1. A system for soil and plant health analysis, comprising: an electromagnetic induction (EMI) unit designed to emit and receive electromagnetic waves, capturing soil and plant responses; a machine learning processor to analyze the captured responses for assessing soil quality and plant health; and a geographic information systems (GIS) interface to visually represent the analyzed data across geographic landscapes.

2. The system of claim 1, wherein the EMI unit comprises multiple frequency bands to capture a broad range of soil and plant electromagnetic responses for a comprehensive analysis.

3. The system of claim 1, further comprising a data storage module to store historic soil and plant health data, enabling the machine learning processor to leverage longitudinal data for enhanced predictive analytics.

4. The system of claim 1, wherein the GIS interface offers a layered visualization approach, allowing users to segregate and visualize soil quality and plant health separately or in a combined overlay.

5. The system of claim 1, further comprising connectivity interfaces enabling integration with external agricultural tools or platforms for holistic farm management based on the analyzed data.

6. A method for soil and plant health analysis using Electromagnetic Induction (EMI) and Machine Learning, comprising the steps of: transmitting electromagnetic waves into the soil and plants using EMI techniques; capturing the electromagnetic responses from the soil and plants; analyzing the captured responses using machine learning algorithms to determine soil quality and plant health metrics; and visually representing these metrics over geographic terrains using a GIS platform.

7. The method of claim 6, further comprising the step of calibrating the EMI unit across multiple frequency bands, allowing for a broad-spectrum capture of electromagnetic responses from varied soil depths and plant types.

8. The method of claim 6, incorporating the step of storing the determined metrics in a database, subsequently utilizing this historical data for trend analysis and predictive assessments in future analyses.

9. The method of claim 6, further comprising the step of layering the GIS visualizations, enabling users to view soil and plant metrics independently or in combination, providing insights into the interplay between soil health and plant vitality.

10. The method of claim 6, further comprising the step of integrating the derived soil and plant health insights with external agricultural management systems, facilitating optimized irrigation, fertilization, and cropping decisions based on comprehensive health data. Soil and Plant health analysis using Electromagnetic Induction (EMI) techniques and Machine Learning for soil quality assessment, Plant Health Monitoring, and Geographic information systems (GIS) visualization Abstract The invention presents a comprehensive system for soil and plant health analysis. Employing an electromagnetic induction (EMI) unit, the system transmits and receives electromagnetic waves to gauge soil and plant responses, providing a non-invasive methodology to gauge their inherent properties. An integrated machine learning processor delves into the captured data, assessing the nuanced metrics of soil quality and plant vitality by discerning intricate patterns and variations. To enhance interpretability and application, a geographic information systems (GIS) interface is incorporated, offering users a spatial visualization of the derived metrics across vast landscapes. This amalgamation of EMI, machine learning, and GIS ushers in a novel approach to agricultural monitoring, marrying precision with user-centric data representation. , Claims:Claims :

1. A system for soil and plant health analysis, comprising: an electromagnetic induction (EMI) unit designed to emit and receive electromagnetic waves, capturing soil and plant responses; a machine learning processor to analyze the captured responses for assessing soil quality and plant health; and a geographic information systems (GIS) interface to visually represent the analyzed data across geographic landscapes.

2. The system of claim 1, wherein the EMI unit comprises multiple frequency bands to capture a broad range of soil and plant electromagnetic responses for a comprehensive analysis.

3. The system of claim 1, further comprising a data storage module to store historic soil and plant health data, enabling the machine learning processor to leverage longitudinal data for enhanced predictive analytics.

4. The system of claim 1, wherein the GIS interface offers a layered visualization approach, allowing users to segregate and visualize soil quality and plant health separately or in a combined overlay.

5. The system of claim 1, further comprising connectivity interfaces enabling integration with external agricultural tools or platforms for holistic farm management based on the analyzed data.

6. A method for soil and plant health analysis using Electromagnetic Induction (EMI) and Machine Learning, comprising the steps of: transmitting electromagnetic waves into the soil and plants using EMI techniques; capturing the electromagnetic responses from the soil and plants; analyzing the captured responses using machine learning algorithms to determine soil quality and plant health metrics; and visually representing these metrics over geographic terrains using a GIS platform.

7. The method of claim 6, further comprising the step of calibrating the EMI unit across multiple frequency bands, allowing for a broad-spectrum capture of electromagnetic responses from varied soil depths and plant types.

8. The method of claim 6, incorporating the step of storing the determined metrics in a database, subsequently utilizing this historical data for trend analysis and predictive assessments in future analyses.

9. The method of claim 6, further comprising the step of layering the GIS visualizations, enabling users to view soil and plant metrics independently or in combination, providing insights into the interplay between soil health and plant vitality.

10. The method of claim 6, further comprising the step of integrating the derived soil and plant health insights with external agricultural management systems, facilitating optimized irrigation, fertilization, and cropping decisions based on comprehensive health data.

