Soil profile intelligent diagnosis robot system based on convolutional neural network

Abstract

This invention discloses a soil profile intelligent diagnostic robot system integrating convolutional neural networks, relating to the fields of agricultural intelligent detection and intelligent robot technology. The system comprises an autonomous mobile operation module, a vertical profile detection module, a multi-dimensional in-situ perception module, an intelligent diagnostic analysis module, and a cloud-based collaborative decision-making module. The autonomous mobile operation module carries the entire machine and achieves automatic navigation and precise positioning within the work area based on path planning; the vertical profile detection module is located at the center of the chassis and is used to perform vertical soil profile drilling operations at designated points; the multi-dimensional in-situ perception module collects soil physical, chemical, and morphological characteristics in real time during the drilling process; the intelligent diagnostic analysis module performs deep learning analysis on the collected data to achieve intelligent identification of soil characteristics; and the cloud-based collaborative decision-making module completes data uploading and remote verification, and outputs fertilization and irrigation decision support information.

Description

Technical Field

[0001] This invention relates to the field of agricultural intelligent detection and intelligent robot technology, specifically to an intelligent soil profile diagnostic robot system that integrates convolutional neural networks. Background Technology

[0002] Soil resources are the material foundation for ensuring global food security, maintaining ecological balance, and promoting sustainable agricultural development. Soil profile diagnosis, as a core window for in-depth understanding, assessment, and management of soil ecosystems, occupies a cornerstone position in soil science research. By systematically characterizing the morphological features and physicochemical properties of soil vertical profiles, we can not only reveal the processes of soil formation and development but also provide indispensable key decision support for environmental protection, engineering construction safety, and land use planning.

[0003] Among these, intelligent diagnostic robot systems that integrate artificial intelligence technology and automated equipment are becoming a technological breakthrough in this field. Their core lies in utilizing the powerful feature analysis capabilities of convolutional neural networks to perform deep modeling of soil profile information. By integrating high-precision sensor arrays and machine vision modules, these systems aim to simulate or even surpass the cognitive logic of human experts, achieving autonomous perception and intelligent recognition of key indicators such as soil profile stratification, color, texture, and structure. This transforms traditional human experience into replicable and scalable digital expert capabilities.

[0004] Current technologies for soil profile diagnosis still face significant challenges. Traditional diagnostic methods rely heavily on expert experience, leading to strong subjectivity in soil layer classification, compaction assessment, and structure interpretation. Diagnostic conclusions often differ significantly among different operators, making it difficult to establish unified standards. While existing sampling equipment improves operational efficiency to some extent, its level of intelligence is insufficient, failing to achieve continuous in-situ acquisition of multiple physicochemical data and lacking the ability to deeply extract and logically integrate complex soil environmental characteristics. This lag in the transformation from experience-based qualitative judgment to objective quantitative analysis not only limits the accuracy and scientific rigor of diagnostic results but also significantly hinders the construction of soil profile big data platforms and the application of intelligent decision-making. Therefore, there is an urgent need for an intelligent soil profile diagnostic solution that can integrate deep learning algorithms and achieve fully automated monitoring throughout the entire process. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a soil profile intelligent diagnostic robot system that integrates convolutional neural networks, solving the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: a soil profile intelligent diagnostic robot system integrating convolutional neural networks, comprising an autonomous mobile operation module, a vertical profile detection module, a multi-dimensional in-situ perception module, an intelligent diagnostic analysis module, and a cloud-based collaborative decision-making module; The autonomous mobile operation module and the autonomous mobile operation chassis are used to carry the entire robot system and perform fully automatic navigation and precise positioning in the target operation area according to the preset path planning instructions. The autonomous mobile operation chassis integrates a power drive module, a tracked walking mechanism and a multi-sensor fusion obstacle avoidance system to ensure that the robot has high passability and environmental adaptability in complex farmland and field environments.

[0007] The vertical profile detection module and the vertical profile detection actuator are installed at the central axis of the autonomous mobile operating chassis. They are used to perform vertical deep excavation or in-situ drilling of the soil profile after reaching the designated coordinate point. The vertical profile detection actuator includes a high-precision servo motor, a spiral drill bit, and a pressure feedback balance module. It can sense the resistance changes during the drilling process in real time and dynamically adjust the operating power to ensure the flatness and verticality of the sampling surface.

[0008] The multidimensional in-situ sensing module, with its multidimensional in-situ sensing array arranged on the side wings or follower slider of the vertical profile detection actuator, is used for real-time data acquisition of physical, chemical, and morphological characteristics within the detection depth range. The multidimensional in-situ sensing array integrates an ultra-high-definition optical imaging module, a hyperspectral scanning sensor, a multi-band soil moisture detector, a miniature electronic varistor array, and an electrochemical impedance spectroscopy analyzer.

[0009] The intelligent diagnostic analysis module, with its deep learning diagnostic engine built into the robot's main control computer, is used for end-to-end feature extraction and logical analysis of the raw signals collected by the multi-dimensional in-situ sensor array. The deep learning diagnostic engine uses a fused convolutional neural network as its core algorithm architecture, and processes optical image features, spectral fingerprint features, and mechanical impedance features through a multi-branch convolutional structure to achieve intelligent diagnosis of soil layer division, soil color quantification, soil texture discrimination, and soil structure description.

