Integrated soil analysis and prediction system

Abstract

Methods, systems, and computer program products for implementing a soil analysis process. A set of soil data may be received at a device from one or more soil sensors. The one or more soil sensors may be coupled to a portion of an agricultural machine configured to be in contact with soil. The device may be configured to analyze soil when a portion of the one or more soil sensors is in contact with the soil. Spectral data and soil conductivity and / or capacitance data may be obtained based on the collected set of soil data. Analyzed soil data may be determined based on the spectral data and soil conductivity and / or capacitance data. The analyzed soil data may be provided to a user device configured to display a visual representation of at least part of the analyzed soil data.

Description

INTEGRATED SOIL ANALYSIS AND PREDICTION SYSTEMCROSS-REFERENCE TO RELATED APPLICATION

[0001] This Application claims the benefit of U.S. Provisional Application Serial No. 63 / 656,370 filed June 5, 2024, which is incorporated by reference herein in its entirety.TECHNICAL FIELD

[0002] The present invention generally relates to computers, computer software and sensor, and more specifically, to methods, systems, hardware and computer program products for implementing a soil analysis process for precision agriculture.BACKGROUND

[0003] Traditional methods of soil analysis require laboratory procedures that are time-consuming, labor intensive, costly, lack data potency, and are not feasible for realtime decision-making.SUMMARY

[0004] In embodiments of the invention, a system for real-time soil analysis is provided. The invention further relates to integrated agricultural management platforms that combine real-time soil diagnostics with advanced yield prediction, decision support systems, and market analytics for both farmers and commodity traders.

[0005] Current agricultural management platforms typically excel in data integration and yield mapping but either lack deep Al-driven decision support or require expensive, slow laboratory tests for soil analysis. Additionally, existing platforms generally focus exclusively on either farm management or commodity trading, failing to bridge the gap between field-level soil data and market-level trading decisions. This separation creates inefficiencies in agricultural markets where real-time soil and crop health data could significantly improve trading strategies and risk management.

[0006] Furthermore, yield prediction systems often rely on limited data inputs and fail to incorporate the complex interrelationships between soil properties, weather patterns, management practices, and historical performance. The lack of comprehensive, real-time soil data as a foundation for these predictions reduces their accuracy and utility for both farm management and market forecasting.

[0007] The system may include a spectroscopy sensor configured to determine soil spectral data, an electrical property sensor configured to determine soil conductivity and / or capacitance data, and a controller communicatively coupled to the spectroscopy sensor and to the electrical property sensor. The controller may be configured to determine analyzed soil data based on at least one of spectral data received from the spectroscopy sensor or soil conductivity and / or capacitance data received from the electrical property sensor, and provide the analyzed soil data to a user device, such that the user device can display a visual representation of at least part of the analyzed soil data.

[0008] These and other embodiments can each optionally include one or more of the following features.

[0009] In some embodiments, determining the analyzed soil data is based on one or more processing techniques and machine learning models. In some embodiments, the spectroscopy sensor and the electrical property sensor are coupled to a tiller blade of an agricultural machine. In some embodiments, the tiller blade comprises a soil sensor protection mechanism configured to protect the spectroscopy sensor and the electrical property sensor during operation of the tiller blade when the spectroscopy sensor and the electrical property sensor are positioned below an upper surface of the soil.

[0010] In some embodiments, the system further includes a global positioning satellite (GPS) module for tracking a location of soil contacted by the spectroscopy sensor and the electrical property sensor, wherein the GPS module is operatively coupled to the controller. In some embodiments, the system further includes a soil sensor data database for storing the spectral data and the conductivity and capacitance data, wherein the soil sensor data database is operatively coupled to the controller. In some embodiments, the system further includes a plurality of pliable U-bolts configured to securely attach the spectroscopy sensor and the electrical property sensor to an agricultural machine. In some embodiments, the system further includes a heightadjustment mechanism configured to position the sensors at varying depths below an upper surface of the soil.

[0011] In some embodiments, the controller is configured to receive climate data from a climate data source, and revise the analyzed soil data based on the received climate data. In some embodiments, the system is wirelessly communicatively coupled to the user device.

[0012] In some embodiments, the system is mounted on an agriculture vehicle configured to be controller by an operator positioned at an operator station of the agriculture vehicle, and wherein the system includes the user device that is configured tobe viewable by an operator positioned at the operator station, and wherein the user device is configured to display a visual representation of at least part of the analyzed soil data.

[0013] In embodiments of the invention, a method for implementing an integrated soil analysis and prediction process is provided. The method, at an electronic device having a processor and a display, includes receiving, from one or more soil sensors, a set of soil data, wherein the one or more soil sensors are coupled to a portion of an agricultural machine configured to be in contact with soil, wherein the electronic device is configured to analyze soil when a portion of the one or more soil sensors is in contact with the soil, and wherein the set of soil data includes spectral data, and soil conductivity and / or capacitance data. The method may further include obtaining, determining, based on the spectral data and soil conductivity and / or capacitance data, analyzed soil data, and providing the analyzed soil data to a user device configured to display a visual representation of at least part of the analyzed soil data.

[0014] These and other embodiments can each optionally include one or more of the following features.

[0015] In some embodiments, determining the analyzed soil data is based on one or more processing techniques and machine learning models. In some embodiments, the one or more soil sensors comprises a spectroscopy sensor configured to determine spectral data of soil. In some embodiments, the one or more soil sensors comprises an electrical property sensor configured to determine soil conductivity and / or capacitance data of soil.

[0016] In some embodiments, the agriculture machine includes a tiller blade, wherein the one or more soil sensors are coupled to the tiller blade, and wherein the agricultural machine comprises a soil sensor protection mechanism configured to protect the one or more soil sensors during operation of the tiller blade when the at least the portion of the one or more soil sensors are positioned below an upper surface of the soil.

[0017] In some embodiments, the method further includes receiving climate data from a climate data source, and updating the analyzed soil data based on the climate data. In some embodiments, the one or more soil sensors are in contact with soil, and the electronic device is analyzing the soil data.

[0018] In some embodiments, a representation of the analyzed soil data is displayed on a soil analyzer user interface that comprises one or more real-time interactive dashboards, wherein each real-time interactive dashboard is associated with one or more soil parameters. In some embodiments, the one or more soil parametersincludes moisture levels, potential of hydrogen (pH) levels, nitrogen, phosphorus, and potassium (NPK) content, carbon levels, or combinations thereof.

[0019] In additional embodiments, the system includes a dual-interface platform that provides a first user interface associated with agriculture field workers and a second user interface associated with commodity traders. The first user interface (e.g., a farmer dashboard) provides field-level soil analysis, nutrient mapping, and agronomic recommendations, while the second user interface (e.g., a trader dashboard) offers regional yield forecasts, market analytics, and scenario testing tools that incorporate soil health data into commodity price predictions. Both interfaces are supported by a sophisticated machine learning pipeline that correlates the processed soil sensor data (e.g., integration) with obtained weather pattern data, historical yield data, and market indicator data.

[0020] Further embodiments include an Al assistant named "Tilly" that provides conversational guidance to users through both interfaces. The Al assistant leverages agronomic ontologies and rule-based decision trees to offer context-aware recommendations, explain complex soil-market relationships, and guide users through scenario testing. The system also includes a standardized Soil Health Scoring System that combines multiple soil parameters into an easy-to-understand composite score, facilitating comparison across fields and regions. Al assistant bridges data and decisions by delivering context-aware recommendations directly through the user interface.

[0021] In some embodiments of the invention, the system may further include an API Integration Layer that enables connectivity with external farm management systems, carbon credit registries, and market data providers, allowing for seamless data exchange and expanded functionality through third-party integrations.

[0022] In embodiments of the invention, a method for integrated agricultural management is provided. The method, at an electronic device having a processor, includes obtaining soil data from one or more sensors, preprocessing the soil data using Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) techniques, analyzing the preprocessed soil data using a model ensemble that includes a plurality of machine learning models, determining a quantification for uncertainty in predictions using a plurality of assessment methods, generating field-level soil analysis and nutrient mapping of at least one agricultural field for display on a first user interface associated with agriculture field workers, generating a regional yield forecast and a market analytics for display on a second user interface associated with commodity traders, and determining at least one scenario testing tool that incorporates soil health data intocommodity price predictions based on the generated field-level soil analysis and nutrient mapping of the at least one agricultural field.

[0023] These and other embodiments can each optionally include one or more of the following features.

[0024] In some embodiments, the method further includes providing conversational guidance through an artificial intelligence (Al) assistant that leverages both rule-based decision trees and adaptive learning capabilities. In some embodiments, the method further includes calculating a standardized soil health score that combines multiple soil parameters into a composite value. In some embodiments, the method further includes enabling data exchange with external systems through an Application Programming Interface (API) Integration Layer.

[0025] In some embodiments, the one or more sensors comprise at least one of a spectroscopy sensos and an electrical property sensor. In some embodiments, analyzing the preprocessed soil data using a model ensemble comprises integrating the processed soil sensor data with obtained weather pattern data, historical yield data, and market indicator data.

[0026] In some embodiments of the invention, a device including a non-transitory computer-readable storage medium, and one or more processors coupled to the non- transitory computer-readable storage medium, wherein the non-transitory computer- readable storage medium includes program instructions that, when executed by the one or more processors, cause the one or more processors to perform the method as described above.

[0027] In some embodiments of the invention, a computing apparatus including one or more processors, at least one memory device coupled with the one or more processors, and a data communications interface operably associated with the one or more processors, where the memory device contains a plurality of program instructions that, when executed by the one or more processors, cause the computing apparatus to perform the method as described above.

[0028] In some embodiments of the invention, a non-transitory computer storage medium encoded with a computer program is provided, where the computer program includes a plurality of program instructions that when executed by one or more processors cause the one or more processors to perform the method as described above.

[0029] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used in isolation as an aid in determining the scope of the claimed subject matter.BRIEF DESCRIPTION OF DRAWINGS

[0030] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate various embodiments of the invention and, together with a general description of the invention given above and the detailed description of the embodiments given below, serve to explain the embodiments of the invention. In the drawings, like reference numerals refer to like features in the various views.

[0031] Figure 1 illustrates an example operating environment for implementing an integrated soil analysis and prediction process for precision agriculture, according to embodiments of the invention.

