapted to locally detect (130A, 130B, 130C) pedoclimatic/agronomic- type data when installed in places of an area (10) of interest of said farmer or agronomist so as to each cover a different sub-area (10-1, 10-2, 10-3), and to transmit, in particular via radio (140) and/or cable, data detected and possibly data calculated to said server (200) by means of a computer network (300), in particular the Internet, wherein data transmitted are associated to the transmitting electronic apparatus and therefore to the corresponding sub-area (10-1, 10-2, 10-3).
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PCT/IB2021/052772 WO2021224695A1 (en) | 2020-05-04 | 2021-04-02 | Electronic system for farmers and agronomists comprising a server |
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US11263707B2 (en) * | 2017-08-08 | 2022-03-01 | Indigo Ag, Inc. | Machine learning in agricultural planting, growing, and harvesting contexts |
US11055447B2 (en) * | 2018-05-28 | 2021-07-06 | Tata Consultancy Services Limited | Methods and systems for adaptive parameter sampling |
WO2019237201A1 (en) * | 2018-06-12 | 2019-12-19 | Paige Growth Technologies Inc. | Devices, systems and methods for multivariable optimization of plant growth and growth of other phototrophic organisms |
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