[0010] The cloud-based collaborative decision-making module is used to upload on-site diagnostic results to the central database via mobile communication networks, and to receive remote verification instructions from experts or decision support information such as fertilization suggestions and irrigation strategies automatically generated by the system.

[0011] The positioning accuracy of the autonomous mobile operating chassis is controlled within 10 centimeters. Its power drive module uses a high-torque brushless DC motor with a planetary reducer to provide continuous and stable power output for the tracked walking mechanism. The obstacle avoidance system combines lidar and depth camera to build a local environment map in real time and use path finding algorithms to achieve autonomous planning in complex obstacle environments.

[0012] Preferably, when the vertical profile detection actuator performs the detection task, its drilling depth range covers 0 to 200 cm, and the depth measurement accuracy reaches the 1 mm level; the pressure feedback balance module calculates the drilling resistance by monitoring the current fluctuation of the servo motor in real time. When the resistance exceeds the preset protection threshold, the system will automatically start the vibration assist mode or slow down the feed speed to prevent mechanical damage and protect the original stratification structure of the soil profile.

[0013] The ultra-high-definition optical imaging module in the multi-dimensional in-situ sensing array uses an industrial camera with no less than 20 million pixels and is equipped with a ring-shaped shadowless cold light source system to eliminate the impact of insufficient light in deep profiles on image quality; the hyperspectral scanning sensor has a wavelength coverage range of 400 to 2500 nanometers and is used to detect the organic matter content, mineral composition, and heavy metal pollution status in the soil; the micro-electronic piezoresistive array is arranged vertically at intervals of 5 centimeters to generate continuous soil penetration resistance curves, thereby providing a quantitative basis for soil compaction evaluation.

[0014] Preferably, the fusion convolutional neural network in the deep learning diagnostic engine consists of a multi-layer structure, specifically including an input layer, a preprocessing layer, a feature extraction branch, a fusion perception layer, and an output layer. The preprocessing layer normalizes and enhances the contrast of the optical image, and smooths, denoises, and performs first-order differential transformation on the spectral data. The feature extraction branch includes at least three parallel deep convolutional branches. The first branch adopts a deep residual network architecture to extract the texture features, particle size distribution, and fracture structure of the soil profile through multi-layer convolutional kernels. The second branch uses a one-dimensional convolutional neural network to process hyperspectral data and extract feature vectors related to soil nutrients. The third branch performs temporal feature extraction on the mechanical impedance data.

[0015] The fusion perception layer uses an attention mechanism to dynamically weight and fuse the features extracted from each branch; the system automatically allocates the weight ratio of optical features and mechanical features according to the environmental noise level at the current detection depth; for example, when the optical features are blurred due to high soil moisture, the system will automatically increase the weight of mechanical impedance features in texture discrimination.

[0016] Preferably, the output layer adopts a multi-task learning architecture and outputs multiple diagnostic indicators simultaneously, including the precise depth limits of each soil generation layer, the soil color code converted according to the standard colorimetric card, the percentage ratio of sand, silt and clay, the grade evaluation of soil structure, and the comprehensive fertility score.

[0017] The deep learning diagnostic engine uses massive amounts of standard soil profile sample data during the training phase and utilizes transfer learning technology to transform general visual recognition capabilities into professional soil feature recognition capabilities. The system has a self-evolution function, which can automatically add samples that have been remotely confirmed by experts in each field operation to the training set, and continuously optimize the convolution kernel parameters through incremental learning to improve the accuracy of diagnosis under different regions and climate conditions.

[0018] Preferably, the cloud-based collaborative decision-making module utilizes fifth-generation mobile communication technology to ensure high-bandwidth, low-latency data transmission; its internally established soil profile big data platform supports the visualization of geographic information systems, enabling the expansion of diagnostic results from a single point into a regional soil quality distribution map.

[0019] The robot system is also equipped with an environmental self-sensing unit, including a weather sensor and a ground flatness detector, which is used to assess whether the current environment meets the safety conditions for automatic diagnosis before operation; if the wind speed exceeds level 6 or the ground slope exceeds 25 degrees, the system will automatically issue an alarm and lock the actuator.

[0020] Preferably, the autonomous mobile operating chassis is also equipped with a solar-assisted charging system, which is arranged on the unfoldable folding plate on the top of the chassis, to provide continuous power supply for the on-board sensors and main control computer, thereby extending the robot's single operation time in remote areas.

[0021] When identifying soil stratification, the deep learning diagnostic engine employs a feature jump detection algorithm based on a sliding window. When the cosine similarity between the convolutional feature vector within the window and the feature vector at the previous depth is less than a preset value of 0.85, the system automatically marks that depth as a potential layer boundary and triggers a hyperspectral sensor to perform encrypted scanning to improve the accuracy of stratification.

[0022] Preferably, the ultra-high-definition optical imaging module supports automatic switching between macro mode and wide-angle mode; in wide-angle mode, it captures the overall configuration of the profile, and in macro mode, it captures detailed images of key feature points such as nodules, spots, root distribution and biological caves. These detailed images will be input into a specific fine-grained classification network for specialized diagnosis.

[0023] The deep learning diagnostic engine solves the problem of fusing features at different scales by introducing a spatial pyramid pooling structure, enabling the system to identify both large-scale soil layer orientation and capture the morphology of fine soil aggregates.

[0024] Preferably, the multidimensional in-situ sensing array further includes a temperature and humidity compensation circuit to correct the influence of ambient temperature changes on the readings of the electrochemical sensor, ensuring the stability of physicochemical parameters during operation in different seasons.

