DESC:FIELD OF THE INVENTION
[0001] The present invention broadly relates to providing connectivity between research and farming communities and more particularly, to a system and a method for predicting crop production in a region.
BACKGROUND
[0002] For a country to become self-sustaining, it needs to have agriculture as one of its strongest pillars as it provides food and employment to many people. The agricultural output may depend on several factors such as type of soil, type of seed, fertilizer availability, pesticides, weather conditions, irrigation system, technological support and the like Often times, the farmers are not aware of various techniques that may be used to improve their crop production. The problems that farmers are facing are lack of knowledge regarding quality of seeds to be used, plant breed based on the soil type, lack of mechanization, type of fertilizers and pesticides, type of plant breed based on weather conditions and lack of access to latest technologies. These issues are largely related to lack of knowledge, among farmers and lack of proper mechanization. Also, there is a lack of access to quality information and data to scientists performing research on agriculture. Similarly, there is lack of access to intelligent, data driven decision to the farmer. Accordingly, there is a need for an online platform that is beneficial to the farmers and the scientists.
SUMMARY
[0003] The present invention’s objective is to predict crop production in a region for better yield to a farmer. The present invention provides an online platform to bridge the gap between the farmers and scientists or researchers. The platform may harness the plant information from farmer and pass it on to researchers for further investigations. Similarly, the platform may provide scientific research and insights to the farmers. Additionally, the platform provides information to the farmers for decision making and better crop rotation. The features and analytics related to crop prediction are eventually used for selecting parents for crossing to attain a crop trait (ideotype).
[0004] In an embodiment of the present invention, a system for crop management is disclosed. The invention provides an integrated platform for farmers and researchers. The system provides a decision support platform for farmers and also provides research data and platform for researchers and/or scientists. The present invention may collect phenotype data associated with crops in fields from farmers and provide this data to researchers for scientific investigation. The system may perform analysis on the phenotype data and provide insights and recommendations for better decision support to the farmers. The system may also provide suggestions regarding crop selection to the farmers based on the analysis of the phenotype data.
BRIEF DESCRIPTION OF DRAWINGS
[0005] FIG. 1 illustrates a network environment for predicting crop production in a region, in accordance with an example embodiment of the present invention;
[0006] FIG. 2 shows a block diagram of a system for predicting crop production in a region, in accordance with an example embodiment of the present invention;
[0007] FIG. 3 shows a flow diagram of a method for predicting crop production in a region, in accordance with an example embodiment of the present invention;
[0008] FIG. 4 shows exemplary scenario for predicting crop production in a region, in accordance with an example embodiment of the present invention;
[0009] FIG. 5 shows a flow diagram of method for leveraging trait selection information in experimental field trials, in accordance with an example embodiment of the present invention.
[0010] FIG. 6 shows a block diagram of a system for predicting crop trait in a farm to execute ideotype breeding according to an embodiment of the present subject matter; and
[0011] FIG. 7 illustrates a flowchart for a method for predicting crop trait in a farm to execute ideotype breeding according to an example embodiment of the present disclosure.
DETAILED DESCRIPTION
[0012] The drawings accompanied herein constitute a part of this disclosure. Reference numerals refer to same parts throughout the different diagrams. Components in the diagrams are not necessarily to scale. Some diagrams may indicate the components of block diagrams and may not represent internal circuitry of each component. The diagrams are provided herein for understanding purpose of the disclosure.
[0013] Throughout the following description, numerous references may be made regarding servers, services, or other systems formed from computing devices. It should be appreciated that the use of such terms is deemed to represent one or more computing devices having at least one processor configured to or programmed to execute software instructions stored on a computer readable tangible, non-transitory medium or also referred to as a processor readable medium. For example, a server system can include one or more computers operating as a web server, data source server, or other type of computer server in a manner to fulfill described roles, responsibilities, or functions. Within the context of this document, the disclosed modules are also deemed to comprise computing devices having a processor and a non-transitory memory storing instructions executable by the processor that cause the device to control, manage, or otherwise manipulate the features of the devices or systems.
[0014] The embodiments are described herein for illustrative purposes and are subject to many variations. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient but are intended to cover the application or implementation without departing from the spirit or the scope of the present disclosure. Further, it is to be understood that the phraseology and terminology employed herein are for the purpose of the description and should not be regarded as limiting. Any heading utilized within this description is for convenience only and has no legal or limiting effect.
[0015] FIG. 1 illustrates an agricultural environment 100 for predicting crop production in a region, in accordance with an example embodiment of the present invention. The agricultural environment 100 may comprise a farming system 101 communicatively coupled to a user device 103 having an application 103a installed, a drone 105, and a database 107 connected via a network 109. The components described in the agricultural environment 100 may be further broken down into more than one component such as one or more sensors or application in the user device 103 and/or combined together in any suitable arrangement. Further, it is possible that one or more components may be rearranged, changed, added, and/or removed.