Description:Soil and Plant health analysis using Electromagnetic Induction (EMI) techniques and Machine Learning for soil quality assessment, Plant Health Monitoring, and Geographic information systems (GIS) visualization
Field of the Invention
[0001] The present invention relates generally to the fields of agriculture and environmental monitoring. More specifically, the invention pertains to a novel system and method for assessing soil quality and plant health by leveraging Electromagnetic Induction (EMI) techniques in conjunction with Machine Learning algorithms. The inventive approach facilitates the capture, analysis, and interpretation of electromagnetic responses from soil and plants. Additionally, this invention integrates Geographic Information Systems (GIS) for the visual representation of soil and plant health metrics, offering a spatial understanding of the agricultural landscape. This combined approach is designed to offer enhanced insights into the status and health of the soil and plants, assisting in precision agriculture and effective land management.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Soil health and plant vitality are fundamental determinants of agricultural productivity. Historically, understanding the intricacies of the soil and monitoring plant health has been a labour-intensive and often imprecise endeavour, relying on manual sampling and visual inspections. This classical approach was fraught with limitations, often resulting in generalizations rather than specific, localized assessments of soil quality and plant health.
[0004] With the advent of technology, several advancements were made in the domain of soil and plant health analysis. Electromagnetic Induction (EMI) techniques emerged as a transformative method, offering a non-invasive means to probe the soil's properties. EMI operates by transmitting electromagnetic waves into the soil, with sensors then capturing the soil's electromagnetic responses. These responses can provide valuable insights into soil moisture, salinity, and texture. Prior art in this field, such as the work by Rhoades et al. (1993), demonstrated how EMI could be employed to map soil salinity across large fields, providing farmers with crucial data to tailor their irrigation strategies.
[0005] While EMI addressed the soil assessment dimension, plant health monitoring often remained a distinct, separate challenge. Preliminary solutions involved spectral imaging, where different wavelengths of light reflected by plants could indicate their health status. However, this method often lacked depth, focusing primarily on surface-level observations.
[0006] The integration of Machine Learning (ML) presented a paradigm shift. With the ability to process and analyze vast datasets, ML algorithms could identify intricate patterns and subtle variations in the electromagnetic responses captured by EMI. Prior research, such as the study by Jones et al. (2015), highlighted the potential of coupling EMI data with ML models to predict various soil properties, including organic carbon content and pH levels. Similarly, Zhang et al. (2018) demonstrated that ML could decipher the nuanced spectral signatures of plants, thereby offering insights into plant stressors and health metrics.
[0007] However, an evident gap in these advancements was the absence of an intuitive visualization mechanism. While EMI and ML provided raw data and analysis, understanding and interpreting this data in a spatial context remained a challenge. This is where Geographic Information Systems (GIS) made a profound impact. GIS platforms, as showcased in studies like Brevik et al. (2016), enabled the mapping of soil and plant health metrics across vast geographies, presenting data in layered, interactive maps. Such visualizations allowed farmers, agronomists, and land managers to pinpoint specific areas of concern, aiding in data-driven decision-making.
[0008] In conclusion, while each of these technologies—EMI, ML, and GIS—made significant strides individually, the holistic integration of all three has been a relatively unexplored territory. The combined potential of these technologies promises a comprehensive, efficient, and user-friendly approach to soil quality assessment, plant health monitoring, and geospatial visualization, addressing the challenges that were hitherto unresolved by individual technological solutions.
[0009]
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[00011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00012] The present invention relates generally to the fields of agriculture and environmental monitoring. More specifically, the invention pertains to a novel system and method for assessing soil quality and plant health by leveraging Electromagnetic Induction (EMI) techniques in conjunction with Machine Learning algorithms. The inventive approach facilitates the capture, analysis, and interpretation of electromagnetic responses from soil and plants. Additionally, this invention integrates Geographic Information Systems (GIS) for the visual representation of soil and plant health metrics, offering a spatial understanding of the agricultural landscape. This combined approach is designed to offer enhanced insights into the status and health of the soil and plants, assisting in precision agriculture and effective land management.
[00013] The proposed system for soil and plant health analysis presents a revolutionary approach to precision agriculture by combining cutting-edge technologies to provide comprehensive insights into soil quality and plant health. At its core, the system features an electromagnetic induction (EMI) unit that emits and receives electromagnetic waves, capturing intricate responses from both soil and plants. This captured data serves as the foundation for a sophisticated analysis conducted by a machine learning processor, leading to an accurate assessment of soil conditions and plant vitality.
[00014] One of the standout features of the EMI unit is its incorporation of multiple frequency bands. This innovative design allows the system to gather a diverse array of electromagnetic responses, resulting in a thorough and detailed analysis. By encompassing a broad spectrum of frequencies, the system can unravel a wealth of information that would have been overlooked by traditional approaches.
[00015] Complementing the EMI unit is the machine learning processor, a powerhouse of computational intelligence. This processor meticulously dissects the captured responses, sifting through intricate patterns and correlations that are beyond the capabilities of human observation. Through advanced algorithms, the processor deciphers the data to provide accurate insights into soil quality and plant health. The system's capabilities are further enhanced by a data storage module, which retains historical soil and plant health data. This historical data empowers the machine learning processor to engage in predictive analytics, enabling farmers to anticipate trends and make informed decisions that optimize agricultural practices.
[00016] A critical aspect of the system's functionality lies in its geographic information systems (GIS) interface. This interface takes the analyzed data and translates it into visually intuitive representations across geographical landscapes. The GIS interface offers users the flexibility to view soil quality and plant health separately or combined as an overlay, providing a multifaceted perspective that aids decision-making.
[00017] Moreover, the system's adaptability is showcased through its connectivity interfaces, facilitating integration with external agricultural tools and platforms. This integration paves the way for holistic farm management, as the analyzed data can be seamlessly incorporated into broader agricultural strategies. This interconnectedness ensures that the insights generated by the system are not isolated but contribute to a larger framework of informed decision-making.
[00018] In conclusion, the system for soil and plant health analysis revolutionizes modern agriculture through the fusion of electromagnetic induction, machine learning, and geographic information systems. By harnessing the power of electromagnetic waves and advanced algorithms, it offers a deeper understanding of soil quality and plant health. The GIS interface adds a visual dimension to the data, while the integration capabilities ensure that the insights gleaned are effectively translated into on-ground actions. This system heralds a new era of precision agriculture, equipping farmers with the tools they need to cultivate healthier soils and more robust plants.
[00019] The method for soil and plant health analysis using Electromagnetic Induction (EMI) and Machine Learning presents an advanced and comprehensive approach to agricultural assessment and management. The method encompasses a series of well-coordinated steps that harness cutting-edge technologies to derive accurate insights into soil quality and plant vitality.
[00020] At its core, the method starts by transmitting electromagnetic waves into the soil and plants through EMI techniques. This process facilitates the elicitation of intricate electromagnetic responses that hold valuable information about the soil's composition and the health status of the plants. These responses are then captured and meticulously processed for analysis.
[00021] The crux of the method lies in the application of machine learning algorithms to the captured electromagnetic responses. Through intricate computations, these algorithms discern patterns, correlations, and subtleties within the data that might otherwise remain concealed. The outcome of this analysis is a precise determination of crucial metrics that gauge soil quality and plant health.
[00022] Visualizing the obtained metrics is achieved through the utilization of a Geographic Information Systems (GIS) platform. This step is pivotal in translating the numerical metrics into a spatial context, offering a visual representation of the soil and plant health status over geographic terrains. Furthermore, the method incorporates the flexibility of layering within the GIS visualization. This allows users to observe the metrics independently or in combination, facilitating a comprehensive understanding of the dynamic interplay between soil health and plant vitality.
[00023] The method's sophistication is underscored by its ability to calibrate the EMI unit across multiple frequency bands. This strategic step broadens the spectrum of captured electromagnetic responses, enabling a thorough assessment of soil layers at varying depths and accommodating diverse plant types.
[00024] Additionally, the method's practicality is evident in its incorporation of historical data storage. By retaining determined metrics in a database, the method enables trend analysis and predictive assessments in subsequent analyses. This historical perspective empowers practitioners to make informed decisions based on past trends and future predictions.
[00025] The method's holistic approach is completed by its integration capabilities with external agricultural management systems. This integration ensures that the valuable insights derived from soil and plant health analysis seamlessly inform irrigation, fertilization, and cropping decisions. By providing comprehensive health data, the method contributes to optimized agricultural practices that maximize productivity and sustainability.
[00026] In summary, the method for soil and plant health analysis using EMI and Machine Learning embodies a groundbreaking approach to modern agriculture. By synergizing technological advancements in electromagnetic induction and machine learning with spatial visualization and integration capabilities, the method empowers agricultural practitioners with a wealth of accurate insights. This leads to enhanced decision-making that supports healthier soils, thriving plants, and ultimately, more productive and sustainable farming practices.
[00027]
Brief Description of the Drawings
[00028] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00029] FIG. 1 represents an architectural overview of a system for soil and plant health analysis, according to some embodiments of the present disclosure.
[00030] FIG. 2 shows an exemplary detailed schematic flow diagram of a method for soil and plant health analysis using Electromagnetic Induction (EMI) and Machine Learning, according to some embodiments of the present disclosure.
[00031]
Detailed Description
[00032] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00033] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00034] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00035] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00036] The present invention relates generally to the fields of agriculture and environmental monitoring. More specifically, the invention pertains to a novel system and method for assessing soil quality and plant health by leveraging Electromagnetic Induction (EMI) techniques in conjunction with Machine Learning algorithms. The inventive approach facilitates the capture, analysis, and interpretation of electromagnetic responses from soil and plants. Additionally, this invention integrates Geographic Information Systems (GIS) for the visual representation of soil and plant health metrics, offering a spatial understanding of the agricultural landscape. This combined approach is designed to offer enhanced insights into the status and health of the soil and plants, assisting in precision agriculture and effective land management.
[00037] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00038] Modern agriculture faces the challenge of ensuring optimal soil quality and plant health to maximize crop yields while minimizing environmental impact. The current invention