[0025] The robot's main control computer adopts an industrial-grade embedded computing platform with powerful graphics processing capabilities. It can support offline inference of deep learning algorithms and can independently complete on-site diagnostic tasks even in blind areas without network signals, and synchronize data after communication is restored.

[0026] Preferably, the end of the vertical profile detection actuator is equipped with an automatic cleaning device, which uses high-pressure airflow or a mechanical brush to remove sticky soil residue from the sensor probe after each detection, avoiding cross-contamination that could interfere with subsequent measurements. After the diagnosis is completed, the system automatically generates an electronic report of the soil profile description that conforms to national standards. The report includes the survey location, coordinates, altitude, parent material, irrigation and drainage conditions, and a summary of the physicochemical properties at each level.

[0027] Preferably, the deep learning diagnostic engine also integrates a Bayesian network-based probabilistic prediction model to assess the uncertainty of the diagnostic results; if the confidence level of a certain level of identification is lower than 70%, the system will prompt the operator to perform manual intervention or instruct the robot to re-perform local detection.

[0028] This invention provides a soil profile intelligent diagnostic robot system that integrates convolutional neural networks, which has the following beneficial effects: (1) When the system is running, by constructing a deep learning diagnostic engine that integrates convolutional neural networks, the fundamental transformation of soil profile from subjective qualitative description to objective quantitative analysis is realized. The system uses a multi-branch convolutional architecture to process optical images, hyperspectral data and mechanical impedance signals at the same time, overcoming the diagnostic differences caused by different experience backgrounds, lighting environments and fatigue levels in traditional manual diagnosis, and greatly improving the accuracy and repeatability of soil stratification, color discrimination and texture assessment.

[0029] (2) This invention integrates an autonomous mobile operation chassis and a vertical profile detection actuator to construct a fully automated in-situ detection operation mode. Compared with traditional pit digging or manual drilling, this system can achieve continuous, non-destructive or low-disturbance detection within a depth range of 200 cm. This not only significantly reduces the labor intensity of field operations, but also allows for real-time acquisition of fine change curves of soil physicochemical properties in the vertical direction, providing more detailed and high-density spatiotemporal data support for the study of soil evolution.

[0030] (3) This invention innovatively introduces an attention mechanism and a multi-task learning framework to realize intelligent fusion perception of complex soil characteristics; the system can dynamically adjust the contribution weight of different sensors according to real-time environmental feedback, effectively solving the problem of insufficient robustness of a single sensor in complex soil environment; at the same time, through transfer learning and incremental learning mechanisms, the system has the ability to continuously evolve and can adapt to the diagnostic needs of different regions and different soil types, providing standardized terminal technology equipment for building a national and even global soil profile big data platform.

[0031] (4) Through deep integration of cloud collaboration and geographic information systems, isolated soil diagnostic points are connected into a dynamic resource management network; the digital diagnostic reports generated by the system can directly serve precision agriculture, land assessment and ecological restoration decisions, realizing closed-loop management from raw data collection to terminal application implementation, and significantly improving the level of intelligent soil resource management. In terms of mechanical design, this invention fully considers the adaptability to complex field environments. Through servo feedback control, automatic obstacle avoidance and solar-assisted power supply, it ensures the long-term stable operation of the system in extreme environments. Its centimeter-level positioning accuracy and millimeter-level depth measurement capability lay a solid physical foundation for the refined modeling of soil profiles. Attached Figure Description

[0032] Figure 1 This is a schematic diagram of the core principle framework of the deep learning diagnostic engine in a soil profile intelligent diagnostic robot system that integrates convolutional neural networks according to the present invention. Figure 2 This is a schematic diagram of the overall technical architecture of a soil profile intelligent diagnostic robot system that integrates convolutional neural networks according to the present invention. Figure 3 This is a logical flowchart of in-situ detection and multi-dimensional feature analysis of soil profiles in a soil profile intelligent diagnostic robot system that integrates convolutional neural networks according to the present invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention. Example 1:

[0034] This invention provides a soil profile intelligent diagnostic robot system that integrates convolutional neural networks. Please refer to [link / reference]. Figure 1 This embodiment discloses a soil profile intelligent diagnostic robot system integrating convolutional neural networks. This system constitutes a comprehensive technology platform integrating autonomous navigation, precise detection, in-situ sensing, deep learning diagnostics, and cloud-based decision-making. Through a highly integrated modular design, the system achieves automated acquisition and intelligent identification of the physicochemical properties and morphological characteristics of soil profiles.

[0035] Combined with appendix Figure 1 The autonomous mobile operating chassis serves as the physical platform for the entire system. Its core structure includes a power drive module, a tracked walking mechanism, and a multi-sensor fusion obstacle avoidance system. The power drive module utilizes two high-torque brushless DC motors, each with a rated power of 1500 watts. Power is transmitted to the tracked walking mechanism via a planetary gearbox with a reduction ratio of 1:25. The tracked walking mechanism is made of high-strength synthetic rubber with an embedded Kevlar fiber reinforcement layer, ensuring sufficient traction for the robot in soft soil, farmland, and sloping terrain. The autonomous mobile operating chassis achieves a positioning accuracy within 10 centimeters, achieved through a tightly coupled algorithm between a high-precision global positioning system receiver and an inertial measurement unit. The obstacle avoidance system integrates a lidar and a depth camera. The lidar, with a scanning frequency of 20 Hz, can model obstacles within a 30-meter radius in real time; the depth camera captures information about subtle obstacles at close range. The system constructs a local environment map in real time and combines it with a dynamic window algorithm for path planning, ensuring that the robot can automatically navigate to the target work area according to the preset coordinate instructions.