[0032] Figure 2 illustrates a side view of an embodiment of an agricultural machine that includes tiller blades for cultivating soil, according to embodiments of the invention.

[0033] Figures 3A-3E illustrate views of an agricultural apparatus 300 that includes soil sensors that may be coupled to an end of an arm of the agricultural apparatus, according to embodiments of the invention.

[0034] Figure 4 illustrates an example environment of an example sensor system for soil analysis, according to embodiments of the invention.

[0035] Figures 5A-5C illustrate an example process 500 for a data pipeline architecture for implementing an integrated soil analysis and prediction process for precision agriculture, according to embodiments of the invention.

[0036] Figure 6 illustrates an example process 600 for a data acquisition processing and output of the soil fertility assessment, according to embodiments of the invention.

[0037] Figures 7-12 illustrate example screenshots for soil analysis and prediction processes via a soil analyzer user interface, according to embodiments of the invention.

[0038] Figure 13 is a flowchart of an example integrated soil analysis and prediction process for precision agriculture, according to embodiments of the invention.

[0039] Figure 14 is a flowchart of an example integrated soil analysis and prediction process for precision agriculture, according to embodiments of the invention.

[0040] Figure 15 is a block diagram showing an example computer architecture for a computer capable of executing the software components described herein, according to embodiments described herein.DETAILED DESCRIPTION

[0041] Generally, systems, methods, devices, and techniques are provided for implementing an integrated soil analysis and prediction process for precision agriculture. This disclosure describes a system that, in one case, provides an integrated system for real-time soil analysis and prediction of soil properties using portable visible and nearinfrared (VIS-NIR) spectroscopy sensors, electrical property sensors, global positioning system (GPS) modules, and advanced machine learning algorithms. The system can integrated with / be mounted on various types of farming and agricultural equipment, enabling continuous data collection and analysis of soil properties at various (root) depths during farming operations. The invention can provide continuous data collection and analysis during farming operations, enhancing decision-making, reducing resource waste, and contributing to sustainable agricultural practices.

[0042] More specifically, this technology includes a method that comprises an electronic device (e.g., a user’s device, i.e., a device of a farmer, user or operator) having a processor and a display; collecting, by one or more soil sensors, a set of soil data, wherein the one or more soil sensors are coupled to a tiller blade of an agricultural machine and configured to analyze soil when a portion of the one or more soil sensors is positioned below an upper surface of the soil (e.g., spectral data via VIS-NIR spectroscopy sensor, conductivity data via electrical sensor, etc., where data is collected / analyzed from under the soil surface). The method may further include obtaining, based on the collected set of soil data, spectral data and soil conductivity and / or capacitance data (e.g, a microcontroller processes data and stores it on a local physical drive; the data is transmitted wirelessly from the sensors to the user’s device using LoRa technology, or other long range electromagnetic technology.

[0043] In some implementations, the method may further include determining, based on the spectral data and soil conductivity and / or capacitance data, analyzed soil data (e.g., processing collected data using preprocessing techniques and machine learning models; could be processed locally or transmitted to wirelessly to a host server to analyze). The method may further include providing the analyzed soil data on the display (e.g., visualizing and analyzing data through a web application, such as a soil analyzer graphic user interface (GUI), to display predicted soil analysis). A web application may provide a comprehensive platform for data visualization and interaction, with features that may include real-time data display, interactive dashboards showing soil parameters such as moisture levels, potential of hydrogen (pH) levels, nitrogen, phosphorus, and potassium (NPK) content, and carbon levels, artificial intelligence (Al)generated insights, crop management recommendations based on real-time data analysis, carbon sequestration tools (e.g., estimation and visualization of potential carbon sequestration), and / or diagnostic tools (e.g., real-time monitoring and error detection for system components).

[0044] Figure 1 illustrates an example operating environment 100 for implementing an integrated soil analysis and prediction process for precision agriculture, according to embodiments of the invention. The example environment 100 includes one or more user device(s) 110, one or more soil sensors 130, and a soil analysis and prediction server 140 that communicate over a data communication network 102, e.g., a local area network (LAN), a wide area network (WAN), the Internet, a mobile network, or a combination thereof.

[0045] A user device 110 can include a desktop, a laptop, a server, or a mobile device, such as a smartphone, tablet computer, wearable device (e.g., smartwatch), in- vehicle computing device, and / or other types of mobile devices. The user device 110 includes applications, such as the application 112, for implementing the soil analysis orchestration processes and communications to / from the soil analysis and prediction server 140. The user device 110 can include other applications. Additionally, the user device 110 includes a display that provides a graphical user interface (GUI) 114. Accordingly, in the event that a user of the user device 110 initiates a soil analysis event via the application 112, corresponding content is generated via the device at user interface 114 and provided at a display of the user device 110 (e.g., a GUI for visualizing and analyzing soil analysis data and display predicted soil analysis and provide access to a comprehensive platform for data visualization and interaction). The user interface 114 is referred to herein as a “soil analyzer user interface,” and will be further described herein.

[0046] The user device 110 includes a front-end soil analysis orchestration instruction set 120 that includes a soil analysis module 122, a soil sensor module 124, a location module 126, and a soil sensor data module 128, according to techniques described herein. In some implementations of the invention, the soil analysis module 122 may be utilized by the front-end soil analysis orchestration instruction set 120 to analyze the soil based on the data received from the one or more soil sensor(s) 130 according to one or more techniques described herein (e.g., spectroscopy data, conductivity and / or capacitance data, etc.). In some embodiments of the invention, the soil analysis module 122 may analyze one or more soil parameters that includes moisture levels, pH levels, NPK content, carbon levels, or a combination thereof. In some implementations, the frontend soil analysis orchestration instruction set 120 may include a climate module forobtaining climate data that may be analyzed to provide contextual environmental information.

[0047] In some implementations of the invention, the soil sensor module 124 may be utilized by the front-end soil analysis orchestration instruction set 120 to communicate with the one or more soil sensor(s) 130. The communication may be connected by a data cable, such as fiber optics, LAN cable, and the like. Additionally, or alternatively, the communication between the soil sensor module 124 and the one or more soil sensor(s) 130 may be wireless. For example, long range (aka “LoRa”) technology for wireless data transmission, or other long range electromagnetic technology may be used for wireless communication between a sensor 130 and the user device 110.

[0048] In some implementations of the invention, the location module 126 may be utilized by the front-end soil analysis orchestration instruction set 120 to ensure precision location tracking and may determine GPS coordinates or other location technology to determine location data for each soil sample. The location data may be embedded as metadata when acquiring the soil data.

[0049] In some implementations of the invention, the soil sensor data module 128 may be utilized by the front-end soil analysis orchestration instruction set 120 to manage, collect, and process the obtained sensor data between the one or more soil sensor(s) 130 and / or the sensor data database 135.

[0050] The one or more soil sensor(s) 130 may include a spectroscopy sensor for obtaining spectral data of soil, an electrical sensor for obtaining conductivity data, or other soil sensors. Because the one or more sensor(s) 130 can be integrated into agricultural equipment for use in contact with soil and moisture, the one or more sensor(s) 130 can be configured to be resistant to long-term electrolysis, can be corrosion resistant, can be encapsulated via vacuum potting, and / or be waterproof (e.g., encapsulated in a cured flame retardant epoxy resin). The one or more soil sensor(s) 130 may include a main housing component that includes the main computing and soil analysis hardware (e.g., a microcontroller, etc.), a power source (e.g., a battery), transmission components (e.g., wireless Tx / Rx components via radio frequency (RF) transmission, cellular, WiFi, short range technologies, other low-bandwidth or high-bandwidth protocols, and the like), and other internal components, for the one or more sensor(s) 130. The one or more soil sensor(s) 130 may further include a sensing probe that contacts the soil to obtain one or more different soil measurements (e.g., spectral, conductivity, capacitance, moisture, pH, NPK content, carbon, humidity, temperature, and the like), depending on the type of sensor. A probe of a sensor may include a steel needle or other high-quality material thatcan withstand long-term electrolysis, acid, and alkali corrosion that may be caused by contact with soil.

[0051] The one or more soil sensor(s) 130 may be mounted directly to agricultural equipment and may include and / or be coupled to additional components (e.g., padding or other force-absorbing material) around the housing to reduce vibrations and overall wear and tear of the one or more soil sensor(s) 130. The one or more soil sensor(s) 130 may be equipped with or coupled to photomultipliers to reduce scan time. The one or more soil sensor(s) 130 may include ultrasonic sensors to measure the path length to the ground, and reduce the impact of stochastic vibrations.

[0052] The soil analysis and prediction server 140 includes a back-end soil analysis orchestration instruction set 150 that includes a soil analysis module 152, a prediction analysis module 154, a location module 156, and a user interface module 158, according to techniques described herein. In some implementations of the invention, the soil analysis module 152 may be utilized by the back-end soil analysis orchestration instruction set 150 to analyze the soil based on the data received from the one or more soil sensor(s) 130 according to one or more techniques described herein (e.g., spectroscopy data, conductivity and / or capacitance data, etc.). In some embodiments of the invention, the soil analysis module 152 may analyze one or more soil parameters that includes moisture levels, potential of hydrogen (pH) levels, nitrogen, phosphorus, and potassium (NPK) content, carbon levels, or a combination thereof. The soil analysis data may be collected and stored in the soil analysis database 142.

[0053] In some implementations of the invention, the prediction analysis module 154 may be utilized by the back-end soil analysis orchestration instruction set 150 to ensure accurate soil property predictions by implementing one or more preprocessing techniques and machine learning models. In some embodiments, the following preprocessing techniques and machine learning models may be integrated and utilized locally at the user device(s) 110 without having to send the data to the soil analysis and prediction server 140 (e.g., a prediction analysis module at the front-end soil analysis orchestration instruction set 120).

[0054] In some implementations, the prediction analysis module 154 may include one or more data preprocessing techniques such as spectral space transformation (SST) to adjust spectral data for moisture interference, standard normal variate (SNV) to apply a transformation to correct for scattering and baseline shifts in the spectral data (e.g..standardizing each spectrum by subtracting its mean and dividing by its standard deviation), multiplicative scatter correction (MSC) (e.g., correcting for multiplicative and additive effects in the spectral data by using a reference spectrum and applying a linearcorrection), interpolation of missing values (e.g., using linear interpolation to fill in missing values in the spectral data), outlier removal (e.g., identifiying and removing outliers based on z-scores that exceed a specified threshold, ensuring cleaner data for analysis), or a combination thereof.