aimed at assessing both soil quality and plant health by integrating Electromagnetic Induction (EMI) techniques and machine learning. This comprehensive approach will enable accurate determination of soil characteristics and nutrient levels, along with monitoring plant health. Furthermore, the invention can involve developing web and GIS applications to provide real-time access to soil quality arid plant health data, facilitating informed decision-making for farmers and agricultural stakeholders.
[00039] Referring to one or more preceding embodiments, the current invention can be focussed to achieve one or more objectives such as to develop a comprehensive data collection system using EMI techniques to measure essential parameters such as EC, pH, N, P, K, and soil moisture. Implementing machine learning models to correlate soil and plant health data with historical growth patterns. Further, creating user-friendly web and GIS applications to visualize soil quality, plant health, and their spatial distribution across agricultural fields.
[00040] This detailed description explores a cutting-edge system 100 that revolutionizes agriculture by combining electromagnetic induction (EMI) unit 102, machine learning processor 104, and geographic information systems (GIS) 106 to provide a comprehensive analysis of soil and plant health. The system's components work in tandem to capture, process, and visualize data, enabling farmers to make informed decisions for optimized crop production and sustainable land management. The proposed system 100 offers a transformative solution by integrating Electromagnetic Induction (EMI) technology, machine learning algorithms, and GIS visualization to create a robust platform for holistic soil and plant health analysis.
[00041] Pictorially represented in FIG. 1, illustrating an architectural setup of the system 100, comprises an EMI unit designed to emit and receive electromagnetic waves, capturing responses from both soil and plants. By transmitting controlled electromagnetic waves, the unit stimulates various physical properties within the soil and plant tissues, inducing distinct electromagnetic responses. These responses are crucial indicators of soil texture, moisture content, and plant vitality. For instance, variations in soil moisture can lead to changes in electrical conductivity, which the EMI unit detects. In a clay-rich soil, the EMI unit's electromagnetic waves will encounter higher resistance due to the soil's moisture retention characteristics. In contrast, sandy soil with lower moisture content will exhibit lower resistance. These differences help assess soil quality and moisture levels.
[00042] Referring to one or more preceding embodiments, in yet another epitome of illustration, the system 100 can employ EMI sensors to measure key parameters, including EC, pH, N, P, K, and soil moisture, both in the soil and around plant roots. Additionally, the system 100 can be employed to set up a network of sensors in various fields, capturing data at different growth stages and under varying conditions. Further, the system 100 can be enabled to gather supplemental data such as weather conditions, irrigation practices, and crop types.
[00043] In yet another embodiment, the captured electromagnetic responses are then fed into a machine learning processor, which employs sophisticated algorithms to analyze intricate patterns and relationships within the data. The processor identifies correlations between the responses and soil properties such as nutrient content, compaction, and drainage. Similarly, it determines plant health indicators like chlorophyll levels and stress patterns. By training on a diverse dataset, the machine learning processor refines its accuracy over time, enabling it to provide increasingly precise assessments. For instance, the machine learning processor might identify a correlation between certain electromagnetic responses and the presence of nitrogen in the soil. Subsequent analyses could then predict nitrogen levels based on electromagnetic data, aiding in precise fertilization.
[00044] Referring to the preceding embodiment, in yet another epitome of illustration, the machine learning processor can pre-process and clean the collected data to ensure data quality and reliability. Further, the processor can be employed to develop machine learning models that establish correlations between soil/plant health and growth patterns. Additionally, incorporating feature engineering to extract relevant insights, such as the effect of nutrients on plant growth and stress detection.
[00045] In yet another embodiment, the system's GIS interface visualizes the analyzed data across geographical landscapes, providing a spatial context to the information. This visual representation allows users to intuitively interpret the soil and plant health metrics. Furthermore, the GIS interface offers a layered visualization approach, enabling users to view soil quality and plant health metrics separately or as an overlay. This comprehensive view facilitates understanding the dynamic relationship between soil conditions and plant vitality. A farmer using the GIS interface can overlay soil quality data onto plant health data. This might reveal that areas with lower soil quality correspond to stressed plants, indicating a need for targeted soil improvement strategies.
[00046] In yet another embodiment, the system includes an EMI unit with multiple frequency bands, ensuring a comprehensive analysis of soil and plant electromagnetic responses. This versatility allows for accurate assessments across various soil depths and plant types. To enhance predictive analytics, the system incorporates a data storage module that archives historic soil and plant health data. Longitudinal data empowers the machine learning processor to identify trends and make informed predictions for future analyses.
[00047] The system's integration capabilities enable it to collaborate with external agricultural tools and platforms. By sharing insights with irrigation, fertilization, and cropping systems, the system supports holistic farm management based on comprehensive health data. The objectives achieved through this invention include designing a user-friendly web application that enables farmers to input field data and view real­ time soil and plant health assessments. To integrate GIS (Geographic Information System) capabilities to create an interactive map showcasing soil quality and plant health surfaces across fields and ensuring data security, real-time updates, and compatibility across devices.
[00048] Farmers can adjust irrigation, nutrient application, and pest management strategies based on real time data, thus maximizing field and resource efficiency Reduced environmental impact with targeted interventions will lead to minimized pesticide and fertilizer use. Additionally, the invention outcome can contribute to a more sustainable and productive agricultural system. By accurate assessment of soil quality and plant health using EMI techniques and machine learning models, can lead to improved plant health monitoring. Timely interventions could be made to nullify the adverse effects of nutrient imbalances. User-friendly web and GIS applications provide farmers with real-time insights into soil and plant conditions. This invention could integrate advanced technologies to address the crucial aspects of soil quality, plant health, and spatial analysis. The use of EMI techniques, machine learning, and GIS visualization will empower farmers with data-driven insights for making informed decisions.
[00049] Referring to one or more preceding embodiments, the integrated system's ability to capture, analyze, and visualize soil and plant health metrics presents a revolutionary advancement in agriculture. Through the fusion of EMI technology, machine learning algorithms, and GIS visualization, farmers gain valuable insights into their land's condition, allowing for informed decision-making that drives sustainable and productive farming practices. This system marks a new era of precision agriculture, transforming the way we manage soil and plant health for a more prosperous and ecologically conscious future.
[00050] Diagrammatically portrayed in FIG. 2, representing a flow diagram of a method 200, for soil and plant health analysis using Electromagnetic Induction (EMI) and Machine Learning, comprising the steps of (at step 202) transmitting electromagnetic waves into the soil and plants using EMI techniques, (at step 204) capturing the electromagnetic responses from the soil and plants, (at step 206) analyzing the captured responses using machine learning algorithms to determine soil quality and plant health metrics, and (at step 208) visually representing these metrics over geographic terrains using a GIS platform.
[00051] In an embodiment, the method 200 for soil and plant health analysis employs electromagnetic induction (EMI) techniques to gather essential data. It begins with transmitting controlled electromagnetic waves into the soil and plants. The EMI unit emits a sequence of electromagnetic waves, each with varying frequencies and amplitudes. These waves interact with the soil's physical properties and the plants' biological characteristics. An EMI unit is positioned in an agricultural field. It emits electromagnetic waves with frequencies ranging from 1 kHz to 1 MHz. These waves penetrate the soil and plant structures. The waves are reflected, refracted, and absorbed by various components such as minerals, water, and organic matter, carrying unique information about their presence and distribution.
[00052] In an embodiment, the EMI unit captures the electromagnetic responses from the soil and plants. By analyzing the changes in amplitude, phase, and frequency of the received signals, the unit discerns how different soil layers and plant tissues react to the electromagnetic waves. As the waves encounter variations in soil moisture, the amplitude of the received signal changes. In a different scenario, when the waves pass through plant roots, the phase shift of the signal indicates the health and density of the root system.
[00053] After capturing the responses, the method incorporates a machine learning processor to analyze the data. This processor employs a diverse range of algorithms to identify patterns, correlations, and relationships within the electromagnetic responses. The processor establishes links between these responses and key metrics related to soil quality and plant health. The machine learning processor employs a convolutional neural network (CNN) to analyze the electromagnetic responses. It identifies specific patterns that correspond to soil properties like moisture, texture, and nutrient content. For plant health assessment, the processor might use a recurrent neural network (RNN) to detect stress-related patterns in the electromagnetic data.
[00054] In an embodiment, the analyzed metrics are visually represented over geographic terrains using a Geographic Information Systems (GIS) platform. This platform creates spatial maps that display the distribution of soil quality and plant health metrics across the field. The GIS platform generates a map where each location is color-coded based on its soil moisture content. Darker colors indicate higher moisture levels, while lighter colors represent drier regions. Similarly, another map might display chlorophyll levels in plants using varying shades of green.
[00055] In an embodiment, the method 200 features calibration of the EMI unit across multiple frequency bands to enhance accuracy and versatility. Each frequency band captures distinct electromagnetic responses from different soil depths and plant types, enabling a comprehensive analysis. The EMI unit is calibrated to operate across three frequency bands: low (1 kHz - 100 kHz), medium (100 kHz - 1 MHz), and high (1 MHz - 10 MHz). Low frequencies penetrate deeper soils, capturing responses from subsoil layers, while high frequencies focus on shallow soil layers and plant tissues.
[00056] In an embodiment, the method 200 involves storing determined metrics in a database for historical reference. This historical data facilitates trend analysis and predictive assessments for future analyses, enhancing the accuracy of the machine learning algorithms. The system maintains a database where metrics from each analysis are stored with corresponding timestamps. Over time, the machine learning processor identifies trends, such as correlations between electromagnetic responses and crop yield, aiding in forecasting future agricultural outcomes.
[00057] In an embodiment, the method 200 further allows for layered GIS visualizations. Users can view soil quality and plant health metrics independently or overlay them, gaining insights into the interplay between soil conditions and plant vitality. A farmer overlays a map displaying soil texture with another map illustrating plant stress patterns. This visualization reveals areas with compacted soil where plants are struggling due to poor root penetration.
[00058] In an embodiment, the method integrates derived soil and plant health insights with external agricultural management systems. These integrations enable optimized irrigation, fertilization, and cropping decisions based on comprehensive health data. The system shares real-time plant health data with an automated irrigation system. When stress patterns are detected, the irrigation system increases water supply to stressed areas, promoting plant recovery.
[00059] Referring to one or more preceding embodiments, the presented embodiments illustrate a robust method 200 for soil and plant health analysis. By integrating EMI, machine learning, and GIS technologies, the method offers a comprehensive approach to informed decision-making in agriculture, enhancing crop productivity and sustainability.
[00060] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00061] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00062] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00063] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00064] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00065] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.