[0036] A vertical profile detection actuator is installed at the central axis of the autonomous mobile operating chassis. This actuator is the core component for performing deep soil profiling or in-situ drilling. The vertical profile detection actuator includes a high-precision servo motor, a auger drill bit, and a pressure feedback balancing module. The servo motor has a rated torque of 20 Nm, and its end is connected to a 150 mm diameter auger drill bit. The drill bit surface is hardened with tungsten carbide to handle soils of varying compaction. The vertical profile detection actuator can drill to depths ranging from 0 to 200 cm, and its vertical displacement is monitored by a high-precision photoelectric encoder, achieving a depth measurement accuracy of 1 mm. During drilling, the pressure feedback balancing module plays a crucial role. It collects real-time current fluctuation data from the servo drive and calculates the real-time drilling resistance by combining the current-torque ratio. When the system detects that the drilling resistance exceeds the preset protection threshold of 1500 Newtons, the control core automatically activates the high-frequency vibration auxiliary mode. This mode generates a 50 Hz micro-vibration via a vibrator to reduce the sidewall friction between the drill bit and the soil, and simultaneously reduces the feed rate by 50%. This prevents overload damage to the mechanical structure and maximizes the preservation of the original bedding structure of the soil profile. After each exploration mission, an automatic cleaning device at the end of the vertical profile exploration actuator blows away the soil adhering to the spiral blades using high-pressure air nozzles, ensuring the purity of subsequent explorations.

[0037] A multidimensional in-situ sensing array is mounted on the follower slider of the vertical profile detection actuator. This array continuously acquires data within the profile as the drill bit moves vertically up and down. The multidimensional in-situ sensing array integrates an ultra-high-definition optical imaging module, a hyperspectral scanning sensor, a multi-band soil moisture analyzer, a miniature electronic piezoresistive array, and an electrochemical impedance spectroscopy analyzer. The ultra-high-definition optical imaging module uses a 20-megapixel industrial-grade complementary metal-oxide-semiconductor camera, equipped with a ring-shaped shadowless cold light source system. This light source system consists of 32 high color rendering index light-emitting diodes, achieving an illuminance of 5000 lux, effectively eliminating shadows and noise caused by insufficient light in deep profiles. The hyperspectral scanning sensor has a wavelength coverage range of 400 to 2500 nanometers and a spectral resolution of 5 nanometers, primarily used to detect the organic matter content, mineral composition, and heavy metal pollution status in the soil. The multi-band soil moisture analyzer utilizes the principle of frequency domain reflection to measure the dielectric constant of the soil by sending alternating electromagnetic waves from 100 MHz to 500 MHz, thereby obtaining accurate volumetric water content. A miniature electronic piezoresistive array is vertically arranged at 5 cm intervals on the sensing support to synchronously record continuous soil penetration resistance curves during drilling. Furthermore, the multi-dimensional in-situ sensing array includes a temperature and humidity compensation circuit. This circuit corrects the potential values ​​acquired by the electrochemical sensor by real-time monitoring of the probe's ambient temperature and applying preset calibration coefficients, ensuring the stability of data readings in operating environments ranging from -10°C to 50°C.

[0038] Please refer to the attached document. Figure 2 The deep learning diagnostic engine, integrated into the robot's main control computer, is the intelligent core of the entire system. Based on a high-performance graphics processor, this engine supports end-to-end feature extraction from massive amounts of raw signals. The deep learning diagnostic engine uses a fused convolutional neural network as its core algorithm architecture, which comprises multiple layers: an input layer, a preprocessing layer, a feature extraction branch, a fusion perception layer, and an output layer. The preprocessing layer performs bicubic interpolation scaling and histogram equalization on ultra-high-definition optical images to enhance texture details; it also performs Savitzky-Grey smoothing and first-order differential transformation on hyperspectral data to eliminate baseline drift. The feature extraction branch contains three parallel deep convolutional branches. The first branch uses a deep residual network architecture, extracting texture features, grain size distribution, and subtle crack structures from the soil profile through 50 layers of convolutional operations. The second branch uses a one-dimensional convolutional neural network to process hyperspectral data, performing feature mapping in the wavelength dimension using 1x3 convolutional kernels to extract feature vectors closely related to soil nutrients. The third branch extracts time-domain features from the mechanical impedance data and captures the trend of resistance variation with depth through a recurrent neural network unit.

[0039] The deep learning diagnostic engine employs an attention mechanism for dynamic weighted fusion when processing features from each branch. The algorithm works as follows: First, the system maps the feature vectors extracted from each branch to a unified feature space and calculates the importance score of each feature vector in the current diagnostic task. The system automatically allocates the weight ratio between optical and mechanical features based on the environmental noise level at the current detection depth. When soil moisture exceeds 35%, causing glare or blurring in the optical image, the fusion perception layer automatically reduces the weight of the first branch and increases the weight of mechanical impedance features in texture discrimination from 0.3 to 0.6. When discriminating soil stratification, the deep learning diagnostic engine uses a sliding window-based feature jump detection algorithm. The algorithm works by setting a sliding window with a vertical step size of 1 cm and calculating the cosine similarity of convolutional feature vectors between adjacent windows in real time. The cosine similarity is calculated by performing a dot product between the feature vector at the current depth position and the feature vector at the previous depth position, and then dividing by the product of their magnitudes. When the cosine similarity is lower than the preset threshold of 0.85, the system determines that a significant change in soil properties has occurred at that location, automatically marks it as a potential layer boundary, and triggers the hyperspectral sensor to perform a encrypted scan with a sampling rate increased by 4 times.