[0016] In an example embodiment, the farming system 101 may be embodied in one or more of several ways as per the required implementation. For example, the farming system 101 may be embodied as a cloud based service or a cloud based platform. As such, the system 101 may be configured as an interface between farmers and researchers and/or scientists. In an embodiment, the farming system 101 may be integrated platform for farmers and researchers. In each of such embodiments, the farming system 101 may be communicatively coupled to the components shown in FIG. 1 to carry out the desired operations and wherever required modifications may be possible within the scope of the present disclosure. In an embodiment, the farming system 101 may be configured to collect data from farmers and store in the database 107. In some embodiments, the farming system 101 may provide data stored in the database 107 to the researchers for further analysis.
[0017] In an embodiment, the user device 103 may be any user accessible device such as a mobile phone, a smartphone, a portable computer, and the like. In an embodiment, the user device 103 may also be referred to as farmer device. The farmer may use different image capturing applications 103a of the user device 103 to collect data and samples from the field by capturing images and videos. The data captured by the user device 103 may be transmitted to the farming system 101 via the network 109 for further processing. In some example embodiments, the user device 103 may be associated, coupled, or otherwise integrated with a vehicle (such as a tractor that may be used by the farmer in the field). In some example embodiments, the user device 103 may comprise processing means such as a central processing unit (CPU), storage means such as on-board read only memory (ROM) and random access memory (RAM), acoustic sensors such as a microphone array, position sensors such as a GPS sensor, gyroscope, a LIDAR sensor, a proximity sensor, motion sensors such as accelerometer, a display enabled user interface such as a touch screen display, and other components as may be required for specific functionalities of the user device 103. Additional, different, or fewer components may be provided.
[0018] In another embodiment, the data associated with the field may also be collected by drone 105. The drone 105 may capture an image of the aerial view of the crops in field and transmit the image to the farming system 101 via the network 109. In some embodiments, satellite image data from satellite (not shown in figure) may also be used to collect data associated with the crops in fields. The data captured by the satellite may be transmitted to the farming system 101 for further processing. In another embodiment, the satellite data may also be stored in the database 107.
[0019] In some example embodiments, the database 107 may be connected to the farming system 101 via the network 109. The database 107 may be an external database or an internal database that may be used to store data associated with the one or more crops in field that is being captured by the user device 103 and/or the drone 105. The database 107 may store collected data associated with one or more of an image or video content associated with the crops in the field captured by the user device 103 and/or the drone 105, which may be used for processing by the farming system 101.
[0020] The network 109 may comprise suitable logic, circuitry, and interfaces that may be configured to provide a plurality of network ports and a plurality of communication channels for transmission and reception of data. Each network port may correspond to a virtual address (or a physical machine address) for transmission and reception of the communication data. For example, the virtual address may be an Internet Protocol Version 4 (IPv4) (or an IPv6 address) and the physical address may be a Media Access Control (MAC) address. The network may be associated with an application layer for implementation of communication protocols based on one or more communication requests from at least one of the one or more communication devices. The communication data may be transmitted or received, via the communication protocols. Examples of such wired and wireless communication protocols may include, but are not limited to, Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), File Transfer Protocol (FTP), ZigBee, EDGE, infrared (IR), IEEE 802.11, 802.16, cellular communication protocols, and/or Bluetooth (BT) communication protocols.
[0021] Examples of the network 109 may include, but is not limited to a wireless channel, a wired channel, a combination of wireless and wired channel thereof. The wireless or wired channel may be associated with a network standard which may be defined by one of a Local Area Network (LAN), a Personal Area Network (PAN), a Wireless Local Area Network (WLAN), a Wireless Sensor Network (WSN), Wireless Area Network (WAN), Wireless Wide Area Network (WWAN), a Long Term Evolution (LTE) network, a plain old telephone service (POTS), and a Metropolitan Area Network (MAN). Additionally, the wired channel may be selected on the basis of bandwidth criteria. For example, an optical fiber channel may be used for a high bandwidth communication. Further, a coaxial cable-based or Ethernet-based communication channel may be used for moderate bandwidth communication.
[0022] Referring now to FIG. 2, a block diagram of the farming system 101 of FIG. 1 for predicting crop production in a region, in accordance with an example embodiment of the present invention. The farming system 101 comprises one or more processors, such as a processor 201, a memory 203, and a communication interface 205.
[0023] The processor 201 may comprise suitable logic, circuitry, and interfaces that may be configured to execute set of instructions stored in the memory 203 for connecting the farmer user device with the researchers using digital service applications. In some example embodiments, the processor 201 may obtain location of the user device 103 based on the geo-locations or core-location framework features of the user device 103. The processor 201 may be embodied in a number of different ways. For example, the processor 201 may be embodied as one or more of various hardware processing means such as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), a processing element with or without an accompanying DSP, or various other processing circuitry including integrated circuits such as, for example, an ASIC (application specific integrated circuit), an FPGA (field programmable gate array), a microcontroller unit (MCU), a hardware accelerator, a special-purpose computer chip, or the like. As such, in some embodiments, the processor 201 may include one or more processing cores configured to perform independently.