Soil and Plant health analysis using Electromagnetic Induction (EMI) techniques and Machine Learning for soil quality assessment, Plant Health Monitoring, and Geographic information systems (GIS) visualization
Field of the Invention
[0001] The present invention relates generally to the fields of agriculture and environmental monitoring. More specifically, the invention pertains to a novel system and method for assessing soil quality and plant health by leveraging Electromagnetic Induction (EMI) techniques in conjunction with Machine Learning algorithms. The inventive approach facilitates the capture, analysis, and interpretation of electromagnetic responses from soil and plants. Additionally, this invention integrates Geographic Information Systems (GIS) for the visual representation of soil and plant health metrics, offering a spatial understanding of the agricultural landscape. This combined approach is designed to offer enhanced insights into the status and health of the soil and plants, assisting in precision agriculture and effective land management.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Soil health and plant vitality are fundamental determinants of agricultural productivity. Historically, understanding the intricacies of the soil and monitoring plant health has been a labour-intensive and often imprecise endeavour, relying on manual sampling and visual inspections. This classical approach was fraught with limitations, often resulting in generalizations rather than specific, localized assessments of soil quality and plant health.
[0004] With the advent of technology, several advancements were made in the domain of soil and plant health analysis. Electromagnetic Induction (EMI) techniques emerged as a transformative method, offering a non-invasive means to probe the soil's properties. EMI operates by transmitting electromagnetic waves into the soil, with sensors then capturing the soil's electromagnetic responses. These responses can provide valuable insights into soil moisture, salinity, and texture. Prior art in this field, such as the work by Rhoades et al. (1993), demonstrated how EMI could be employed to map soil salinity across large fields, providing farmers with crucial data to tailor their irrigation strategies.
[0005] While EMI addressed the soil assessment dimension, plant health monitoring often remained a distinct, separate challenge. Preliminary solutions involved spectral imaging, where different wavelengths of light reflected by plants could indicate their health status. However, this method often lacked depth, focusing primarily on surface-level observations.
[0006] The integration of Machine Learning (ML) presented a paradigm shift. With the ability to process and analyze vast datasets, ML algorithms could identify intricate patterns and subtle variations in the electromagnetic responses captured by EMI. Prior research, such as the study by Jones et al. (2015), highlighted the potential of coupling EMI data with ML models to predict various soil properties, including organic carbon content and pH levels. Similarly, Zhang et al. (2018) demonstrated that ML could decipher the nuanced spectral signatures of plants, thereby offering insights into plant stressors and health metrics.
[0007] However, an evident gap in these advancements was the absence of an intuitive visualization mechanism. While EMI and ML provided raw data and analysis, understanding and interpreting this data in a spatial context remained a challenge. This is where Geographic Information Systems (GIS) made a profound impact. GIS platforms, as showcased in studies like Brevik et al. (2016), enabled the mapping of soil and plant health metrics across vast geographies, presenting data in layered, interactive maps. Such visualizations allowed farmers, agronomists, and land managers to pinpoint specific areas of concern, aiding in data-driven decision-making.
[0008] In conclusion, while each of these technologies—EMI, ML, and GIS—made significant strides individually, the holistic integration of all three has been a relatively unexplored territory. The combined potential of these technologies promises a comprehensive, efficient, and user-friendly approach to soil quality assessment, plant health monitoring, and geospatial visualization, addressing the challenges that were hitherto unresolved by individual technological solutions.
[0009]
[00010] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
Summary
[00011] Various objects, features, and advantages of the disclosed subject matter can be more fully appreciated with reference to the following detailed description of the disclosed subject matter when considered in connection with the following drawings, in which like reference numerals identify like elements.
[00012] The present invention relates generally to the fields of agriculture and environmental monitoring. More specifically, the invention pertains to a novel system and method for assessing soil quality and plant health by leveraging Electromagnetic Induction (EMI) techniques in conjunction with Machine Learning algorithms. The inventive approach facilitates the capture, analysis, and interpretation of electromagnetic responses from soil and plants. Additionally, this invention integrates Geographic Information Systems (GIS) for the visual representation of soil and plant health metrics, offering a spatial understanding of the agricultural landscape. This combined approach is designed to offer enhanced insights into the status and health of the soil and plants, assisting in precision agriculture and effective land management.
[00013] The proposed system for soil and plant health analysis presents a revolutionary approach to precision agriculture by combining cutting-edge technologies to provide comprehensive insights into soil quality and plant health. At its core, the system features an electromagnetic induction (EMI) unit that emits and receives electromagnetic waves, capturing intricate responses from both soil and plants. This captured data serves as the foundation for a sophisticated analysis conducted by a machine learning processor, leading to an accurate assessment of soil conditions and plant vitality.
[00014] One of the standout features of the EMI unit is its incorporation of multiple frequency bands. This innovative design allows the system to gather a diverse array of electromagnetic responses, resulting in a thorough and detailed analysis. By encompassing a broad spectrum of frequencies, the system can unravel a wealth of information that would have been overlooked by traditional approaches.
[00015] Complementing the EMI unit is the machine learning processor, a powerhouse of computational intelligence. This processor meticulously dissects the captured responses, sifting through intricate patterns and correlations that are beyond the capabilities of human observation. Through advanced algorithms, the processor deciphers the data to provide accurate insights into soil quality and plant health. The system's capabilities are further enhanced by a data storage module, which retains historical soil and plant health data. This historical data empowers the machine learning processor to engage in predictive analytics, enabling farmers to anticipate trends and make informed decisions that optimize agricultural practices.
[00016] A critical aspect of the system's functionality lies in its geographic information systems (GIS) interface. This interface takes the analyzed data and translates it into visually intuitive representations across geographical landscapes. The GIS interface offers users the flexibility to view soil quality and plant health separately or combined as an overlay, providing a multifaceted perspective that aids decision-making.
[00017] Moreover, the system's adaptability is showcased through its connectivity interfaces, facilitating integration with external agricultural tools and platforms. This integration paves the way for holistic farm management, as the analyzed data can be seamlessly incorporated into broader agricultural strategies. This interconnectedness ensures that the insights generated by the system are not isolated but contribute to a larger framework of informed decision-making.
[00018] In conclusion, the system for soil and plant health analysis revolutionizes modern agriculture through the fusion of electromagnetic induction, machine learning, and geographic information systems. By harnessing the power of electromagnetic waves and advanced algorithms, it offers a deeper understanding of soil quality and plant health. The GIS interface adds a visual dimension to the data, while the integration capabilities ensure that the insights gleaned are effectively translated into on-ground actions. This system heralds a new era of precision agriculture, equipping farmers with the tools they need to cultivate healthier soils and more robust plants.
[00019] The method for soil and plant health analysis using Electromagnetic Induction (EMI) and Machine Learning presents an advanced and comprehensive approach to agricultural assessment and management. The method encompasses a series of well-coordinated steps that harness cutting-edge technologies to derive accurate insights into soil quality and plant vitality.
[00020] At its core, the method starts by transmitting electromagnetic waves into the soil and plants through EMI techniques. This process facilitates the elicitation of intricate electromagnetic responses that hold valuable information about the soil's composition and the health status of the plants. These responses are then captured and meticulously processed for analysis.
[00021] The crux of the method lies in the application of machine learning algorithms to the captured electromagnetic responses. Through intricate computations, these algorithms discern patterns, correlations, and subtleties within the data that might otherwise remain concealed. The outcome of this analysis is a precise determination of crucial metrics that gauge soil quality and plant health.
[00022] Visualizing the obtained metrics is achieved through the utilization of a Geographic Information Systems (GIS) platform. This step is pivotal in translating the numerical metrics into a spatial context, offering a visual representation of the soil and plant health status over geographic terrains. Furthermore, the method incorporates the flexibility of layering within the GIS visualization. This allows users to observe the metrics independently or in combination, facilitating a comprehensive understanding of the dynamic interplay between soil health and plant vitality.
[00023] The method's sophistication is underscored by its ability to calibrate the EMI unit across multiple frequency bands. This strategic step broadens the spectrum of captured electromagnetic responses, enabling a thorough assessment of soil layers at varying depths and accommodating diverse plant types.
[00024] Additionally, the method's practicality is evident in its incorporation of historical data storage. By retaining determined metrics in a database, the method enables trend analysis and predictive assessments in subsequent analyses. This historical perspective empowers practitioners to make informed decisions based on past trends and future predictions.
[00025] The method's holistic approach is completed by its integration capabilities with external agricultural management systems. This integration ensures that the valuable insights derived from soil and plant health analysis seamlessly inform irrigation, fertilization, and cropping decisions. By providing comprehensive health data, the method contributes to optimized agricultural practices that maximize productivity and sustainability.
[00026] In summary, the method for soil and plant health analysis using EMI and Machine Learning embodies a groundbreaking approach to modern agriculture. By synergizing technological advancements in electromagnetic induction and machine learning with spatial visualization and integration capabilities, the method empowers agricultural practitioners with a wealth of accurate insights. This leads to enhanced decision-making that supports healthier soils, thriving plants, and ultimately, more productive and sustainable farming practices.
[00027]
Brief Description of the Drawings
[00028] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00029] FIG. 1 represents an architectural overview of a system for soil and plant health analysis, according to some embodiments of the present disclosure.
[00030] FIG. 2 shows an exemplary detailed schematic flow diagram of a method for soil and plant health analysis using Electromagnetic Induction (EMI) and Machine Learning, according to some embodiments of the present disclosure.
[00031]
Detailed Description
[00032] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00033] In view of the many possible embodiments to which the principles of the present discussion may be applied, it should be recognized that the embodiments described herein with respect to the drawing figures are meant to be illustrative only and should not be taken as limiting the scope of the claims. Therefore, the techniques as described herein contemplate all such embodiments as may come within the scope of the following claims and equivalents thereof.
[00034] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different instances in the description and the figures may indicate similar or identical items.
[00035] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00036] The present invention relates generally to the fields of agriculture and environmental monitoring. More specifically, the invention pertains to a novel system and method for assessing soil quality and plant health by leveraging Electromagnetic Induction (EMI) techniques in conjunction with Machine Learning algorithms. The inventive approach facilitates the capture, analysis, and interpretation of electromagnetic responses from soil and plants. Additionally, this invention integrates Geographic Information Systems (GIS) for the visual representation of soil and plant health metrics, offering a spatial understanding of the agricultural landscape. This combined approach is designed to offer enhanced insights into the status and health of the soil and plants, assisting in precision agriculture and effective land management.
[00037] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00038] Modern agriculture faces the challenge of ensuring optimal soil quality and plant health to maximize crop yields while minimizing environmental impact. The current invention