[0040] The deep learning diagnostic engine's output layer employs a multi-task learning architecture, enabling simultaneous output of diagnostic indicators across multiple dimensions. These indicators include, but are not limited to, precise depth boundaries of each soil layer, soil color coding converted according to international standard color charts, percentage ratios of sand, silt, and clay, soil structure grade evaluation, and comprehensive fertility score. During the training phase, the deep learning diagnostic engine utilized 50,000 sets of standard soil profile samples from different climate zones across China. Through transfer learning techniques, it transferred the feature extraction capabilities pre-trained on large-scale general visual datasets to the specialized field of soil science. The system possesses self-evolution capabilities, automatically storing samples remotely verified by experts during each field operation into local storage. Upon connecting to the network, these samples are synchronized to the server, and the incremental learning mechanism continuously optimizes the weight parameters of the convolutional kernels. Example 2:

[0041] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figures 1 to 3 Specifically: in conjunction with the appendix Figure 3The logical flow of in-situ soil profile detection and multi-dimensional feature analysis in this embodiment is as follows. First, the robot reaches the preset location via its autonomous mobile chassis, and the environmental self-sensing unit begins operation. This unit includes a meteorological sensor and a ground flatness detector. If the wind speed measurement exceeds 12 meters per second (level 6 wind speed), or the tilt angle displayed by the ground slope sensor exceeds 25 degrees, the main control computer will immediately issue an audible and visual alarm and lock the downward command of the vertical profile detection actuator to ensure equipment safety. After the environmental parameters meet the standards, the vertical profile detection actuator begins to drill at a constant speed, and the multi-dimensional in-situ sensing array is activated synchronously. The optical imaging module automatically switches between wide-angle mode and macro mode based on the current lighting feedback. The wide-angle mode is used to capture the overall profile configuration within a 100-centimeter range, while the macro mode magnifies the details of nodules, blemishes, root distribution, or biological cavities by 10 times. These detailed images are input into a specific fine-grained classification network to accurately identify the material composition in the soil.

[0042] The cloud-based collaborative decision-making module utilizes 5G mobile communication technology to establish a high-speed link with a remote server. Raw data and preliminary diagnostic results generated on-site are packaged into standard data packets and uploaded to the central database. The central database contains a geographic information system-based visualization platform that can expand point-source diagnostic information from individual points into regional soil quality distribution maps using spatial interpolation algorithms. Experts can verify the uploaded images and data via mobile terminals, and verification commands are transmitted back to the robot in real time via a low-latency network. The system then automatically generates precise fertilization recommendations, irrigation quota strategies, and tillage improvement plans based on the comprehensive diagnostic results and the built-in agricultural expert system model. After the diagnostic process is fully completed, the system automatically generates an electronic soil profile description report that conforms to national standards. The report comprehensively covers the latitude and longitude, altitude, parent material type, surrounding irrigation and drainage conditions, and a summary of the physicochemical properties at each level of the survey site.

[0043] Furthermore, to meet the demands of extended field operations, the autonomous mobile chassis is equipped with a solar-assisted charging system on its top. This system consists of four deployable monocrystalline silicon folding panels, with a total area of ​​1.2 square meters and a maximum output power of 240 watts. Under sufficient sunlight, this system can directly power the onboard sensors and main control computer, storing excess energy in a 40 ampere-hour lithium battery pack, thus extending the robot's single-charge endurance by more than three times. The main control computer itself utilizes an industrial-grade embedded computing platform with a floating-point operation capability of 40 trillion operations per second, ensuring that deep learning algorithms can perform offline real-time inference even in remote areas without network coverage. If the Bayesian network probabilistic prediction model in the deep learning diagnostic engine assesses the confidence level of the current identification result as below 70%, the system will automatically perform two retests at the current depth, eliminating random errors by averaging or resampling to ensure the scientific validity and authority of the diagnostic results.

[0044] This embodiment achieves non-destructive, high-resolution, multi-dimensional in-situ monitoring of soil profile characteristics through the coordinated operation of the aforementioned components. Compared to the traditional manual excavation method for profiling, this system reduces the investigation time for a single profile from 4 hours to less than 30 minutes, and avoids subjective biases caused by human factors in describing soil color, texture, and structure. Example 3:

[0045] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figures 1 to 3 Specifically, this embodiment discloses an enhanced diagnostic configuration scheme for environments with extremely high soil compaction and complex root distribution. This scheme features targeted enhancements in both hardware structure and algorithm logic.

[0046] Please refer to the attached document again. Figure 1 In this embodiment, the vertical profile detection actuator incorporates an ultrasonic fracturing subunit. This subunit, by integrating an ultrasonic transducer at the tip of the auger drill bit, generates 28 kHz mechanical vibrations to break up small gravel or large roots in the soil, ensuring drilling continuity. The servo motor's power has been increased to 2200 watts, paired with a reinforced drill bit made of high-strength alloy steel. The spacing of the miniature electronic piezoresistive array in the multidimensional in-situ sensing array has been reduced from 5 cm to 2.5 cm to obtain denser penetration resistance data, which is crucial for identifying the location of soil stagnant layers in arid regions.