[0024] Examples of the processor 201 may be an Application-Specific Integrated Circuit (ASIC) processor, a Complex Instruction Set Computing (CISC) processor, a central processing unit (CPU), an Explicitly Parallel Instruction Computing (EPIC) processor, a Very Long Instruction Word (VLIW) processor, and/or other processors or circuits. The processor 201 may implement a number of processor technologies known in the art such as artificial intelligence (AI) models, or the like. As such, in some embodiments, the processor 201 may include one or more processing cores configured to perform independently. A multi-core processor may enable multiprocessing within a single physical package.
[0025] Additionally or alternatively, the processor 201 may include one or more processors configured in tandem via the bus to enable independent execution of instructions, pipelining and/or multithreading. Additionally or alternatively, the processor 201 may include one or processors capable of processing large volumes of workloads and operations to provide support for big data analysis. However, in some cases, the processor 201 may be a processor specific device (for example, a mobile terminal or a fixed computing device) configured to employ an embodiment of the disclosure by further configuration of the processor 201 by instructions for performing the algorithms and/or operations described herein.
[0026] The memory 203 may comprise suitable logic, circuitry, and interfaces that may be configured to store the set of instructions for connecting the user with the digital service provider, a machine code and/or instructions executable by the processor 201. Additionally, the set of instructions may include program codes corresponding to artificial intelligence techniques for connecting the user with the digital service provider. The memory 203 may be non-transitory and may include, for example, one or more volatile and/or non-volatile memories. For example, the memory 203 may be an electronic storage device (for example, a computer readable storage medium) comprising gates configured to store data (for example, bits) that may be retrievable by a machine (for example, a computing device like the processor 201). The memory 203 may be configured to store information, data, content, applications, instructions, or the like, for enabling the apparatus to carry out various functions in accordance with an example embodiment of the present invention. For example, the memory 203 may be configured to store information including processor instructions for connecting the user with the service provider using AI applications. Examples of implementation of the memory 203 may include, but are not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Hard Disk Drive (HDD), a Solid-State Drive (SSD), a CPU cache, and/or a Secure Digital (SD) card.
[0027] The communication interface 205 may comprise input interface and output interface for supporting communications to and from any component with which the farming system 101 may communicate. The communication interface 205 may be any means such as a device or circuitry embodied in either hardware or a combination of hardware and software that is configured to receive and/or transmit data to/from a communications device in communication with the user device 103. In this regard, the communication interface 205 may include, for example, an antenna (or multiple antennae) and supporting hardware and/or software for enabling communications with a wireless communication network. Additionally or alternatively, the communication interface 205 may include the circuitry for interacting with the antenna(s) to cause transmission of signals via the antenna(s) or to handle receipt of signals received via the antenna(s). In some environments, the communication interface 205 may alternatively or additionally support wired communication. As such, for example, the communication interface 205 may include a communication modem and/or other hardware and/or software for supporting communication via cable, digital subscriber line (DSL), universal serial bus (USB) or other mechanisms.
[0028] FIG. 3 shows a flow diagram of a method 300 for predicting crop production in a region, in accordance with an example embodiment of the present invention. In an embodiment, the region may be associated with a field, or a map location.
[0029] It will be understood that each block of the flow chart 300 may be implemented by various means, such as hardware, firmware, processor, circuitry, and/or other communication devices associated with execution of software including one or more computer program instructions. For example, one or more of the procedures described above may be embodied by computer program instructions. In this regard, the computer program instructions which embody the procedures described above may be stored by a memory of a system, employing an embodiment of the present invention and executed by a processor. As will be appreciated, any such computer program instructions may be loaded onto a computer or other programmable apparatus (for example, hardware) to produce a machine, such that the resulting computer or other programmable apparatus implements the functions specified in the flow diagram blocks. These computer program instructions may also be stored in a computer-readable memory that may direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture the execution of which implements the function specified in the flowchart blocks. The computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operations to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide operations for implementing the functions specified in the flow chart blocks.
[0030] Accordingly, blocks of the flow chart support combinations of means for performing the specified functions and combinations of operations for performing the specified functions. It will also be understood that one or more blocks of the flow diagram, and combinations of blocks in the flow chart, may be implemented by special purpose hardware-based computer systems which perform the specified functions, or combinations of special purpose hardware and computer instructions. According to some embodiments, the method 300 may include steps 301-305.