aimed at assessing both soil quality and plant health by integrating Electromagnetic Induction (EMI) techniques and machine learning. This comprehensive approach will enable accurate determination of soil characteristics and nutrient levels, along with monitoring plant health. Furthermore, the invention can involve developing web and GIS applications to provide real-time access to soil quality arid plant health data, facilitating informed decision-making for farmers and agricultural stakeholders.
[00039] Referring to one or more preceding embodiments, the current invention can be focussed to achieve one or more objectives such as to develop a comprehensive data collection system using EMI techniques to measure essential parameters such as EC, pH, N, P, K, and soil moisture. Implementing machine learning models to correlate soil and plant health data with historical growth patterns. Further, creating user-friendly web and GIS applications to visualize soil quality, plant health, and their spatial distribution across agricultural fields.
[00040] This detailed description explores a cutting-edge system 100 that revolutionizes agriculture by combining electromagnetic induction (EMI) unit 102, machine learning processor 104, and geographic information systems (GIS) 106 to provide a comprehensive analysis of soil and plant health. The system's components work in tandem to capture, process, and visualize data, enabling farmers to make informed decisions for optimized crop production and sustainable land management. The proposed system 100 offers a transformative solution by integrating Electromagnetic Induction (EMI) technology, machine learning algorithms, and GIS visualization to create a robust platform for holistic soil and plant health analysis.
[00041] Pictorially represented in FIG. 1, illustrating an architectural setup of the system 100, comprises an EMI unit designed to emit and receive electromagnetic waves, capturing responses from both soil and plants. By transmitting controlled electromagnetic waves, the unit stimulates various physical properties within the soil and plant tissues, inducing distinct electromagnetic responses. These responses are crucial indicators of soil texture, moisture content, and plant vitality. For instance, variations in soil moisture can lead to changes in electrical conductivity, which the EMI unit detects. In a clay-rich soil, the EMI unit's electromagnetic waves will encounter higher resistance due to the soil's moisture retention characteristics. In contrast, sandy soil with lower moisture content will exhibit lower resistance. These differences help assess soil quality and moisture levels.
[00042] Referring to one or more preceding embodiments, in yet another epitome of illustration, the system 100 can employ EMI sensors to measure key parameters, including EC, pH, N, P, K, and soil moisture, both in the soil and around plant roots. Additionally, the system 100 can be employed to set up a network of sensors in various fields, capturing data at different growth stages and under varying conditions. Further, the system 100 can be enabled to gather supplemental data such as weather conditions, irrigation practices, and crop types.
[00043] In yet another embodiment, the captured electromagnetic responses are then fed into a machine learning processor, which employs sophisticated algorithms to analyze intricate patterns and relationships within the data. The processor identifies correlations between the responses and soil properties such as nutrient content, compaction, and drainage. Similarly, it determines plant health indicators like chlorophyll levels and stress patterns. By training on a diverse dataset, the machine learning processor refines its accuracy over time, enabling it to provide increasingly precise assessments. For instance, the machine learning processor might identify a correlation between certain electromagnetic responses and the presence of nitrogen in the soil. Subsequent analyses could then predict nitrogen levels based on electromagnetic data, aiding in precise fertilization.
[00044] Referring to the preceding embodiment, in yet another epitome of illustration, the machine learning processor can pre-process and clean the collected data to ensure data quality and reliability. Further, the processor can be employed to develop machine learning models that establish correlations between soil/plant health and growth patterns. Additionally, incorporating feature engineering to extract relevant insights, such as the effect of nutrients on plant growth and stress detection.
[00045] In yet another embodiment, the system's GIS interface visualizes the analyzed data across geographical landscapes, providing a spatial context to the information. This visual representation allows users to intuitively interpret the soil and plant health metrics. Furthermore, the GIS interface offers a layered visualization approach, enabling users to view soil quality and plant health metrics separately or as an overlay. This comprehensive view facilitates understanding the dynamic relationship between soil conditions and plant vitality. A farmer using the GIS interface can overlay soil quality data onto plant health data. This might reveal that areas with lower soil quality correspond to stressed plants, indicating a need for targeted soil improvement strategies.
[00046] In yet another embodiment, the system includes an EMI unit with multiple frequency bands, ensuring a comprehensive analysis of soil and plant electromagnetic responses. This versatility allows for accurate assessments across various soil depths and plant types. To enhance predictive analytics, the system incorporates a data storage module that archives historic soil and plant health data. Longitudinal data empowers the machine learning processor to identify trends and make informed predictions for future analyses.
[00047] The system's integration capabilities enable it to collaborate with external agricultural tools and platforms. By sharing insights with irrigation, fertilization, and cropping systems, the system supports holistic farm management based on comprehensive health data. The objectives achieved through this invention include designing a user-friendly web application that enables farmers to input field data and view real­ time soil and plant health assessments. To integrate GIS (Geographic Information System) capabilities to create an interactive map showcasing soil quality and plant health surfaces across fields and ensuring data security, real-time updates, and compatibility across devices.
[00048] Farmers can adjust irrigation, nutrient application, and pest management strategies based on real time data, thus maximizing field and resource efficiency Reduced environmental impact with targeted interventions will lead to minimized pesticide and fertilizer use. Additionally, the invention outcome can contribute to a more sustainable and productive agricultural system. By accurate assessment of soil quality and plant health using EMI techniques and machine learning models, can lead to improved plant health monitoring. Timely interventions could be made to nullify the adverse effects of nutrient imbalances. User-friendly web and GIS applications provide farmers with real-time insights into soil and plant conditions. This invention could integrate advanced technologies to address the crucial aspects of soil quality, plant health, and spatial analysis. The use of EMI techniques, machine learning, and GIS visualization will empower farmers with data-driven insights for making informed decisions.
[00049] Referring to one or more preceding embodiments, the integrated system's ability to capture, analyze, and visualize soil and plant health metrics presents a revolutionary advancement in agriculture. Through the fusion of EMI technology, machine learning algorithms, and GIS visualization, farmers gain valuable insights into their land's condition, allowing for informed decision-making that drives sustainable and productive farming practices. This system marks a new era of precision agriculture, transforming the way we manage soil and plant health for a more prosperous and ecologically conscious future.