[0047] Combined with appendix Figure 2The deep learning diagnostic engine introduces a spatial pyramid pooling structure in the feature extraction layer. Located between the convolutional and fully connected layers, this structure's core function is to address the issue of varying input image scales. By setting multiple pooling window levels such as 1x1, 2x2, and 4x4, the deep learning diagnostic engine can simultaneously extract features at different spatial scales. For example, when identifying soil aggregates, the system can capture both the orientation of large-scale soil clumps and extract micron-level particle arrangement morphology through the bottom layer of the pyramid structure. Furthermore, the attention mechanism in this embodiment is upgraded to a cross-modal interactive attention model. This model not only assigns weights to each branch but also calculates the cross-correlation coefficient between optical image features and electrochemical data features.

[0048] In this enhanced embodiment, the fused convolutional neural network employs a sequence comparison logic based on a dynamic time warping algorithm when determining layer boundaries. The system treats the real-time sensor data stream as a time-series signal and determines the soil type switching point by calculating the minimum bending path distance between the current depth sequence and the standard soil layer template sequence. This method exhibits higher robustness than simple cosine similarity when handling profiles with obvious transition layers.

[0049] Please refer to the attached document. Figure 3 In this embodiment, an adaptive calibration step is added to the workflow. Before the detection begins, the multidimensional in-situ sensing array undergoes zero-point drift testing in air and on a known standard block. For the ultra-high-definition optical imaging module, the system automatically adjusts white balance and exposure compensation by photographing a built-in 18% neutral grayscale reference plate, ensuring high comparability of soil colors collected under different seasons and color temperatures. The electrochemical impedance spectroscopy analyzer verifies the circuit integrity by measuring the built-in standard resistor-capacitor network.

[0050] In this embodiment, the cloud-based collaborative decision-making module adds 3D modeling capabilities. Using data acquired through multiple round trips by the robot, the cloud server can construct a 3D soil property model of the target area. This model not only displays the surface fertility distribution but also shows the spatial variation trends of organic matter, moisture, and compaction at different underground depths. This function provides direct scientific basis for selecting seedling planting depth in precision forestry.

[0051] To ensure reliability in extreme environments, the obstacle avoidance system of the autonomous mobile chassis has been enhanced with an infrared thermal imaging sensor. This sensor can identify animal burrows hidden by weeds or concealed puddles, guiding the robot safely even in the dim light of early morning or late afternoon when visible light cameras are unavailable. The battery management system has also been upgraded with the addition of an active thermal management unit. When the temperature drops below 0 degrees Celsius, it consumes a small amount of electrical energy to heat the battery, ensuring discharge efficiency and motor output power in low-temperature environments.

[0052] The deep learning diagnostic engine employs a compensation algorithm based on a stress wave attenuation model when processing mechanical impedance characteristics. Since soil moisture significantly affects the measured penetration resistance, the algorithm uses a multivariate regression equation to incorporate real-time moisture sensor readings into the impedance calculation formula. This formula, by introducing a moisture correction factor, transforms the original measured resistance into a baseline resistance at standard moisture content. In this way, even in moist soil after rainfall, the system can accurately identify subsurface compaction barriers caused by excessive mechanical compaction.

[0053] The electronic report generated by the output layer further expands the ecosystem service function evaluation module in this embodiment. Based on the diagnosed soil texture and carbon storage information, the report can automatically calculate the carbon sequestration potential and water conservation level of the profile point. All diagnostic process data is encrypted and stored using distributed ledger technology to ensure the authenticity, immutability, and traceability of soil resource data, providing legally recognized technical proof for quality assessment during land transfer.

[0054] The advantage of this embodiment lies in its strong adaptability to complex and extreme habitats. Through simultaneous upgrades to both hardware and software, the robot system can perform highly challenging profile diagnosis tasks in desertified land, saline-alkali wasteland, and degraded forest land, achieving industry-leading levels in both data accuracy and system stability. Example 4:

[0055] This embodiment is an explanation based on Embodiment 3. Please refer to it. Figure 1 For details, please refer to the appendix. Figure 1 In this embodiment, the autonomous mobile operating chassis is equipped with a wireless charging sensing area and an automatic battery replacement interface. When the system detects that the remaining battery power is below 15%, the robot will automatically search for a mobile charging mother station in the operating area and wirelessly replenish power through magnetic induction technology, or the robotic arm of the mother station will automatically replace the standard power battery pack within 3 minutes. This enables the robot system to operate 24 hours a day without interruption. A micro laser-induced breakdown spectroscopy module has been added to the multidimensional in-situ sensing array. This module emits high-energy nanosecond pulse lasers focused on the soil profile surface to generate micro-area plasma. By analyzing the plasma emission spectrum, minute-level in-situ quantitative analysis of the total nitrogen, phosphorus, potassium, and trace elements in the soil can be achieved, greatly enriching the input dimensions of the deep learning diagnostic engine.