[0031] The method flow 300 starts at step 301. At step 301, the farming system 101 may obtain phenotype data and autonomous data associated with the crops in the region. In an embodiment, “crops” and “plants” may be interchangeably used. Similarly, “region” and “field” may also be used interchangeably. The farmers in the field may capture images of the plants by using mobile devices or web based applications. The captured images may be transmitted to the farming system 101 to obtain the phenotype data of the plants. In an embodiment, the phenotype data may include phenotypic features such as geometrical and physiological and growth related features of the plants. The features that may be obtained from phenotype data are, but not limited to, height of plants, size of leaves, shape of leaves, color of leaves, leaf texture, stem length, stem thickness, grain size, spike length, information about stomata, and root surface area at maturity and the like.
[0032] The farming system 101 may also obtain autonomous data associated with the fields using the drone 105. In another embodiment, along with the drone, the satellite images captured using satellites may also be used to capture the aerial view of the field. The drone 105 may provide geospatial data associated with the field. The drone may also provide data associated with field elevation. In an embodiment, the autonomous data may include data on storms or atmospheric patterns in the area where the field is located. In another embodiment, the autonomous data may also include the health, stress and yield of crops. Additionally, the autonomous data may also include soil conditions in the field, for example, type of soil, soil pH, and moisture in the soil. In an embodiment, the drone 105 may also acquire images that may assist in sensing vegetation health and identify areas in the fields that are nitrogen deficient through a process known as Normalized Difference Vegetation Index (NDVI).
[0033] In an embodiment, data may also be obtained from other sources such as meteorological department that may provide information of lighting, humidity, temperature, soil moisture in the area where the field is located, by the farming system 101. In an embodiment, the farming system 101 may store the collected data in the database 107.
[0034] At step 303, analysing the obtained phenotype data and autonomous data using genome phenome analysis and using machine learning algorithms is performed by the farming system 101. In an embodiment, the farming system 101 may first set data configuration based on color, shape structure, genotype and the like. Further, the farming system 101 may filter the collected data by detecting outliers in the data to select important traits of the plant. The farming system 101 may further select feature based on the filtered data. The feature selection and analysis may be performed by machine learning algorithms. The algorithms that may be used for analysis and building models may be one or more of random forest, gradient boosting, logistic regression or the like. The farming system 101 may provide the collected data that is stored in the database 107 to the researchers for further analysis. The farming system 101 may collect data, organize, and implement different data mining tools, statistical methods, mathematical formulations on the data for further analysis and modeling.
[0035] At step 305, the farming system 101 may provide prediction of estimated yield to the farmers, based on the analysis conducted on the phenotype data provided by the farmers and autonomous data provided by the drone 105. The farming system 101 may provide decision support platform to the farmers by informing them about different growth factors affecting yield of crops in the field. For example, the researchers may provide suggestion to the farmers about best type of plant breed based on the different weather conditions, and soil conditions. The farming system 101 may also provide agricultural prescription regarding trait selection of plant breed based on genotype and phenotype data, so that it may help the farmers for better crop production. The farming system 101 may also provide suggestions regarding type of pesticide or fertilizers to be used by the farmers based on the soil type. The farming system 101 may also provide suggestion on fertilizers to treat the health and stress related problems of the plants. The farming system 101 may also provide post-harvest management techniques to the farmers.
[0036] FIG. 4 shows exemplary scenario of the farming system 101 for predicting crop production in a region, in accordance with an example embodiment of the present invention. There is shown a field 401, a farmer 403, the farmer holding a mobile device 405 (similar to the user device 103), a farm tractor 407 plough and a drone 409 (similar to the drone 105) to capture aerial view of the field 401.
[0037] In an embodiment, as shown in FIG. 4, the farm tractor 407 is cultivating the field 401 and the farmer 403 may capture plurality of images associated with the field 401 by using applications 103a installed in the mobile device 405. The obtained data may include collection of captured images associated with the plants in the field 401. The captured images may be provided by the farming system 101 to the researchers for further investigations and inputs. The investigations by researchers may include determining the geographical and physiological information related to soil color, plant size, leaf size, shape of leaves, color of leaves, stem thickness, weather condition and the like. Further, the aerial data of the field may also be collected using drone 409 or satellites (not shown in Figure). The images or video collected by drones 409 may be used to obtain data associated with type of soil, soil pH, moisture in the soil, weather conditions in the area, elevation of the field and the like.