[00050] Diagrammatically portrayed in FIG. 2, representing a flow diagram of a method 200, for soil and plant health analysis using Electromagnetic Induction (EMI) and Machine Learning, comprising the steps of (at step 202) transmitting electromagnetic waves into the soil and plants using EMI techniques, (at step 204) capturing the electromagnetic responses from the soil and plants, (at step 206) analyzing the captured responses using machine learning algorithms to determine soil quality and plant health metrics, and (at step 208) visually representing these metrics over geographic terrains using a GIS platform.
[00051] In an embodiment, the method 200 for soil and plant health analysis employs electromagnetic induction (EMI) techniques to gather essential data. It begins with transmitting controlled electromagnetic waves into the soil and plants. The EMI unit emits a sequence of electromagnetic waves, each with varying frequencies and amplitudes. These waves interact with the soil's physical properties and the plants' biological characteristics. An EMI unit is positioned in an agricultural field. It emits electromagnetic waves with frequencies ranging from 1 kHz to 1 MHz. These waves penetrate the soil and plant structures. The waves are reflected, refracted, and absorbed by various components such as minerals, water, and organic matter, carrying unique information about their presence and distribution.
[00052] In an embodiment, the EMI unit captures the electromagnetic responses from the soil and plants. By analyzing the changes in amplitude, phase, and frequency of the received signals, the unit discerns how different soil layers and plant tissues react to the electromagnetic waves. As the waves encounter variations in soil moisture, the amplitude of the received signal changes. In a different scenario, when the waves pass through plant roots, the phase shift of the signal indicates the health and density of the root system.
[00053] After capturing the responses, the method incorporates a machine learning processor to analyze the data. This processor employs a diverse range of algorithms to identify patterns, correlations, and relationships within the electromagnetic responses. The processor establishes links between these responses and key metrics related to soil quality and plant health. The machine learning processor employs a convolutional neural network (CNN) to analyze the electromagnetic responses. It identifies specific patterns that correspond to soil properties like moisture, texture, and nutrient content. For plant health assessment, the processor might use a recurrent neural network (RNN) to detect stress-related patterns in the electromagnetic data.
[00054] In an embodiment, the analyzed metrics are visually represented over geographic terrains using a Geographic Information Systems (GIS) platform. This platform creates spatial maps that display the distribution of soil quality and plant health metrics across the field. The GIS platform generates a map where each location is color-coded based on its soil moisture content. Darker colors indicate higher moisture levels, while lighter colors represent drier regions. Similarly, another map might display chlorophyll levels in plants using varying shades of green.
[00055] In an embodiment, the method 200 features calibration of the EMI unit across multiple frequency bands to enhance accuracy and versatility. Each frequency band captures distinct electromagnetic responses from different soil depths and plant types, enabling a comprehensive analysis. The EMI unit is calibrated to operate across three frequency bands: low (1 kHz - 100 kHz), medium (100 kHz - 1 MHz), and high (1 MHz - 10 MHz). Low frequencies penetrate deeper soils, capturing responses from subsoil layers, while high frequencies focus on shallow soil layers and plant tissues.
[00056] In an embodiment, the method 200 involves storing determined metrics in a database for historical reference. This historical data facilitates trend analysis and predictive assessments for future analyses, enhancing the accuracy of the machine learning algorithms. The system maintains a database where metrics from each analysis are stored with corresponding timestamps. Over time, the machine learning processor identifies trends, such as correlations between electromagnetic responses and crop yield, aiding in forecasting future agricultural outcomes.
[00057] In an embodiment, the method 200 further allows for layered GIS visualizations. Users can view soil quality and plant health metrics independently or overlay them, gaining insights into the interplay between soil conditions and plant vitality. A farmer overlays a map displaying soil texture with another map illustrating plant stress patterns. This visualization reveals areas with compacted soil where plants are struggling due to poor root penetration.
[00058] In an embodiment, the method integrates derived soil and plant health insights with external agricultural management systems. These integrations enable optimized irrigation, fertilization, and cropping decisions based on comprehensive health data. The system shares real-time plant health data with an automated irrigation system. When stress patterns are detected, the irrigation system increases water supply to stressed areas, promoting plant recovery.
[00059] Referring to one or more preceding embodiments, the presented embodiments illustrate a robust method 200 for soil and plant health analysis. By integrating EMI, machine learning, and GIS technologies, the method offers a comprehensive approach to informed decision-making in agriculture, enhancing crop productivity and sustainability.
[00060] Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are contained in the above discussions, these should not be construed as limitations on the scope of the subject matter described herein, but rather as descriptions of features that may be specific to particular embodiments. Certain features that are described in this specification in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable sub-combination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a sub-combination or variation of a sub-combination.
[00061] The term “memory,” as used herein relates to a volatile or persistent medium, such as a magnetic disk, or optical disk, in which a computer can store data or software for any duration. Optionally, the memory is non-volatile mass storage such as physical storage media. Furthermore, a single memory may encompass and in a scenario wherein computing system is distributed, the processing, memory and/or storage capability may be distributed as well.
[00062] Throughout the present disclosure, the term ‘server’ relates to a structure and/or module that include programmable and/or non-programmable components configured to store, process and/or share information. Optionally, the server includes any arrangement of physical or virtual computational entities capable of enhancing information to perform various computational tasks.
[00063] Throughout the present disclosure, the term “network” relates to an arrangement of interconnected programmable and/or non-programmable components that are configured to facilitate data communication between one or more electronic devices and/or databases, whether available or known at the time of filing or as later developed. Furthermore, the network may include, but is not limited to, one or more peer-to-peer network, a hybrid peer-to-peer network, local area networks (LANs), radio access networks (RANs), metropolitan area networks (MANS), wide area networks (WANs), all or a portion of a public network such as the global computer network known as the Internet, a private network, a cellular network and any other communication system or systems at one or more locations.
[00064] Throughout the present disclosure, the term “process”* relates to any collection or set of instructions executable by a computer or other digital system so as to configure the computer or the digital system to perform a task that is the intent of the process.
[00065] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.