[0055] In some implementations, the prediction analysis module 154 may include one or more data machine learning techniques using machine learning models based on training data from training data database 160. The one or more data machine learning techniques may include partial least squares regression (PLSR) (e.g., a regression technique that models the relationship between the spectral data and soil properties and projects the predictors and the response variables to a new space and performs linear regression in this space), support vector regression (SVR) (e.g., using a Gaussian kernel to model the relationship between the principal component analysis (PCA)-transformed spectral data and soil properties, providing flexibility in capturing non-linear relationships), neural networks (NN) (e.g., utilizing a feedforward neural network with two hidden layers for predicting soil properties and is trained with normalized input data and includes dropout regularization to prevent overfitting), a cubist-like model (e.g., implementing a bagged ensemble of regression trees, similar to Cubist models, to predict soil properties based on the PCA-transformed data), and the like.

[0056] In some implementations, the prediction analysis module 154 may include one or more data processing techniques for dimensionality reduction, such as principal component analysis (PCA), that reduces the dimensionality of the spectral data by transforming it into a set of principal components (e.g., this helps in identifying the most significant features while reducing multicollinearity, where the number of components is chosen to capture at least 95% of the explained variance). In some implementations, the prediction analysis module 154 may include one or more data processing techniques for cross-validation, such as k-Fold cross-validation (e.g., dividing the dataset into k subsets and performs training and testing iteratively on these subsets to ensure robust model evaluation and includes calculating root mean square error (RMSE) for each fold to assess model performance). In some implementations, the prediction analysis module 154 may include one or more data processing techniques for ensemble prediction, such as averaging predictions (e.g., combining predictions from PLSR, SVR, NN, and the Cubist-like model to form an ensemble prediction, which helps in improving the overall accuracy and robustness of the system).

[0057] In some implementations, to analyze the spectral data, the prediction analysis module 154 may utilize an SNV algorithm to correct baseline shifts by centering the data around zero to remove any additive baseline shifts, ensuring that the spectra arenot biased by variations in the measurement setup. In some implementations, to analyze the spectral data, the prediction analysis module 154 may utilize an SNV algorithm to reduce light scattering effects by normalizing by the standard deviation to mitigate the multiplicative effects of light scattering, resulting in more consistent and comparable spectra.

[0058] In some implementations, the prediction analysis module 154 may use software scripts to align SST, soil moisture, and spectral data based on their timestamps and geo-references. This synchronization ensures that each spectral reading is paired with the corresponding SST and soil moisture measurements. The synchronized data may be integrated into a unified dataset. This involves combining spectral readings with the corresponding SST and soil moisture data into a single database or data frame.

[0059] In some implementations, the prediction analysis module 154 may utilize SST and soil moisture data are used to refine baseline correction algorithms. For instance, SST data helps adjust for temperature-related baseline shifts in spectral readings. Soil moisture levels from EC sensors are used to normalize spectral data using simple regression, accounting for variations caused by differing moisture conditions. From the integrated dataset, features are extracted that combine spectral, SST, and soil moisture information. These features provide a more comprehensive representation of the factors affecting the soil sensing techniques described herein. Machine learning models are trained on the integrated dataset (e.g. stored in the training data database 160). In some implementations, the inclusion of SST and soil moisture data may enhance the models by providing additional context that improves the prediction accuracy for various outcomes (e.g., plant health, soil conditions). The models are validated using a portion of the data to ensure accuracy. Calibration routines are implemented to periodically adjust the models based on new data, maintaining their performance over time.

[0060] In some implementations, the prediction analysis module 154 may utilize competitive adaptive reweighted sampling (CARS) variable selection techniques to select the most relevant wavelengths from spectral data, thus enhancing the predictive accuracy of models. In some implementations, the prediction analysis module 154 may utilize successive projections algorithm (SPA) variable selection techniques to reduce redundant information by selecting the most relevant and non-collinear wavelengths from spectral data, thus enhancing the predictive accuracy of models. In some implementations, the prediction analysis module 154 may utilize support vector machines (SVM) as a powerful machine learning technique for classification and regression task for analyzing the spectral data to improve accuracy and robustness of the one or more predictive models(e.g., by effectively handling high-dimensional data, preventing overfitting, and capturing complex patterns, SVM may improve the reliability of the analysis).

[0061] In some implementations of the invention, the location / mapping module 156 may be utilized by the back-end soil analysis orchestration instruction set 150 to ensure precision location tracking and may determine GPS coordinates or other location technology to determine location data for each soil sample. The location data may be embedded as metadata when acquiring the soil data. The soil analysis and prediction server 140 may collectively analyze, via the location / mapping module 156, multiple different user device(s) 110 location data to generate a system wide map (e.g., pull together soil data from several different cultivators for a large farm / ranch area based on the location data). The location / mapping data may be collected and stored in the location mapping database 144. As described in greater detail below, the different sensed / mapped qualities of the soil can be graphically displayed to the operator via the user interface 114.

[0062] In some implementations of the invention, the user interface module 158 may be utilized by the back-end soil analysis orchestration instruction set 150 to may be utilized for generating and managing a soil analyzer user interface (e.g., a front-end application programming interface (API)) at a user interface 114 of a user device 110 (e.g., an operator's device such as a mobile phone, laptop, computer, and the like).

[0063] The soil analysis and prediction server 140 may be a front-end server for managing, collecting, processing, and communicating soil sensor data, from multiple devices and / or one or more other sources. For example, the soil analysis and prediction server 140 may be a central server for processing multiple user device(s) 110 that are simultaneously collecting soil data for the same general area (e.g., multiple farmers cultivating a large area). In some implementations of the invention, the previous soil analysis information from the soil analysis database 142 and / or the previous location information from the location mapping database 144 may also be accessed by the application 112 on the user device 110.

[0064] Figure 2 illustrates an example environment 200 of a side view of an embodiment of an agricultural machine that includes tiller blades for cultivating soil, according to embodiments of the invention. In particular, Figure 2 illustrates an agricultural machine 202 (e.g., a tractor) pulling a cultivator 204 that includes a group of tiller blades 206 that are cultivating the soil 208 of an area of land. The expanded area 210 is an expanded view of an example tiller blade assembly 215 of one tiller blade of the group of tiller blades 206. According to embodiments of the invention, one or more soil sensors (e.g., soil sensor 130) may be added to the tiller blade assembly 215 to analyzethe soil 208. For example, all and / or a portion of a sensor 130 may be positioned at a lower / distal end 220 of one tiller blade assembly 215, such that, as the agricultural machine 202 pulls the cultivator 204, an end portion of the tiller blades 206 (e.g., the blade) cultivates the soil, and a soil sensor 130, if positioned at or near the end 220, can measure one or more soil properties of the soil 208, as further discussed herein.

[0065] In some implementations, the soil sensor may include a power source and / or a wireless transmitter to independently (e.g., wirelessly) communicate soil sensor data to a soil analysis and prediction system (e.g., transmit the soil data from a soil sensor 130 to a soil sensor module 124 of a user device 110, as illustrated in Figure 1). Additionally, or alternatively, in some implementations, the soil sensor 130 may be connected to a power source (not shown) and / or a wireless transmitter (not shown) to provide the soil sensor data to a soil analysis and prediction system (e.g., transmit the soil data from a soil sensor 130 to a soil sensor module 124 of a user device 110, as illustrated in Figure 1).

[0066] Figures 3A-3E illustrate views of an agricultural apparatus 300A-300D (e.g., tiller blade assemblies), respectively, that includes one or more soil sensors that may be coupled to an end of an arm of the agricultural apparatus, according to embodiments of the invention. The views of the agricultural apparatus 300 provide different views of the tiller blade assembly 215 of one tiller blade of the group of tiller blades 206 of the cultivator 204 as illustrated in Figure 2. In particular, Figure 3A is a perspective view of an agricultural apparatus 300A with controller box 330 that includes the enclosure (housing) 310 and window 312, and inside the controller box 330 are sensors . The enclosure (housing) 310 is coupled to the agricultural apparatus 300A (e.g., via a mounting bracket 302). Each sensor 340, 342, may include a sensing probe (not illustrated) that is attached by a cable, or wirelessly coupled, to the sensor system as illustrated by sensor 340, 342. The sensor probe for each sensor 340, 342 may be positioned at an end of the arm 320 of the agricultural apparatus 300A as further illustrated herein. The arm 320 of the agricultural apparatus 300A includes a blade 321 that provides the main functionality of the agricultural apparatus 300A that contacts soil.

[0067] The arm 320 of the agricultural apparatus 300A further includes a soil sensor protection mechanism 322 for protecting a communication cable (e.g., fiber optic cable) that is attached to the soil sensors 340, 342. The sensor protection mechanism 322, in the illustrated embodiment, takes the form of a pair of parallel plates defining a gap therebetween, which can receive the communication cable. The arm 320 of the agricultural apparatus 300A may further include a shock absorber 323 for impact reduction protecting a probe that may be connected to the communication cable (e.g.,fiber optic cable) that is attached to the soil sensors 340, 342. As illustrated, the sensors 340, 342, may include the main sensor components such as a housing, the computing and soil analysis hardware (e.g., a microcontroller, etc.), and / or power for the sensors (e.g., a battery). The sensors 340, 342 may be positioned at several different locations of the agricultural apparatus 300A, as long as a communication cable (e.g., fiber optic cable) to a corresponding sensor probe that could contact the soil for each respective sensor 340, 342. However, in some implementations, the communication from the sensors 340, 342, to a corresponding sensor probe 315 (e.g., illustrated in Figure 3B) may be wireless.