[0056] Combined with appendix Figure 2The deep learning diagnostic engine incorporates a generative adversarial network (GAN) architecture for sample augmentation. When dealing with extremely rare soil types or special soil formation layers, the system uses a generator to simulate physically accurate virtual soil images and spectral features to assist the discriminator in training, thus addressing the overfitting problem caused by the scarcity of samples for specific soil types. The fusion perception layer employs a variant of the self-attention mechanism, namely a multi-head attention network. This network can simultaneously observe the correlations between features from eight different subspaces, such as simultaneously focusing on color abrupt changes and fine-tuning of compactness, thereby making more accurate decisions at complex composite layer boundaries. Example 5:

[0057] This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1 For details, please refer to the appendix. Figure 3 This embodiment incorporates a strategy optimization step based on deep reinforcement learning into the diagnostic process. The robot can automatically adjust the sampling density of subsequent points based on the diagnostic results of the first few detection points. If a clear linear decreasing trend in soil organic matter content is detected in three consecutive points, the system will automatically add a denser detection point between two preset points to more accurately delineate the boundary of nutrient depletion. This adaptive sampling strategy effectively reduces unnecessary mechanical operations while ensuring mapping accuracy.

[0058] In this embodiment, the cloud-based collaborative decision-making module is deeply integrated with the weather forecasting system. The system can predict the probability of rainfall within the next 48 hours. If heavy rain is predicted, cloud commands will instruct the robot to prioritize the detection of low-lying areas and promptly and automatically backfill and compact the vertical cross-section holes after the work is completed to prevent localized erosion caused by rainwater seepage along the boreholes. The generation of electronic reports has also become more intelligent; the system can automatically switch the focus of the report based on the user's industry background. For agricultural users, the report focuses on fertility distribution and fertilization plans; for environmental monitoring users, the report prioritizes displaying heavy metal migration paths and pollution risk levels.

[0059] Furthermore, the surface of the spiral drill bit of the vertical profile detection actuator is coated with a layer of nano-hydrophobic and oleophobic material, which significantly reduces the adhesion of highly cohesive soil to the drill bit. Combined with improved ultrasonic vibration technology, the system's operating speed in high-moisture clay soils in southern regions has increased by 40%. The temperature and humidity compensation circuit of the multi-dimensional in-situ sensor array incorporates a Kalman filter algorithm to smooth out minute fluctuations in the sensor output, resulting in a 12 dB improvement in the system's signal-to-noise ratio even in complex electromagnetic interference environments.

[0060] The deep learning diagnostic engine also integrates a Bayesian network-based probabilistic prediction model, which is used to assess the integrity of the entire system's diagnostic chain. It not only evaluates the confidence level of hierarchical results but also monitors the health status of each sensor in real time. If laser power attenuation is detected in the hyperspectral sensor, the system automatically activates a software compensation mechanism and assigns a lower weight to that data in the report, ensuring the robustness of the final decision.

[0061] This embodiment constructs a highly autonomous intelligent soil survey ecosystem by introducing more advanced sensing technologies, power replenishment methods, and collaborative algorithms. This system not only improves the operational efficiency of individual machines but also provides a complete intelligent solution for large-scale national soil census tasks through multi-machine collaboration and cloud-based intelligent decision-making.

[0062] In summary, the embodiments of this invention, by integrating convolutional neural networks and precision electromechanical control technology, have fundamentally changed the traditional situation of soil profile surveys, which rely on manual labor, are highly subjective, and inefficient. It utilizes a multi-dimensional in-situ sensor array to acquire the full spectrum information of the profile in real time, and performs multi-task parallel analysis through a deep learning diagnostic engine, ultimately generating standardized digital reports and decision recommendations. This system has achieved significant breakthroughs in key indicators such as centimeter-level navigation and positioning, millimeter-level depth control, and microscopic feature recognition, providing strong technical support for the precision management of modern agriculture and the refined protection of the ecological environment.

[0063] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A soil profile intelligent diagnostic robot system integrating convolutional neural networks, characterized in that: It includes an autonomous mobile operation module, a vertical profile detection module, a multi-dimensional in-situ sensing module, an intelligent diagnostic analysis module, and a cloud-based collaborative decision-making module; The autonomous mobile operation module and the autonomous mobile operation chassis are used to support the entire robot system and perform fully automatic navigation and precise positioning in the target operation area according to the preset path planning instructions. The vertical profile detection module is installed at the central axis of the autonomous mobile operating chassis and is used to perform vertical deep excavation or in-situ drilling of the soil profile after reaching the designated coordinate point. A multidimensional in-situ sensing module, with a multidimensional in-situ sensing array arranged on the side wings or follower slider of the vertical profile detection actuator, is used to collect real-time data on physical, chemical and morphological features within the detection depth range. The intelligent diagnostic analysis module, with a deep learning diagnostic engine built into the main control computer, is used for end-to-end feature extraction and logical analysis of the raw signals acquired by the multi-dimensional in-situ sensing array. The cloud-based collaborative decision-making module is used to upload on-site diagnostic results to the central database via mobile communication networks, and to receive remote verification instructions from experts or fertilization suggestions and irrigation strategy decision support information automatically generated by the system.

2. The intelligent soil profile diagnostic robot system integrating convolutional neural networks according to claim 1, characterized in that: The positioning accuracy of the autonomous mobile operating chassis is controlled within 10 centimeters; The power drive module uses a brushless DC motor in conjunction with a planetary reducer to provide power output for the tracked walking mechanism. The obstacle avoidance system combines lidar and depth camera to achieve autonomous planning in complex obstacle environments by constructing local environment maps in real time and using path-finding algorithms.