[0038] The farming system 101 may perform data analytics on the obtained data for predicting growth factors and to estimate the yield of crops or plants in field, using genome-phenome analysis. The data analytics is performed using different machine learning algorithms. In the genome phenome analysis, the farming system 101 acquires plurality of images associated with plants in the field 401.The farming system 101 may further use image processing to convert the data into trait (feature) metrics associated with phenotype and genotype data. Further, the farming system 101 may extract patterns, data assimilation, trait (feature) identification using machine learning algorithms. In an embodiment, one or more machine learning algorithms may be used for feature identification and/or selection such as random forest, gradient boosting, and logistic regression, support vector machine or the like. In an embodiment, the random forest algorithm may be applied for gene selection and disease prediction from the dataset. Similarly, in other embodiment, support vector machine may be used for image classification, stress identification in plants based on image features. In another embodiment, machine learning tools may be used to classify color related traits of plants based on the autonomous data and the phenotype data. The autonomous data and the phenotype data are used for performing data analytics. The farming system 101 may also combine inputs provided by the researchers to determine analytical insights. The farming system 101 may further provide decision support to the farmers, based on the analytical insights gathered from analysis of the autonomous data and the phenotype data. The farming system 101 may also provide prediction of the estimated yield.
[0039] FIG. 5 shows a flow diagram of method 500 for leveraging trait selection information in experimental field trials, in accordance with an example embodiment of the present invention. The method is implemented by the farming system 101 to assist agricultural researchers to perform research by leveraging the farming system 101. The agricultural researchers may utilize the farming system 101 to perform experimental designs. The farming system 101 provides an interface for the researchers or scientists for efficiently designing experiments and field trials. The interface may comprise a plurality of designs so that the researchers can choose any design that is best suited for the experiment. The interface of the farming system 101 may be an integrated interface where the researchers have access to data obtained from the farmers and the farmers have access to the outcome of the experimental trials performed by the researchers. The interface of the farming system 101 may also provide plurality models that can be used for analysis of data. The researchers may utilize the models for experimental trials. The farming system 101 may also provide data obtained from genome phenome analysis such that the researchers may utilize the genome phenome data for designing experiments and field trials. An example method of utilizing genome phenome analysis for designing experimental trials is explained below.
[0040] The method flow 500 starts at step 501. At step 501, the farming system 101 may obtain trait or plant breed selected for a field based on the genome phenome analysis. After the analysis and prediction using different models, at step 502, the farming system 503 may assist researchers to perform field trails based on the obtained trait or plant breed in realistic conditions. In an embodiment, the field trails are conducted to validate the selected trait in realistic field condition. The farming system 101 may validate the plant breed or trait selected in a field by performing experiments under different environmental parameters. For example, experimenting by monitoring growth and yield under different weather conditions and analyzing the changes in the growth, monitoring yield in different soil types. Sometimes, insects, diseases, and weeds account for losses in crop yields and animal health. Therefore, the farming system 101 may also assist in recognizing the diseases and stress in the plants based on the experimental field trails. Based on outcome of the experimental field trials performed by the researchers, the farming system 101 may provide suggestions and decisions to the farmers for better crop production.
[0041] FIG. 6 shows a block diagram of a system 600 for predicting crop trait in a farm to execute ideotype breeding according to an embodiment of the present subject matter. In some embodiments, the farm may include but not limited to vertical farming implementation, field, greenhouse, agri-trials etc. and may be used interchangeably as per the application attributes. The system 600 may be similar to the systems used in above description and may also have same or similar components as used in the systems described above. In the embodiment of FIG. 6, the system 600 comprises a sensing module 602 to obtain inputs pertaining to at least one of phenotype data having one or more features, genomic data, and autonomous data associated with the crops in at least partial region of the farm. The sensing module 602 is further configured to obtain weather forecast data comprising values of air temperature, humidity, wind speed, air pressure, air density, and surface temperature wind speed radiation etc. The sensing module 602 may include but not limited to a camera sensor, a microphone array, a GPS sensor, a gyroscope, a LIDAR sensor, a proximity sensor, and an accelerometer.
[0042] The system 600 further comprises an input module 604 communicably coupled to the sensing module 602 to receive the inputs pertaining to at least one of the phenotype data, genomic data and the autonomous data. The input module 604 is configured to receive the inputs pertaining to at least one of the phenotype data, genomic data the autonomous data, and geospatial data associated with the field via an unmanned aerial vehicle, and the unmanned aerial vehicle comprises one or more sensors to identify one or more regions-of-interest of the farm. The unmanned aerial vehicle (UAV) acquires images to identify at least one nitrogen deficient region of the farm. In an embodiment, the UAV may be replaceable with any other robotic sensing device. The system 600 further comprises a processor 606 communicably coupled to the input module. The processor 606 is configured to process the received inputs by implementing a machine learning model 608. The machine learning model 608 analyzes the at least one of phenotype data, genomic data and autonomous data to create assistance data for ideotype breeding; and predict an estimated crop trait based on the genome-phenome analysis. In an example, the assistance data may be used by the researchers for experimental trials. The machine learning model 608 includes one or more of a random forest, gradient boosting, logistic regression, and support vector machine. The system 600 further comprises a communication interface 610 communicably coupled to the sensing module 602 to receive and forward the inputs to the input module 604. The machine learning model 608 captures at least one of index values to obtain information pertaining to vegetation health in the at least one nitrogen deficient region. The machine learning model 608 is further executed by the processor 606 to perform one or more actions based on the obtained inputs. The one or more actions include identifying traits in plants in a region of interest of the farm; notifying to a cloud control server about the identification of the traits in the plants; determining a response action based on a type of trait of the identified traits; and activating the response action based on the type of trait.