[0068] Figure 3B is a side view of the agricultural apparatus 300B with a first sensor 340 and a second sensor 342 placed next to the end of the arm 320 of the agricultural apparatus 300B. Figure 3B illustrates a communication cable 311 for sensor 340, and a communication cable 313 for sensor 342, that are connected together to a singular communication cable 314 that is in turn connected to a soil sensor probe 315. In an exemplary embodiment, two or more sensors (e.g., sensor 340 and sensor 342) may share the same sensor probe (e.g., sensor probe 315). Alternatively, in some embodiments, each separate sensor (e.g., sensor 340 and sensor 342) may each have a separate respective sensor probe that contacts soil. Figure 3B illustrates another view of the soil sensor protection mechanism 322 for protecting a communication cable (e.g., communication cables 311 , 313, and / or 314) and / or protecting the sensor probe 315. The arm 320 of the agricultural apparatus 300A may further include a shock absorber 323 for impact reduction protecting a probe that may be connected to the communication cable (e.g., fiber optic cable) that is attached to the soil sensors 340, 342. Additionally, Figure 3B illustrates a communication cable encasement 324 for protecting the communication cable (e.g., communication cables 311 , 313, and / or 314). In some embodiments, the communication coil cable encasement 324 is directly coupled to the soil sensor protection mechanism 322, as illustrated in Figure 3C.

[0069] Figure 3C illustrates another perspective view of the arm 320 of the agricultural apparatus 300C. For example, Figure 3C illustrates a communication cable encasement 324 for protecting the communication cable, and the soil sensor protection mechanism 322 which holds / connects the communication cable encasement 324 to the arm 320.

[0070] Figure 3D illustrates a view of the agricultural apparatus 300D connected to a cultivator (e.g., as illustrated in Figure 2). In particular, Figure 3D illustrates sensors 340, 342, connected to a sensor probe 315 via the communication cable 314. Figure 3D further includes a support mechanism 335 that may provide vibration isolation to the controller box 330 that is attached to the cultivator to provide vibration stabilization for thesensors 340, 342 inside the controller box 330. The support mechanism 335 may include one or more pliable U-bolts configured to securely attach the controller box 330, which includes the sensors 340, 342 (e.g., a spectroscopy sensor and an electrical property sensor) to an agricultural machine. For example, the pliable U-bolts may be made of rubber, synthetic rubber, and / or other polymers that provide vibration isolation to the controller box 330.

[0071] Figure 3E illustrates another perspective view of the arm 320 of the agricultural apparatus 300E. For example, Figure 3C illustrates the controller box 330 that includes the enclosure (housing) 310 which is connected to the communication cable 314 for obtaining the sensor data from the sensor probe 315. The controller box 330 contains the controller circuitry 332 that is connected to the sensors 340, 342.

[0072] Figure 4 illustrates an example sensor system 410 for soil analysis that is communicatively coupled to a network 102, according to embodiments of the invention. In particular, sensor system 410 illustrates an example block diagram configuration of a soil sensor 130 of Figure 1 (e.g., components within a housing 412 of a sensor, such as first sensor 340 and a second sensor 342 as illustrated in Figure 3D). The sensor system 410 includes a controller 415 (e.g., a microcontroller) for managing, processing, and controlling the data processing for the one or more components. The controller 415 is communicatively coupled to the data translator 414 for transmission of the soil data (e.g., to a user device 110 via the network 102, or by other means). The sensor system 410 may include one or more sensor(s) such as NIR spectroscopy sensor -1 420, NIR spectroscopy sensor -2425, electrical conductivity sensor 430, and other sensor(s) 440 (e.g., an ultrasound sensor, etc.). The sensor system 410 may include two or more different NIR spectroscopy sensor modules to analyze different wavelengths in the nearinfrared region of the electromagnetic spectrum (e.g., NIR spectroscopy sensor -1 420 may measure 1600nm-2400nm and NIR spectroscopy sensor -1 425 may measure 900nm-1700nm). The one or more sensor(s) may then be communicatively coupled to a sensor probe to obtain the soil data for processing (e.g., optical cable, LAN cable, wireless, etc.).

[0073] The sensor system 410 further includes a GPS module 450 for obtaining location data and a clock 460 for the microcontroller. The sensor system 410 further includes a sensor data database 470 for locally storing soil sensor data at the sensor system 410 (e.g., locally store the data such that the user can manually download the data at a later time or just for back up purposes). The sensor system 410 may further include a power source 480 (e.g., a battery) within the housing 412 of the sensor.Alternatively, a power source may be external to the housing 412 of the sensor system 410 (e.g., plugged into a power source such as on the agricultural equipment).

[0074] Figures 5A-5C illustrate an example process 500 for a data pipeline architecture for implementing an integrated soil analysis and prediction process for precision agriculture, according to embodiments of the invention. In particular, Figures 5A-5C illustrate the detailed architecture of an exemplary Soil Analysis Engine, accordingly to methods described herein.

[0075] Referring to Figure 5A, the process 500 begins with field data collection from GPS modules 502, electrical conductivity (EC) sensors 504, and dual-range spectrometers 506, 508 (e.g., ranges from 900-1700nm and 1600-2400nm, respectively). The system begins with raw data collection at the microcontroller 510 from multiple field sensors, including a GPS module 502, an EC sensor 504, and two spectrometers 506, 508 operating across 900-2400 nm. These sensors capture location, spectral reflectance, and soil properties such as nitrogen, phosphorus, potassium, pH, moisture, temperature, and salinity.

[0076] The collected data is routed to a microcontroller 510 and preprocessed locally through an integration program (e.g., integration programs 512, 514, 516, 518, etc.), which allow the microcontroller to send and receive commands, and data with the various sensors. For the spectrometer data from integration programs 516, 518 are further processed at block 517 where spectral data spanning 900-1700 nm and 1600- 2400 nm are interpolated to a uniform 2 nm spectral resolution for seamless integration and analysis. The interpolated data from block 517 and the data from integration programs 512, 514 are then combined in the array 520. The data from array 520 is stored temporarily on an SD card 536 and using a integration program 522 which allows hardware communication between the microcontroller 510 and a low-power wide-area network radio translator 530 and antenna 532 to a remote gateway 534 transmitted, where it is then uploaded 538 to the cloud or a server.

[0077] To support real-time decision-making, the system employs a high- throughput, low-latency architecture. Field data from a GPS module, dual-range NearInfrared and Short-Wave Infrared (NIR / SWIR) spectrometers (900-1700 nm and 1600- 2400 nm), and a multi-functional electrical property sensor are simultaneously acquired and preprocessed by an onboard microcontroller 510. This 7-in-1 sensor measures EC, temperature (via an integrated thermistor), salinity (inferred from EC), moisture, pH, and macronutrients (N, P, K). Combined with spectral data, these measurements enable the estimation of additional parameters such as organic carbon and soil structure through machine learning. While the claims emphasize EC and NPK, the sensor suite supportsbroader agronomic and predictive capabilities. The microcontroller aligns and averages sensor inputs in under 500ms, temporarily caches the data on local SD storage, and transmits it via a low-power wide-area network radio every 2-5 seconds, ensuring responsive operation even in low-connectivity environments.

[0078] Once received by the gateway, data is immediately uploaded to a cloudbased server infrastructure via Long Term Evolution (LTE) or Ethernet uplinks. The backend supports stream-based ingestion into a time-series database with sub-second latency using Message Queuing Telemetry Transport (MQTT) and Apache Kafka pipelines. This ensures that field-level readings are accessible for Al processing and dashboard visualization within approximately 3-10 seconds from the time of collection, depending on network conditions. This multi-layer architecture ensures robustness in remote deployments, where intermittent connectivity is buffered by local caching, and rapid cloud-side processing maintains the system’s “real-time” classification. The modularity of the pipeline also allows future expansion for higher-frequency sampling, edge-based Al inferencing, or satellite integration without disrupting current functionality.

[0079] Referring to Figure 5B, the process 500 proceeds to a cloud environment (e.g., cloud server 550), where the incoming data is first ingested as an array 560. From this array 560, wavelength and absorbance values 562 are extracted. The process 500 then analyzes the transmitted data from the microcontroller 510 and applies preprocessing techniques 563, including Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Principal Component Analysis (PCA). PCA is used in conjunction with auxiliary variables such as moisture, temperature, and pH (e.g., block 561) to reduce spectral noise by accounting for their influence on spectral readings. This is followed by feature selection 564 using statistical techniques such as Spearman correlation, Analysis of Variance (ANOVA), and Recursive Feature Elimination (RFE). The cleaned dataset is then partitioned using K-fold cross-validation 565 to support uncertainty estimation and model validation. Machine learning models including Partial Least Squares Regression (PLSR), RuleFit, Support Vector Regression (SVR), Transformer-based Convolutional Neural Networks (CNNs), and Gradient Boosting (e.g., block 566) are subsequently applied to predict soil properties at block 567 such as nitrogen (N), phosphorus (P), potassium (K), pH, moisture content, and total carbon. These predictions are geotagged using the location data from the array 560.

[0080] A historical validation model 540 compares predicted soil values against past regional datasets 542 to ensure realism and consistency based on the instructions 544. Predictions falling outside reasonable ranges are removed and interpolated based on nearby validated values and are sent to block 570 in Figure 5C. The final outputsinclude an array 572 of predicted soil parameters including but not limited to Total Nitrogen, Available Potassium, Available Phosphorus, Total Carbon, Cationic Exchange Capacity, pH, and Moisture. These are then processed at block 576 for a recommendation program by which crop data from 542 is used to calculate nutrient removal rates from available nutrients from 572 making fertilizer recommendations and sent to array 574, which can be exported to a frontend dashboard or transmitted directly to fertilizer application systems at block 578.

[0081] Figure 6 illustrates an example process 600 for a data acquisition processing and output of the soil fertility assessment, according to embodiments of the invention. In particular, Figure 6 illustrates a high-level overview of process 500 in Figure 5. For example, the soil property prediction system may include the soil properties predicted in process 500, and process 600 integrates these results with external datasets including historical yield records, satellite and drone imagery, weather application programming interfaces (APIs), and farm management practices across four primary layers: data acquisition, integration and storage, machine learning, and output visualization. This clear sequencing reinforces the distinct yet connected roles of each pipeline and ensures transparency for examiners and users regarding their technical interaction. It integrates predicted soil properties with external data inputs (e.g., weather, market, historical yield) across four stages: data acquisition, storage, modeling, and output visualization. The yield forecast engine supports trader-specific tools and scenario testing features.

[0082] The first layer 610 includes inputs such as real-time soil data 612, historical yield records 614, satellite and drone imagery 615, weather data APIs 616, and farm management practices 618. These inputs are transmitted to a unified cloud database and data lake 625 of the second layer 620 (e.g., integration and storage), enabling real-time access and seamless data fusion.