3. The intelligent soil profile diagnostic robot system based on convolutional neural networks according to claim 1, characterized in that: The vertical profile detection actuator includes a servo motor, a spiral drill bit, and a pressure feedback balance module, which is used to sense the resistance changes during the drilling process in real time and dynamically adjust the operating power to ensure the flatness and verticality of the sampling surface. The drilling depth range of the vertical profile detection actuator is 0 to 200 cm, and the depth measurement accuracy reaches the 1 mm level. The pressure feedback balance module is used to calculate drilling resistance by monitoring the current fluctuation of the servo motor in real time. When the resistance exceeds the preset protection threshold, the system automatically starts the vibration assist mode or slows down the feed speed to protect the original layering structure of the soil profile. The end of the vertical profile detection actuator is also equipped with an automatic cleaning device, which is used to remove sticky soil residue on the sensor probe after each detection by means of high-pressure airflow or mechanical brush.

4. The intelligent soil profile diagnostic robot system integrating convolutional neural networks according to claim 1, characterized in that: The multi-dimensional in-situ sensing array integrates an ultra-high-definition optical imaging module, a hyperspectral scanning sensor, a multi-band soil moisture detector, a miniature electronic varistor array, and an electrochemical impedance spectroscopy analyzer. The ultra-high-definition optical imaging module uses an industrial camera with over 20 million pixels and is equipped with a ring-shaped shadowless cold light source system. The hyperspectral scanning sensor has a wavelength range of 400 to 2500 nanometers and is used to detect the organic matter content, mineral composition, and heavy metal pollution status in the soil. The microelectronic varistor array is arranged vertically at 5-centimeter intervals to generate continuous soil penetration resistance curves. The multidimensional in-situ sensing array also includes a temperature and humidity compensation circuit to correct the effect of ambient temperature changes on the readings of the electrochemical sensor.

5. The intelligent soil profile diagnostic robot system integrating convolutional neural networks according to claim 1, characterized in that: The deep learning diagnostic engine uses a fusion convolutional neural network as its core algorithm architecture. Through a multi-layer structure including an input layer, a preprocessing layer, a feature extraction branch, a fusion perception layer, and an output layer, it processes optical image features, spectral fingerprint features, and mechanical impedance features respectively, thereby achieving intelligent diagnosis of soil layer division, soil color quantification, soil texture discrimination, and soil structure description. The preprocessing layer is used to normalize and enhance the contrast of the optical image, and to smooth, denoise, and perform first-order differential transformation on the spectral data; the feature extraction branch includes at least three parallel deep convolution branches. The first branch uses a deep residual network architecture to extract the texture features, particle size distribution and fracture structure of the soil profile; The second branch uses a one-dimensional convolutional neural network to process hyperspectral data and extract feature vectors related to soil nutrients. The third branch is used to extract time-domain features from the mechanical impedance data.

6. The intelligent soil profile diagnostic robot system based on convolutional neural networks according to claim 5, characterized in that: The fusion perception layer uses an attention mechanism to dynamically weight and fuse the features extracted from each branch; the system automatically allocates the weight ratio of optical features and mechanical features according to the environmental noise level at the current detection depth. When the system determines that the soil moisture exceeds the preset moisture threshold and the optical features are blurred, the system automatically increases the weight of mechanical impedance features in texture discrimination.

7. The intelligent soil profile diagnostic robot system integrating convolutional neural networks according to claim 1, characterized in that: The output layer adopts a multi-task learning architecture and outputs multiple diagnostic indicators simultaneously. The diagnostic indicators include the precise depth limits of each soil layer, the soil color code converted according to the standard colorimetric card, the percentage ratio of sand, silt and clay, the grade evaluation of soil structure, and the comprehensive fertility score.

8. The intelligent soil profile diagnostic robot system based on convolutional neural networks according to claim 1, characterized in that: The deep learning diagnostic engine employs a feature jump detection algorithm based on a sliding window when determining soil stratification. When the cosine similarity between the convolutional feature vector within the window and the feature vector at the previous depth is less than a preset value of 0.85, the system automatically marks that depth as a potential layer boundary and triggers the hyperspectral sensor to perform encrypted scanning.

9. The intelligent soil profile diagnostic robot system integrating convolutional neural networks according to claim 4, characterized in that: The ultra-high-definition optical imaging module supports automatic switching between macro mode and wide-angle mode; The overall profile was captured in wide-angle mode, and detailed images of nodules, markings, root distribution, and biological burrows were taken in macro mode. The detailed images are input into a specific fine-grained classification network for specialized diagnosis; The deep learning diagnostic engine also identifies soil layer orientation and soil aggregate morphology at different scales by introducing a spatial pyramid pooling structure.

10. The intelligent soil profile diagnostic robot system based on convolutional neural networks according to claim 1, characterized in that: It also includes an environmental self-sensing unit, which includes a meteorological sensor and a ground flatness detector, used to assess environmental safety conditions before operation; The autonomous mobile operating chassis is also equipped with a solar-assisted charging system to provide power to the on-board sensors and main control computer; The deep learning diagnostic engine also integrates a Bayesian network-based probabilistic prediction model to assess the uncertainty of diagnostic results. When the identification confidence level is below 70%, the system prompts for manual intervention or a re-probing of the local area.

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