[0043] In an example, the machine learning model 608 may obtain a maximum value and a minimum value of a probability index related to identifying traits in plants in a region of interest of the farm. The minimum value may be subtracted from the maximum value to obtain a maximum variation range of the probability index. Further, one or more topography factors related to farmland may be extracted and correlated to a landform classification from digital elevation model data in a target area. A minimum value of soil infiltration rate may also be calculated to understand real-time time values related to the soil moisture and temperature.
[0044] FIG. 7 illustrates a flowchart for a method 700 for predicting crop trait in a farm to execute ideotype breeding according to an example embodiment of the present disclosure. The method may be performed by the system 600 described in FIG. 6 of the present disclosure. The method 700 may be implemented by a processing resource or the system 600 through any suitable hardware, non-transitory machine-readable medium, or a combination thereof. In some embodiments, steps involved in the method 700 may be executed by the processing resource, for example the processor 606 (shown in FIG. 6). The processor 606 may be in communication with additional components. The processor 606 may include one or more components operable to execute computer executable instructions or computer code embodied in the memory. The method 700 will be described with reference to FIGS. 1 to 6.
[0045] Referring to FIG. 6, At step 702 the method 700 comprises obtaining, via a sensing module, inputs pertaining to at least one of phenotype data having one or more features, genomic data and autonomous data associated with the crops in at least partial region of the farm.
[0046] At step 704 the method 700 comprises receiving, via an input module the inputs pertaining to at least one of the phenotype data, genomic data and the autonomous data.
[0047] At step 706 the method 700 comprises processing, via the processor, the received inputs by implementing a machine learning model to analyze the at least one of phenotype data, genomic data and autonomous data to create assistance data for ideotype breeding; and predict an estimated crop production based on the genome-phenome analysis.
[0048] In an embodiment, the method 700 comprises receiving the inputs pertaining to at least one of the phenotype data, genomic data, the autonomous data, and geospatial data associated with the field via an unmanned aerial vehicle, obtaining, via the sensing module weather forecast data comprising values of air temperature, humidity, wind speed, air pressure, air density, and surface temperature, and wherein the unmanned aerial vehicle comprises one or more sensors to identify one or more regions-of-interest of the farm.
[0049] In an embodiment, the method 700 comprises acquiring images via the unmanned aerial vehicle to identify at least one nitrogen deficient region of the farm.
[0050] In an embodiment, the method 700 comprises capturing a at least one of index values, via the machine learning model to obtain information pertaining to vegetation health in the at least one nitrogen deficient region.
[0051] In an embodiment, the method 700, via the machine learning model comprises performing one or more actions based on the obtained inputs, wherein the one or more actions include identifying traits in plants in a region of interest of the farm; notifying to a cloud control server about the identification of the traits in the plants; determining a response action based on a type of trait of the identified traits; and activating the response action based on the type of trait.
[0052] Various embodiments of the present invention disclose a method and a system for crop management. The present invention provides bridges the gap between researchers and farmers by providing a platform where both the communities may harness information from each other. The researcher may provide decision support to farmers along with suggestion of better plant breeds based on the different conditions in the field. The present invention helps to get new agriculture products in the market faster because of better crop production. The present invention may also save time and cost of farmers and scientists. Additionally, the present invention may also provide machine learning based predictive analytics. The present invention may provide a platform for a comprehensive view of crop performance and logistics to make informed decisions. The present invention may also provide suggestions of mechanization tools to be used for better crop production.
,CLAIMS:1. A system (600) for predicting crop traits in a farm to execute ideotype breeding, the system (600) comprising:
a sensing module (602) to obtain inputs pertaining to at least one of phenotype data having one or more features, genomic data, and autonomous data associated with the crops in at least partial region of the farm;
an input module (604) communicably coupled to the sensing module (602) to receive the inputs pertaining to at least one of the phenotype data, genomic data and the autonomous data; and
a processor (606) communicably coupled to the input module (604), wherein the processor (606) is configured to process the received inputs by implementing a machine learning model (608) to:
analyze the at least one of phenotype data, genomic data and autonomous data to create assistance data for ideotype breeding; and
predict an estimated crop trait based on the genome-phenome analysis.