[0083] The third layer 630 of the process 600 utilizes baseline machine learning models 632 such as Random Forest and XGBoost, alongside advanced models 634 including Stacking Regressors, RuleFit, Support Vector Regression (SVR), Neural Networks (NN), Gradient Boosting, Deep Neural Networks (DNN), and Long Short-Term Memory (LSTM) networks. A real-time adjustment module 636 may incorporate Bayesian methods and scenario learning, dynamically refining predictions based on outputs received from the soil property prediction pipeline.

[0084] The final output layer 640 includes interfaces 645 for web and mobile dashboards. These dashboards present yield forecasts, management recommendations, and scenario-based risk assessments. Real-time data updates enable farmers andstakeholders to make timely, informed decisions regarding crop performance and input strategies.

[0085] Figure 7 illustrates an example screenshot 700 for soil analysis and prediction processes via a soil analyzer user interface 701 , according to embodiments of the invention. The soil analyzer user interface 701 may also be referred to herein as a “Soil Analysis / Prediction Support Tool.’’ The example screenshot 700 illustrates an example heatmap (e.g., selected element 710).

[0086] As illustrated in Figure 7, the soil analyzer user interface 701 includes a soil analysis mapping application 720. For example, after a user / operator (e.g., a farmer) has completed scanning an area of land, the soil analysis mapping application 720 can provide color coded shading of areas based on the one or more different soil measurements so a user can quickly identify different areas based on the respective soil properties. For example, area 722 may be shaded in red to signify a higher level of an issue, area 724 may be shaded in green to signify a lower level, and area 726 may be shaded in purple to signify another level.

[0087] As illustrated in Figure 7, the soil analyzer user interface 701 may further include a generative artificial intelligence (Al) assistant interface 740. For example, the generative Al assistant interface 740 may provide advice to a user (e.g., a farmer) on how to treat different areas of the land.

[0088] Figure 8 illustrates an example screenshot 800 for soil analysis and prediction processes via a soil analyzer user interface 801 , according to embodiments of the invention. The example screenshot 800 illustrates an example farmer dashboard which displays spatial nutrient maps, trend charts, activity logs, and supports agronomic guidance via the Tilly Al assistant. As illustrated in Figure 8, the soil analyzer user interface 801 includes a soil analysis dashboard application 810. The interface includes a field-level heatmap display showing spatially variable nutrient levels across the selected field, using a color-coded system to distinguish between low, optimal, and high values of nitrogen, phosphorus, and potassium. For example, after a user / operator (e.g., a farmer) has completed scanning an area of land, the soil analysis dashboard application 810 can provide color coded shading of areas for each different type of soil measurement for a given area. For example, a map application may provide a heat map of nitrogen levels, a heat map of phosphorus levels, and a heat map of potassium levels.

[0089] The dashboard features of the user interface 801 further include a conversational Al assistant panel 830, referred to as Tilly, which provides contextual agronomic recommendations based on field data. The chat interface displays user inquiries and Al-generated responses in a visually distinct, message-thread format. TheAl assistant includes shortcut buttons for frequently used queries such as nutrient levels, weather forecasts, and fertilizer applications. The integration of the Al assistant provides accessible guidance that helps users navigate complex agronomic and market decisions without requiring deep technical expertise. Meanwhile, the standardized Soil Health Scoring System simplifies the interpretation of complex soil data, making it more actionable for users with varying levels of agronomic knowledge.

[0090] The dashboard features of the user interface 801 further include a nutrient trend chart 814, a history panel logging recent agricultural actions (e.g., irrigation, soil sampling, fertilizer applications), and action buttons such as “Apply Fertilizer” or “View History.” The integration of real-time field data, Al recommendations, and actionable controls in a unified interface enables efficient and informed decision-making directly from the field.

[0091] Figure 9 illustrates an example screenshot 900 for soil analysis and prediction processes via a soil analyzer user interface 901 , according to embodiments of the invention. The example screenshot 900 illustrates an example trader dashboard that presents regional yield forecasts, live market pricing data from one or more commodity exchanges pricing, and scenario controls for weather and input cost risks. As illustrated in Figure 9, a soil analyzer user interface 901 includes a national insights application 910. In some embodiments, a Regional Yield Forecast Map section 920 is configured to present a color-coded display of national and sub regional yield projections. A Nitrogen Recommendation section 930 may generate management guidance based on analyzed nitrogen levels; a Phosphorus Recommendation section 940 may generate management guidance based on analyzed phosphorus levels; and a Potassium Recommendation section 950 may generate management guidance based on analyzed potassium levels.

[0092] In some embodiments of the invention, the farmer dashboard (Figure 8) and the trader dashboard (Figure 9) convert soil and yield predictions into decision-ready formats, allowing users to act without needing to interpret raw model outputs. To support real-time utility, the system uses asynchronous data ingestion. Soil data updates every 1- 10 minutes based on network conditions, while forecast and market data refresh hourly or daily, depending on external API and model schedules. This hybrid update model ensures both fast agronomic feedback and timely market forecasting, even in bandwidthlimited environments.

[0093] Figure 10 illustrates an example screenshot 1000 for soil analysis and prediction processes via a soil health scoring user interface 1001 , according to embodiments of the invention. The example screenshot 1000 illustrates an example soil health scoring application 1010. As illustrated in Figure 10, the soil health scoring userinterface 1001 provides a soil health scoring system that may be derived from the soil property pipeline outputs, according to embodiments of the invention. This soil health scoring user interface 1001 consolidates outputs from the soil property prediction pipeline described herein into a standardized score. A composite gauge visualizes the overall health rating (0-100), color-coded for ease of interpretation.

[0094] In an exemplary embodiment, the soil health scoring system may calculate the composite Soil Health Score 1018 by determining three subscores: 1) Biological Activity Score 1012 (based on microbial indicators and SOM, predicted via RuleFit and NN), 2) a Structural Integrity Score 1014 (based on compaction and texture from EC + PLSR), and 3) a Nutrient Balance Score 1016 (using NPK predictions from Support Vector Regression (SVR) and RuleFit).

[0095] The Biological Activity Score 1012 is derived from indicators including soil organic matter (SOM), microbial respiration, and soil carbon ratios. These variables are estimated using reflectance in key spectral bands (e.g., 1400 nm, 1900 nm), electrical conductivity (EC) signal variability, and principal component analysis (PCA) transformations of spectral inputs. RuleFit models provide interpretable insights into dominant microbial predictors, while neural networks model complex non-linear relationships among bio-indicators. This score reflects dynamic microbial functions that govern nutrient cycling and long-term soil health and is weighted at 35% in the overall composite score due to its foundational role in soil fertility.

[0096] The Structural Integrity Score 1014 represents the soil’s physical structure and its ability to retain water, resist erosion, and permit root growth. It is computed from electrical conductivity depth profiles, inferred bulk density, and aggregate stability indices. Partial Least Squares Regression (PLSR) integrates these features to estimate compaction levels and textural class (sand, silt, clay ratios). A spatial variance model also identifies structural heterogeneity (e.g., plow pans or compacted zones). This score is weighted at 30% in the overall composite due to its influence on water infiltration and crop resilience.

[0097] The Nutrient Balance Score 1016 evaluates the bioavailability and agronomic balance of macronutrients, particularly nitrogen, phosphorus, and potassium (NPK). Near-infrared and short-wave infrared reflectance patterns, EC-derived ion mobility indicators, and inferred cation exchange capacity are processed using Support Vector Regression (SVR) and RuleFit. The model accounts for antagonistic nutrient interactions through a derived metric Nutrient Efficiency Index (NEI) that penalizesimbalanced ratios. This sub score, weighed at 35%, supports precise fertilizer recommendations and environmentally responsible nutrient management.

[0098] Each sub score is normalized on a 0-100 scale using percentile-based scaling derived from regional agronomic baselines. The composite Soil Health Score 1018 is computed as: Soil Health Score (SHS) = 0.35 x B + 0.30 x s + 0.35 x N, where B, S, and N represent the Biological Activity Score 1012, Structural Integrity Score 1014, and Nutrient Balance Score 1016, respectively.

[0099] This composite Soil Health Score 1018 may be visualized through a dashboard-integrated gauge (color-coded red, yellow, green), and a radar chart displaying each sub score. In some implementations, when critical thresholds are crossed, the system may trigger Al-generated recommendations from the Al assistant such as deep tillage for compaction, compost application for low biological activity, or specific nutrient blends for imbalances. For example, the critical thresholds may include a bulk density greater than about 1.6 g cm-3or penetrometer resistance above about 2 MPa (= 300 psi) in the root zone; an active carbon below about 350 mg kg-1or 24 hour soil respiration below about 0.5 mg CO2C g~1; pre-sidedress nitrate N outside a range of about 10 ppm (deficient) to about 40 ppm (excess) obtained via a pre-sidedress nitrate test (PSNT); Bray Pi phosphorus below about 15 ppm or above about 50 ppm; and / or exchangeable potassium below about 100 ppm or above about 250 ppm.

[0100] The soil health scoring user interface 1001 further includes a score-over- time chart 1020 to visualize and track soil health trends and a benchmarking widget 1030 to compare soil performance to regional standards. These tools guide users in identifying opportunities for improvement and evaluating the long-term impacts of agronomic interventions.

[0101] These scores are directly integrated with the recommendation engine and dashboard interface, allowing an Al interface (e.g., a virtual assistant) or user prompts to generate actionable prescriptions based on score thresholds or deterioration over time.

[0102] In some implementations, the soil Health Scoring System illustrated in the soil health scoring user interface 1001 of Figure 10 may be directly integrated with the recommendation engine, translating scores into specific management actions that can improve soil health in deficient areas. This creates a clear path from measurement to action for users. The soil Health Scoring System represents a significant advancement in agricultural technology by bridging the gap between field-level soil data and market-level trading decisions. By combining real-time soil analysis with sophisticated prediction models and dual interfaces for different user types, the system creates a comprehensive platform that serves the needs of both agricultural producers and commodity traders.