Claims
I/We Claim:
1. A system for soil and plant health analysis, comprising:
an electromagnetic induction (EMI) unit designed to emit and receive electromagnetic waves, capturing soil and plant responses;
a machine learning processor to analyze the captured responses for assessing soil quality and plant health; and
a geographic information systems (GIS) interface to visually represent the analyzed data across geographic landscapes.
2. The system of claim 1, wherein the EMI unit comprises multiple frequency bands to capture a broad range of soil and plant electromagnetic responses for a comprehensive analysis.
3. The system of claim 1, further comprising a data storage module to store historic soil and plant health data, enabling the machine learning processor to leverage longitudinal data for enhanced predictive analytics.
4. The system of claim 1, wherein the GIS interface offers a layered visualization approach, allowing users to segregate and visualize soil quality and plant health separately or in a combined overlay.
5. The system of claim 1, further comprising connectivity interfaces enabling integration with external agricultural tools or platforms for holistic farm management based on the analyzed data.
6. A method for soil and plant health analysis using Electromagnetic Induction (EMI) and Machine Learning, comprising the steps of:
transmitting electromagnetic waves into the soil and plants using EMI techniques;
capturing the electromagnetic responses from the soil and plants;
analyzing the captured responses using machine learning algorithms to determine soil quality and plant health metrics; and
visually representing these metrics over geographic terrains using a GIS platform.
7. The method of claim 6, further comprising the step of calibrating the EMI unit across multiple frequency bands, allowing for a broad-spectrum capture of electromagnetic responses from varied soil depths and plant types.
8. The method of claim 6, incorporating the step of storing the determined metrics in a database, subsequently utilizing this historical data for trend analysis and predictive assessments in future analyses.
9. The method of claim 6, further comprising the step of layering the GIS visualizations, enabling users to view soil and plant metrics independently or in combination, providing insights into the interplay between soil health and plant vitality.
10. The method of claim 6, further comprising the step of integrating the derived soil and plant health insights with external agricultural management systems, facilitating optimized irrigation, fertilization, and cropping decisions based on comprehensive health data.