2. The system (600) as claimed in claim 1, wherein the system (600) further comprises a communication interface (610) communicably coupled to the sensing module (602) to receive and forward the inputs to the input module (604).

3. The system (600) as claimed in claim 1, wherein the machine learning model (608) includes one or more of a random forest, gradient boosting, logistic regression, and support vector machine.

4. The system (600) as claimed in claim 1, wherein the phenotype data comprises geometrical growth and physiological growth related features of plants.

5. The system (600) as claimed in claim 1, wherein the geospatial data comprises one or more of a weather data, health of crops, stress of crops, yield of crops, soil pH, moisture in soil, and elevation of a field.

6. The system (600) as claimed in claim 1, wherein the sensing module (602) is one or more of a camera sensor, a microphone array, a GPS sensor, a gyroscope, a LIDAR sensor, a proximity sensor, and an accelerometer.

7. The system (600) as claimed in claim 1, wherein the input module (604) is configured to receive the inputs pertaining to at least one of the phenotype data, genomic data the autonomous data, and geospatial data associated with the field via an unmanned aerial vehicle, wherein the sensing module (602) is further configured to obtain weather data comprising values of air temperature, humidity, wind speed, air pressure, air density, and surface temperature, and wherein the unmanned aerial vehicle comprises one or more sensors to identify one or more regions-of-interest of the farm.