[0103] Figure 11 illustrates an example screenshot 1100 for soil analysis and prediction processes via a forecasting interface 1101 , according to embodiments of the invention. The example screenshot 1100 illustrates an example yield forecasting application which presents crop yield forecasts (in bushels per acre) generated by the Yield Forecasting Pipeline. Forecasts are shown under multiple climate scenarios, including normal, dry, and wet conditions, as seasonal line charts.

[0104] As illustrated in Figure 11 , the forecasting interface 1101 includes market forecast graphs 1110, which display monthly price projections for major commodities such as corn and soybeans. These are generated using environmental and soil condition data linked from the earlier prediction stages. The forecasting interface 1101 further includes scenario filters 1120 which allow users to dynamically adjust forecast views based on irrigation levels, drought stress, or macroclimate factors such as El Nino. These controls help model yield and market responses under different risk profiles. In some implementations, users may export forecast reports directly from the interface and integrate predictions into downstream decision-making tools. This forecasting interface 1101 serves both agronomic planning and financial strategy for commodity producers and traders.

[0105] Figure 12 illustrates an example screenshot 1200 for soil analysis and prediction processes via a soil health dashboard interface 1201 , according to embodiments of the invention. The dashboard interface 1201 displays a sustainability report dashboard 1210 that includes a composite soil health score. The composite soil health score is described herein with reference to Figure 10. The sustainability report dashboard 1210 includes a “Nutrient Balance” section 1220 that displays bar graphs or gauges of measured N, P, K and micronutrient levels against agronomic targets; a “Soil Health Trend” section 1230 that plots the composite soil health score across sampling periods; and a “Carbon Sequestration” section 1240 that estimates soil organic carbon stock and annual sequestration rate (t CO2eq ha-1yr1), and a “Management Recommendation” section 1250 that generates adaptive practice guidance based on the analyzed parameters. In some embodiments, the dashboard 1210 may further include a “Water Use Efficiency” section that shows volumetric soil moisture trends and irrigation efficiency metrics. In some embodiments, the dashboard 1210 may additionally provide alert indicators that flag out of tolerance nutrient or pH values and benchmark panels 1280 that compare the field’s metrics with regional or national quartiles.

[0106] Figure 13 illustrates a flowchart of an example method 1300 for implementing an integrated soil analysis and prediction process for precision agriculture, according to embodiments of the invention. Operations of the method 1300 can beimplemented, for example, by a system that includes one or more data processing apparatus, such as one or more user device(s) 110 and / or a soil analysis and prediction server 140 of Figure 1. The method 1300 can also be implemented by instructions stored on computer storage medium, where execution of the instructions by a system that includes a data processing apparatus cause the data processing apparatus to perform the operations of the method 1300.

[0107] The system receives, by one or more soil sensors, a set of soil data, the one or more soil sensors in one case being coupled to a tiller blade of an agricultural machine and configured to analyze soil when a portion of the one or more soil sensors is in contact with the soil, and where the set of soil data includes spectral data, and soil conductivity and / or capacitance data (1310). For example, as illustrated in Figure 2, a soil sensor (e.g., soil sensor 130) may analyze soil 208 at the end 220 of one tiller blade assembly 215 as shown in expanded area 210 (e.g., a probe of a soil sensor 130 contacts the soil 208). As discussed in the context of Figure 1 , a soil sensor 130 may obtain spectral data via a VIS-NIR spectroscopy sensor, conductivity data via electrical sensor, and the like from an area of soil as a cultivator is dragged across / through the soil. The soil data is thus collected and analyzed from under the soil surface.

[0108] In some implementations, the one or more soil sensors comprises a spectroscopy sensor configured to determine spectral data of soil. In some implementations, the one or more soil sensors comprises an electrical property sensor configured to determine soil conductivity and / or capacitance data of soil.

[0109] The system determines analyzed soil data based on the spectral data and soil conductivity and / or capacitance data (1320). For example, a microcontroller (e.g., controller 415 of the sensor system 410) processes the soil data and stores it on a local physical drive (e.g., sensor data database 470). In some implementations, the data is transmitted wirelessly from the sensors a user’s device using LoRa technology, or other long range electromagnetic technology.

[0110] The system provides the analyzed soil data for display at a user device (1330). For example, as illustrated in Figures 5 and 6, the integrated soil analysis and prediction system provides a visualization of the analyzed soil data through a web application, e.g., a soil analyzer GUI (e.g., soil analyzer user interface 501). Furthermore, as illustrated in Figures 7-12, a soil analyzer user interface may display predicted soil analysis and location mapping features at a user device (e.g., via a web based via browser, an application on a user’s device, and the like). Some of the features may include real-time data display such as interactive dashboards showing soil parameters such as moisture levels, pH, NPK content, and carbon levels. Additionally, some of thefeatures provided by the soil analyzer user interface may include Al-generated insights such as crop management recommendations based on real-time data analysis, carbon sequestration tools such as estimation and visualization of potential carbon sequestration, and / or diagnostic tools such as real-time monitoring and error detection for system components.

[0111] In some embodiments of the invention, determining the analyzed soil data is based on one or more processing techniques and machine learning models. For example, as discussed herein, the prediction analysis module 154 (which may be provided by the user device 110) includes preprocessing techniques (e g., SST, SNV, MSC, interpolation of missing values, outlier removal, and the like), PCS, machine learning techniques (e.g., PLSR, SVR, NN, cubist-like models, etc.), and the like.

[0112] In some embodiments of the invention, the tiller blade comprises a soil sensor protection mechanism configured to protect the one or more soil sensors during operation of the tiller blade when the at least the portion of the one or more soil sensors are positioned below the upper surface of the soil. For example, as illustrated in Figures 3A-3D, an end of the agricultural apparatus 300 (e.g., a tiller blade) includes a soil sensor protection mechanism that protects the one or more soil sensors during operation of the tiller blade when the one or more soil sensors are below a surface of the soil. The soil sensor protection mechanism may include a cable protector cavity (e.g., soil sensor protection mechanism 322) that houses communication cables 311 , 313, and / or 315 that are connected to the first sensor 340 and the second sensor 342. The sensors 340, 342 may be connected to a separate power source (not shown) and / or a separate wireless transmitter (not shown), or the power source and wireless transmitter may be embedded within the housing of each sensors 340, 342. The wireless transmitter may be used to provide the soil sensor data to a soil analysis and prediction system (e.g., transmit the soil data from a soil sensor 130 to a soil sensor module 124 of a user device 110, as illustrated in Figure 1).

[0113] In some embodiments of the invention, the method 1300 further includes receiving climate data from a climate data source, and updating the analyzed soil data based on the climate data (e.g., climate data integration for contextual analysis).

[0114] In some embodiments of the invention, the analyzed soil data is displayed on a soil analyzer user interface that comprises one or more real-time interactive dashboards, wherein each real-time interactive dashboard is associated with one or more soil parameters.

[0115] In some embodiments of the invention, the one or more soil parameters includes moisture levels, potential of hydrogen (pH) levels, nitrogen, phosphorus, and potassium (NPK) content, carbon levels, or a combination thereof.

[0116] Figure 14 illustrates a flowchart of an example method 1400 for implementing an integrated soil analysis and prediction process for precision agriculture, according to embodiments of the invention. Operations of the method 1400 can be implemented, for example, by a system that includes one or more data processing apparatus, such as one or more user device(s) 110 and / or a soil analysis and prediction server 140 of Figure 1. The method 1400 can also be implemented by instructions stored on computer storage medium, where execution of the instructions by a system that includes a data processing apparatus cause the data processing apparatus to perform the operations of the method 1400.

[0117] The system obtains soil data from one or more sensors at block 1410. For example, one or more spectroscopy sensors and / or electrical property sensors may be used to obtain soil data for analysis. For example, a sensing module may couple (i) a NIRvascan Smart Near Infrared Spectrometer (900-1700 nm), (ii) a NIRvascan Smart NIR Extended Plus Spectrometer Model F13 (1600-2400 nm), and (iii) a five pin pH / NPK / temperature humidity EC probe. The probes may be inserted into the soil at a depth of about 4-6 in. (10-15 cm), where the spectrometers acquire six scans in roughly three seconds under tungsten halogen illumination and average the results before forwarding the co located spectral and conductivity data for preprocessing at block 1420.

[0118] The system, at block 1420, preprocesses the soil data using Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) techniques. For example, at block 1420, the system may apply a two-step scatter correction routine. First, a standard normal variate (SNV) transform is applied to each raw reflectance spectrum R(A): the spectrum’s mean is subtracted and the result divided by its standard deviation, yielding a mean centered, unit variance vector that removes additive offsets and global scaling caused by variable moisture and probe surface geometry. The module then performs multiplicative scatter correction (MSC) by linearly regressing the SNV treated spectrum against a reference mean spectrum Rref(A) to obtain slope a and intercept b; the corrected spectrum is (RSNV (A) - b ) / a. Together, SNV and MSC suppress particle size dependent scattering and illumination artefacts, exposing the chemical absorption features needed by modelling at block 1430.

[0119] The system, at block 1430, analyzes the preprocessed soil data using a model ensemble that includes a plurality of machine learning models. For example, thesystem may analyze the preprocessed data using a stacked ensemble of machine learning models. In one embodiment, the base learners comprise partial least squares regression (PLSR), random forest, gradient boosted trees (XGBoost), support vector regression, RuleFit, and a one dimensional convolutional neural network; a transformer CNN may be substituted in alternative builds. In some embodiments, predictions from the base learners may be passed to a linear meta learner that assigns weights by minimizing 5 fold cross validation error over approximately 20,000 training spectra.

[0120] The system, at block 1440, determines a quantification for uncertainty in predictions using a plurality of assessment methods. In some implementations, the system may first construct a 95 % bootstrap prediction intervals by resampling the calibration set 1 ,000+ times, refitting the ensemble, and taking the 2.5th and 97.5th percentiles. For in domain soils, these bounds may span ± 5 - 15 % of the predicted value for properties such as total nitrogen, organic carbon, pH, and available phosphorus. To capture epistemic variance in the deep learning branch, the may next apply a Monte Carlo dropout, executing thirty stochastic forward passes and extracting the 10th— 90th percentiles (80 % interval), which empirically narrows to about ± 8 - 12 % of the estimate on well represented spectra. In some implementations, Interval half width may be mapped to a user configurable traffic light palette green when < 10 % of the point estimate, amber for 10-20 %, and red when > 20 % to give growers and traders an immediate visual gauge of risk. In latency sensitive deployments either method may be replaced by a quantile regression forest (QRF) decoder, which outputs prediction quantiles in a single pass while preserving the same color band logic.