Soil and Plant health analysis using Electromagnetic Induction (EMI) techniques and Machine Learning for soil quality assessment, Plant Health Monitoring, and Geographic information systems (GIS) visualization
Abstract
The invention presents a comprehensive system for soil and plant health analysis. Employing an electromagnetic induction (EMI) unit, the system transmits and receives electromagnetic waves to gauge soil and plant responses, providing a non-invasive methodology to gauge their inherent properties. An integrated machine learning processor delves into the captured data, assessing the nuanced metrics of soil quality and plant vitality by discerning intricate patterns and variations. To enhance interpretability and application, a geographic information systems (GIS) interface is incorporated, offering users a spatial visualization of the derived metrics across vast landscapes. This amalgamation of EMI, machine learning, and GIS ushers in a novel approach to agricultural monitoring, marrying precision with user-centric data representation.
, Claims:Claims
I/We Claim:
1. A system for soil and plant health analysis, comprising:
an electromagnetic induction (EMI) unit designed to emit and receive electromagnetic waves, capturing soil and plant responses;
a machine learning processor to analyze the captured responses for assessing soil quality and plant health; and
a geographic information systems (GIS) interface to visually represent the analyzed data across geographic landscapes.
2. The system of claim 1, wherein the EMI unit comprises multiple frequency bands to capture a broad range of soil and plant electromagnetic responses for a comprehensive analysis.
3. The system of claim 1, further comprising a data storage module to store historic soil and plant health data, enabling the machine learning processor to leverage longitudinal data for enhanced predictive analytics.
4. The system of claim 1, wherein the GIS interface offers a layered visualization approach, allowing users to segregate and visualize soil quality and plant health separately or in a combined overlay.
5. The system of claim 1, further comprising connectivity interfaces enabling integration with external agricultural tools or platforms for holistic farm management based on the analyzed data.
6. A method for soil and plant health analysis using Electromagnetic Induction (EMI) and Machine Learning, comprising the steps of:
transmitting electromagnetic waves into the soil and plants using EMI techniques;
capturing the electromagnetic responses from the soil and plants;
analyzing the captured responses using machine learning algorithms to determine soil quality and plant health metrics; and
visually representing these metrics over geographic terrains using a GIS platform.
7. The method of claim 6, further comprising the step of calibrating the EMI unit across multiple frequency bands, allowing for a broad-spectrum capture of electromagnetic responses from varied soil depths and plant types.
8. The method of claim 6, incorporating the step of storing the determined metrics in a database, subsequently utilizing this historical data for trend analysis and predictive assessments in future analyses.
9. The method of claim 6, further comprising the step of layering the GIS visualizations, enabling users to view soil and plant metrics independently or in combination, providing insights into the interplay between soil health and plant vitality.
10. The method of claim 6, further comprising the step of integrating the derived soil and plant health insights with external agricultural management systems, facilitating optimized irrigation, fertilization, and cropping decisions based on comprehensive health data.