8. The system (600) as claimed in claim 7, wherein the unmanned aerial vehicle acquires images to identify at least one nitrogen deficient region of the farm.

9. The system (600) as claimed in one of claims 1 to 8, wherein the machine learning model (608) is to capture a at least one of index values to obtain information pertaining to vegetation health in the at least one nitrogen deficient region.

10. The system (600) as claimed in claim 1, wherein the machine learning model (608) is further executed by the processor (606) to perform one or more actions based on the obtained inputs, wherein the one or more actions include:
identifying traits in plants in a region of interest of the farm;
notifying to a cloud control server about the identification of the traits in the plants;
determining a response action based on a type of trait of the identified traits; and
activating the response action based on the type of trait.

11. The system (600) as claimed in claim 1, wherein the one or more features of the phenotype data comprises, height of plants, size of leaves, shape of leaves, color of leaves, leaf texture, stem length, stem thickness, grain size, spike length, information about stomata, and root surface area at maturity.

12. A method for predicting crop traits in a farm to execute ideotype breeding, the method comprising:
obtaining, via a sensing module (602), inputs pertaining to at least one of phenotype data having one or more features, genomic data and autonomous data associated with the crops in at least partial region of the farm;
receiving, via an input module (604) the inputs pertaining to at least one of the phenotype data, genomic data and the autonomous data; and
processing, via the processor (606), the received inputs by implementing a machine learning model (608) to:
analyze the at least one of phenotype data, genomic data and autonomous data to create assistance data for ideotype breeding; and
predict an estimated crop trait based on the genome-phenome analysis.

13. The method as claimed in claim 12, wherein the method comprises:
receiving the inputs pertaining to at least one of the phenotype data, genomic data, the autonomous data, and geospatial data associated with the field via an unmanned aerial vehicle; and
obtaining, via the sensing module (602) weather data comprising values of air temperature, humidity, wind speed, air pressure, air density, and surface temperature
wherein the unmanned aerial vehicle comprises one or more sensors to identify one or more regions-of-interest of the farm.

14. The method as claimed in claim 13, wherein the method comprises acquiring images via the unmanned aerial vehicle to identify at least one nitrogen deficient region of the farm.

15. The method as claimed in one of claims 12 to 14, wherein the method comprises capturing a at least one of index values, via the machine learning model (608) to obtain information pertaining to vegetation health in the at least one nitrogen deficient region.

16. The method as claimed in claim 12, wherein the method, via the machine learning model (608) comprises performing one or more actions based on the obtained inputs, wherein the one or more actions include:
identifying traits in plants in a region of interest of the farm;
notifying to a cloud control server about the identification of the traits in the plants;
determining a response action based on a type of trait of the identified traits; and
activating the response action based on the type of trait.