[0121] The system, at block 1450, generates a field-level soil analysis and nutrient mapping for display on a farmer dashboard. In some implementations, the system may utilize georeferenced point measurements that may be interpolated onto a 5 m x 5 m grid by ordinary kriging the geostatistical method most often reported to minimize error in soil nutrient surfaces while an inverse distance weighting (IDW) option is retained for rapid previews. The resulting layer stack may include pH, total nitrogen, available phosphorus, exchangeable potassium, and soil organic matter (SOM). Each layer may be stored as both GeoTIFF and shapefile to support in browser raster tiling and offline Geographic Information System (GIS) analysis. In some implementations, these rasters may be streamed to the dashboard where color ramped tiles render variable rate prescription zones and historical trend overlays for grower decision making.

[0122] The system, at block 1460, generates a regional yield forecast and a market analytics for display on a trader dashboard. In some implementations, the pipeline ingests (i) cloud masked Sentinel 2 NDVI composites at 10 m resolution, (ii) daily weatherobservations from the National Oceanic and Atmospheric Administration (NOAA) Global Historical Climatology Network (GHCN) network, and (iii) Chicago Mercantile Exchange (CME) front month futures curves for the target crop. A hybrid learner couples a gradient boosted tree (GBM), which captures spatial heterogeneity, with a long short term memory (LSTM) network that models temporal dynamics (e.g., stacking the two reduces error versus either model alone). In some implementations, the system may be trained on several seasons of county level yield data, and the ensemble may achieve a mean absolute percentage error of= 7 % in five fold cross validation. In some implementations, data feeds may be refreshed daily for weather and futures and weekly for NDVI. In some implementations, the forecast layer may be rebuilt nightly and published as tiled GeoTIFFs for map rendering and Comma Separated Values / Representational State Transfer (CSV / REST) endpoints for quantitative traders.

[0123] The system, at block 1470, determines at least one scenario testing tool that incorporates soil health data into commodity price predictions based on the generated field-level soil analysis and nutrient mapping of the at least one agricultural field. For example, as illustrated in Figure 9, the example screenshot 900 illustrates an example trader dashboard that presents regional yield forecasts, live market pricing data from one or more commodity exchanges pricing, and scenario controls for weather and input cost risks, and includes a national insights application 910. Furthermore, as illustrated in Figure 11 , the forecasting interface 1101 includes scenario filters 1120 which allow users to dynamically adjust forecast views based on irrigation levels, drought stress, or macroclimate factors such as El Nino. These controls help model yield and market responses under different risk profiles. In some implementations, users may export forecast reports directly from the interface and integrate predictions into downstream decision-making tools. This forecasting interface 1101 serves both agronomic planning and financial strategy for commodity producers and traders.

[0124] Figure 15 illustrates an example computer architecture 1500 for a computer 1502 capable of executing the software components described herein for the sending / receiving and processing of tasks (e.g., user device 110, soil analysis and prediction server 140, and the like). The computer architecture 1500 shown in Figure 15 illustrates a server computer, workstation, desktop computer, laptop, a server operating in a cloud environment, or other computing device, and may be utilized to execute any aspects of the software components presented herein described as executing on a host server, or other computing platform. The computer 1502 preferably includes a baseboard, or “motherboard,” which is a printed circuit board to which a multitude of components ordevices may be connected by way of a system bus or other electrical communication paths. In one illustrative embodiment, one or more central processing units (CPUs) 1504 operate in conjunction with a chipset 1506. The CPUs 1504 can be programmable processors that perform arithmetic and logical operations necessary for the operation of the computer 1502.

[0125] The CPUs 1504 preferably perform operations by transitioning from one discrete, physical state to the next through the manipulation of switching elements that differentiate between and change these states. Switching elements may generally include electronic circuits that maintain one of two binary states, such as flip-flops, and electronic circuits that provide an output state based on the logical combination of the states of one or more other switching elements, such as logic gates. These basic switching elements may be combined to create more complex logic circuits, including registers, adders- subtractors, arithmetic logic units, floating-point units, or the like.

[0126] The chipset 1506 provides an interface between the CPUs 1504 and the remainder of the components and devices on the baseboard. The chipset 1506 may provide an interface to a memory 1508. The memory 1508 may include a random access memory (RAM) used as the main memory in the computer 1502. The memory 1508 may further include a computer-readable storage medium such as a read-only memory (ROM) or non-volatile RAM (NVRAM) for storing basic routines that that help to startup the computer 1502 and to transfer information between the various components and devices. The ROM or NVRAM may also store other software components necessary for the operation of the computer 1502 in accordance with the embodiments described herein.

[0127] According to various embodiments, the computer 1502 may operate in a networked environment using logical connections to remote computing devices through one or more networks 1512, a local-area network (LAN), a wide-area network (WAN), the Internet, or any other networking topology known in the art that connects the computer 1502 to the devices and other remote computers. The chipset 1506 includes functionality for providing network connectivity through one or more network interface controllers (NICs) 1510, such as a gigabit Ethernet adapter. For example, the NIC 1510 may be capable of connecting the computer 1502 to other computer devices in the utility provider's systems. It should be appreciated that any number of NICs 1510 may be present in the computer 1502, connecting the computer to other types of networks and remote computer systems beyond those described herein.

[0128] The computer 1502 may be connected to at least one mass storage device 1518 that provides non-volatile storage for the computer 1502. The mass storage device 1518 may store system programs, application programs, other program modules, anddata, which are described in greater detail herein. The mass storage device 1518 may be connected to the computer 1502 through a storage controller 1514 connected to the chipset 1506. The mass storage device 1518 may consist of one or more physical storage units. The storage controller 1514 may interface with the physical storage units through a serial attached SCSI (SAS) interface, a serial advanced technology attachment (SATA) interface, a fiber channel (FC) interface, or other standard interface for physically connecting and transferring data between computers and physical storage devices.

[0129] The computer 1502 may store data on the mass storage device 1518 by transforming the physical state of the physical storage units to reflect the information being stored. The specific transformation of physical state may depend on various factors, in different embodiments of the invention of this description. Examples of such factors may include, but are not limited to, the technology used to implement the physical storage units, whether the mass storage device 1518 is characterized as primary or secondary storage, or the like. For example, the computer 1502 may store information to the mass storage device 1518 by issuing instructions through the storage controller 1514 to alter the magnetic characteristics of a particular location within a magnetic disk drive unit, the reflective or refractive characteristics of a particular location in an optical storage unit, or the electrical characteristics of a particular capacitor, transistor, or other discrete component in a solid-state storage unit. Other transformations of physical media are possible without departing from the scope and spirit of the present description, with the foregoing examples provided only to facilitate this description. The computer 1502 may further read information from the mass storage device 1518 by detecting the physical states or characteristics of one or more particular locations within the physical storage units.

[0130] The mass storage device 1518 may store an operating system 1520 utilized to control the operation of the computer 1502. According to some embodiments, the operating system includes the LINUX operating system. According to another embodiment, the operating system includes the WINDOWS® SERVER operating system from MICROSOFT Corporation of Redmond, Wash. According to further embodiments, the operating system may include the UNIX or SOLARIS operating systems. It should be appreciated that other operating systems may also be utilized. The mass storage device 1518 may store other system or application programs and data utilized by the computer 1502, such as soil analysis module 1522 to perform soil analysis, a location module 1524 for tracking location data of the soil that is analyzed, a prediction analysis module 1526 to perform soil analysis and the soil prediction analysis, and a user interface module 1528 for data decryption, according to embodiments described herein.

[0131] In some embodiments, the mass storage device 1518 may be encoded with computer-executable instructions that, when loaded into the computer 1502, transforms the computer 1502 from being a general-purpose computing system into a special-purpose computer capable of implementing the embodiments described herein. These computer-executable instructions transform the computer 1502 by specifying how the CPUs 1504 transition between states, as described above. According to some embodiments, the mass storage device 1518 stores computer-executable instructions that, when executed by the computer 1502, perform portions of the method 1300 and / or 1400, for implementing a data location system, as described herein. In further embodiments, the computer 1502 may have access to other computer-readable storage medium in addition to or as an alternative to the mass storage device 1518.

[0132] The computer 1502 may also include an input / output controller 1530 for receiving and processing input from a number of input devices, such as a keyboard, a mouse, a touchpad, a touch screen, an electronic stylus, or other type of input device. Similarly, the input / output controller 1530 may provide output to a display device, such as a computer monitor, a flat-panel display, a digital projector, a printer, a plotter, or other type of output device. It will be appreciated that the computer 1502 may not include all of the components shown in Figure 15, may include other components that are not explicitly shown in Figure 15, or may utilize an architecture completely different than that shown in Figure 15.

[0133] In general, the routines executed to implement the embodiments of the invention, whether implemented as part of an operating system or a specific application, component, program, object, module or sequence of instructions, or even a subset thereof, may be referred to herein as “computer program code,” or simply “program code." Program code typically includes computer readable instructions that are resident at various times in various memory and storage devices in a computer and that, when read and executed by one or more processors in a computer, cause that computer to perform the operations necessary to execute operations and / or elements embodying the various aspects of the embodiments of the invention. Computer readable program instructions for carrying out operations of the embodiments of the invention may be, for example, assembly language or either source code or object code written in any combination of one or more programming languages.

[0134] The program code embodied in any of the applications / modules described herein is capable of being individually or collectively distributed as a program product in a variety of different forms. In particular, the program code may be distributed using acomputer readable storage medium having computer readable program instructions thereon for causing a processor to carry out aspects of the embodiments of the invention.

[0135] Computer readable storage media, which is inherently non-transitory, may include volatile and non-volatile, and removable and non-removable tangible media implemented in any method or technology for storage of information, such as computer- readable instructions, data structures, program modules, or other data. Computer readable storage media may further include random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other solid state memory technology, portable compact disc read-only memory (CD-ROM), or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and which can be read by a computer. A computer readable storage medium should not be construed as transitory signals per se (e.g., radio waves or other propagating electromagnetic waves, electromagnetic waves propagating through a transmission media such as a waveguide, or electrical signals transmitted through a wire). Computer readable program instructions may be downloaded to a computer, another type of programmable data processing apparatus, or another device from a computer readable storage medium or to an external computer or external storage device via a network.

[0136] Computer readable program instructions stored in a computer readable medium may be used to direct a computer, other types of programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions that implement the functions / acts specified in the flowcharts, sequence diagrams, and / or block diagrams. The computer program instructions may be provided to one or more processors of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the one or more processors, cause a series of computations to be performed to implement the functions and / or acts specified in the flowcharts, sequence diagrams, and / or block diagrams.

[0137] In certain alternative embodiments, the functions and / or acts specified in the flowcharts, sequence diagrams, and / or block diagrams may be re-ordered, processed serially, and / or processed concurrently without departing from the scope of the embodiments of the invention. Moreover, any of the flowcharts, sequence diagrams, and / or block diagrams may include more or fewer blocks than those illustrated consistent with embodiments of the invention.

[0138] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. Furthermore, to the extent that the terms “includes”, “having”, “has”, “with”, “comprised of’, or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”

[0139] While all of the invention has been illustrated by a description of various embodiments and while these embodiments have been described in considerable detail, it is not the intention of the Applicant to restrict or in any way limit the scope of the appended claims to such detail. Additional advantages and modifications will readily appear to those skilled in the art. The invention in its broader aspects is therefore not limited to the specific details, representative apparatus and method, and illustrative examples shown and described. Accordingly, departures may be made from such details without departing from the spirit or scope of the Applicant’s general inventive concept.

Claims

CLAIMS1 . A system for real-time soil analysis, the system comprising: a spectroscopy sensor configured to determine soil spectral data; an electrical property sensor configured to determine soil conductivity and / or capacitance data; and a controller communicatively coupled to the spectroscopy sensor and to the electrical property sensor, wherein the controller is configured to: determine analyzed soil data based on at least one of spectral data received from the spectroscopy sensor or soil conductivity and / or capacitance data received from the electrical property sensor; and provide the analyzed soil data to a user device, such that the user device can display a visual representation of at least part of the analyzed soil data.

2. The system of claim 1 , wherein determining the analyzed soil data is based on one or more processing techniques and machine learning models.

3. The system of claim 1 , wherein the spectroscopy sensor and the electrical property sensor are coupled to a tiller blade of an agricultural machine.

4. The system of claim 3, wherein the tiller blade comprises a soil sensor protection mechanism configured to protect the spectroscopy sensor and the electrical property sensor during operation of the tiller blade when the spectroscopy sensor and the electrical property sensor are positioned below an upper surface of the soil.

5. The system of claim 1 , further comprising a global positioning satellite (GPS) module for tracking a location of soil contacted by the spectroscopy sensor and the electrical property sensor, wherein the GPS module is operatively coupled to the controller.

6. The system of claim 1 , further comprising a soil sensor data database for storing the spectral data and the conductivity and / or capacitance data, wherein the soil sensor data database is operatively coupled to the controller.

7. The system of claim 1 , further comprising a plurality of pliable U-bolts configured to securely attach the spectroscopy sensor and the electrical property sensor to an agricultural machine.

8. The system of claim 1 , further comprising a height-adjustment mechanism configured to position the sensors at varying depths below an upper surface of the soil.

9. The system of claim 1 , wherein the controller is configured to receive climate data from a climate data source, and revise the analyzed soil data based on the received climate data.

10. The system of claim 1 , wherein the system is wirelessly communicatively coupled to the user device.

11. The system of claim 1 , wherein the system is mounted on an agriculture vehicle configured to be controller by an operator positioned at an operator station of the agriculture vehicle, wherein the system includes the user device that is configured to be viewable by an operator positioned at the operator station, and wherein the user device is configured to display a visual representation of at least part of the analyzed soil data.

12. A computer-implemented method for real-time soil analysis and prediction, the method comprising: at an electronic device having a processor: receiving, from one or more soil sensors, a set of soil data, wherein the one or more soil sensors are coupled to a portion of an agricultural machine configured to be in contact with soil, wherein the electronic device is configured to analyze soil when a portion of the one or more soil sensors is in contact with the soil, wherein the set of soil data includes spectral data, and soil conductivity and / or capacitance data; determining, based on the spectral data and soil conductivity and / or capacitance data, analyzed soil data; and providing the analyzed soil data to a user device configured to display a visual representation of at least part of the analyzed soil data.

13. The method of claim 12, wherein determining the analyzed soil data is based on one or more processing techniques and machine learning models.

14. The method of claim 12, wherein the one or more soil sensors comprises a spectroscopy sensor configured to determine spectral data of soil.

15. The method of claim 12, wherein the one or more soil sensors comprises an electrical property sensor configured to determine soil conductivity and / or capacitance data of soil.

16. The method of claim 12, wherein the agriculture machine includes a tiller blade, wherein the one or more soil sensors are coupled to the tiller blade, and wherein the agricultural machine comprises a soil sensor protection mechanism configured to protect the one or more soil sensors during operation of the tiller blade when the at least the portion of the one or more soil sensors are positioned below an upper surface of the soil.

17. The method of claim 12, further comprising: receiving climate data from a climate data source; and updating the analyzed soil data based on the climate data.

18. The method of claim 12, wherein a representation of the analyzed soil data is displayed on a soil analyzer user interface that comprises one or more real-time interactive dashboards, wherein each real-time interactive dashboard is associated with one or more soil parameters.

19. The method of claim 18, wherein the one or more soil parameters comprises: moisture levels; potential of hydrogen (pH) levels; nitrogen, phosphorus, and potassium (NPK) content; carbon levels; or combinations thereof.

20. The method of claim 12, wherein the one or more soil sensors are in contact with soil, and wherein the electronic device is analyzing the soil data.

21. A device comprising: a non-transitory computer-readable storage medium; andone or more processors coupled to the non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium comprises program instructions that, when executed by the one or more processors, cause the one or more processors to perform the operations of the method of claims 12-20.

22. A non-transitory computer storage medium encoded with a computer program, the computer program comprising a plurality of program instructions that when executed by one or more processors cause the one or more processors to perform the operations of the method of claims 12-20.

23. A system for integrated agricultural management, comprising: a soil analysis system including spectroscopy and electrical property sensors; a dual-interface platform providing a first user interface associated with agriculture field workers and a second user interface associated with commodity traders; a machine learning module configured to process soil sensor data and correlate the processed soil sensor data with obtained weather pattern data, historical yield data, and market indicator data; and a controller configured to: generate field-level soil analysis and nutrient mapping of at least one agricultural field for display on the first user interface; generate regional yield forecasts and market analytics for display on the second user interface; and provide at least one scenario testing tool that incorporates soil health data into commodity price predictions based on the generated field-level soil analysis and nutrient mapping of the at least one agricultural field.

24. The system of claim 23, further comprising an artificial intelligence (Al) assistant configured to provide conversational guidance to users through both farmer and trader interfaces.

25. The system of claim 24, wherein the Al assistant leverages agronomic ontologies and rule-based decision trees to offer context-aware recommendations.

26. The system of claim 24, wherein the Al assistant includes both rule-based inference capabilities and real-time adaptive learning functionality.2 ~ . The system of claim 24, wherein the Al assistant includes user customization options and voice integration capabilities.

28. The system of claim 23, wherein the trader dashboard includes: a regional yield forecast map with color-coded yield indicators; a commodities pricing panel displaying market pricing data from one or more commodity exchanges pricing; market indicator visualizations showing volatility indices and moving averages; and scenario testing controls with interactive sliders for risk assessment.

29. The system of claim 23, wherein the machine learning pipeline includes: a model ensemble combining multiple machine learning techniques including Stacking Regressors, RuleFit, Support Vector Regression (SVR), Neural Networks (NN), and Gradient Boosting; an uncertainty quantification component assessing prediction confidence using multiple methods; and a meta-modeling approach for optimally combining predictions from these base models.

30. The system of claim 29, wherein the uncertainty quantification component visualizes prediction confidence using at least one of prediction intervals, Monte Carlo simulations, Bayesian inference, or model disagreement metrics.

31. The system of claim 23, further comprising a standardized Soil Health Scoring System that combines multiple soil parameters into a composite score.

32. The system of claim 31 , wherein the Soil Health Scoring System includes subscores for biological activity, structural integrity, and nutrient balance.

33. The system of claim 23, further comprising an API Integration Layer configured to enable connectivity with external farm management systems, carbon credit registries, and market data providers.

34. A method for integrated agricultural management, comprising: at an electronic device having a processor: obtaining soil data from one or more sensors; preprocessing the soil data using Standard Normal Variate (SNV) and Multiplicative Scatter Correction (MSC) techniques;analyzing the preprocessed soil data using a model ensemble that includes a plurality of machine learning models; determining a quantification for uncertainty in predictions using a plurality of assessment methods; generating field-level soil analysis and nutrient mapping of at least one agricultural field for display on a first user interface associated with agriculture field workers; generating a regional yield forecast and a market analytics for display on a second user interface associated with commodity traders; and determining at least one scenario testing tool that incorporates soil health data into commodity price predictions based on the generated field-level soil analysis and nutrient mapping of the at least one agricultural field.

35. The method of claim 34, further comprising providing conversational guidance through an artificial intelligence (Al) assistant that leverages both rule-based decision trees and adaptive learning capabilities.

36. The method of claim 34, further comprising calculating a standardized soil health score that combines multiple soil parameters into a composite value.

37. The method of claim 34, further comprising enabling data exchange with external systems through an Application Programming Interface (API) Integration Layer.

38. The method of claim 34, wherein the one or more sensors comprise at least one of a spectroscopy sensos and an electrical property sensor.

39. The method of claim 34, wherein analyzing the preprocessed soil data using a model ensemble comprises integrating the processed soil sensor data with obtained weather pattern data, historical yield data, and market indicator data.

40. A device comprising: a non-transitory computer-readable storage medium; and one or more processors coupled to the non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium comprises program instructions that, when executed by the one or more processors, cause the one or more processors to perform the operations of the method of claims 34-39.

41. A non-transitory computer storage medium encoded with a computer program, the computer program comprising a plurality of program instructions that when executed by one or more processors cause the one or more processors to perform the operations of the method of claims 34-39.

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