Soil deep humidity prediction system and method based on near infrared spectrum and federated learning

Abstract

The invention belongs to the technical field of soil humidity prediction methods, and particularly relates to a soil deep humidity prediction system and method based on near infrared spectrum and federated learning. The system comprises a near infrared spectrum acquisition device, an auxiliary information acquisition module, an edge end data preprocessing module, a federated learning client module, a cloud federated aggregation server and a man-machine interaction and equipment control module. The method comprises the following steps: establishing a surface humidity estimation model by collecting soil surface near-infrared reflectivity and environment auxiliary information; a deep humidity prediction model is constructed in combination with a heat conduction equation and machine learning, and the accuracy is improved by adopting weighted fusion. A federal learning framework is introduced, local training and cloud aggregation of each agricultural machine are realized, data privacy is protected, and the model generalization ability is improved. The system does not need to destroy the soil structure, has the advantages of non-contact, high efficiency, safety and expandability, and is suitable for intelligent prediction of large-range deep humidity in farmland and linkage control of agricultural operation.

Description

Technical Field

[0001] The present invention relates to the technical field of soil moisture prediction methods, and specifically to a deep soil moisture prediction system and method based on near-infrared spectroscopy and federated learning. Background Art

[0002] Soil moisture is a critical environmental parameter in agricultural production, directly influencing crop emergence rates, growth cycles, and agronomic management decisions such as fertilization and irrigation. Mainstream soil moisture detection methods include manual sampling and embedded electrode sensors. While these methods offer some accuracy, they suffer from cumbersome deployment, high costs, difficult maintenance, limited spatial coverage, and poor real-time performance. They are particularly unsuitable for scenarios requiring large-scale dynamic monitoring or deployment on mobile agricultural machinery for real-time operational monitoring.

[0003] With the development of near-infrared spectroscopy, its application in non-contact soil moisture detection has gradually attracted attention. Near-infrared spectroscopy has a strong absorption capacity for water in specific wavelengths. Research has shown that by analyzing soil reflectance spectral data, it is possible to quickly estimate the moisture content of the soil surface.

[0004] However, existing technologies mostly focus on estimating surface soil moisture, while effective methods for real-time measurement of moisture in deeper layers (e.g., 15 cm) remain lacking. In actual agricultural operations, crop seeds are often buried between 5 and 15 cm below the surface. Moisture levels at this depth directly impact sowing success and crop rooting. Surface moisture alone cannot fully reflect moisture conditions at depth.

[0005] A search revealed Chinese patent application number 202411536560.9, titled "A Soil Moisture Prediction System and Method Based on Multispectral Imaging," which proposes using multispectral imagery to analyze soil surface moisture and estimate moisture distribution through a model. However, this method focuses on extracting visual texture features and is primarily applied to surface moisture estimation. Furthermore, the model training process relies on centralized data collection, making it difficult to meet the requirements of privacy protection and multi-region generalization modeling.

[0006] With the rise of intelligent agricultural machinery and edge computing, data distribution in agricultural scenarios exhibits both regional heterogeneity and privacy sensitivity. Traditional centralized training models face the risks of data silos and privacy leaks, making it difficult to support multi-region, multi-device collaborative modeling. In this context, federated learning, as a distributed collaborative training framework, provides a new solution for data privacy protection and cross-device modeling. It allows agricultural machinery terminals to complete model training locally and only upload model parameters for cloud aggregation, effectively avoiding the risks and costs associated with uploading raw data.

[0007] In summary, there is an urgent need for an innovative system that combines near-infrared spectroscopy non-contact detection, deep moisture prediction modeling and federated learning collaborative mechanism, which can not only protect soil data privacy, but also realize dynamic perception and intelligent prediction of deep moisture in a large range and multiple scenarios.

[0008] Therefore, we propose a deep soil moisture prediction system and method based on near-infrared spectroscopy and federated learning to solve the above problems. Summary of the Invention

[0009] The purpose of this section is to summarize some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the abstract and title of this application to avoid blurring the purpose of this section, the abstract and the title of the invention, and such simplifications or omissions should not be used to limit the scope of the present invention.

[0010] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions: A deep soil moisture prediction system based on near-infrared spectroscopy and federated learning, including: A near-infrared spectrum acquisition device, integrated into a mobile agricultural machinery platform, is used to dynamically collect reflectance spectrum data of the soil surface in the near-infrared band during operation; An auxiliary information collection module, integrated into the mobile agricultural machinery platform, is used to synchronously record soil sampling location, time, soil type, and surface temperature data, providing contextual support for subsequent modeling; An edge data preprocessing module, deployed on a mobile agricultural machinery platform and connected to a signal output terminal of the near-infrared spectrum acquisition device, for receiving and processing the reflectance spectrum data collected by the near-infrared spectrum acquisition device; The federated learning client module is embedded in each agricultural machinery terminal. Based on locally collected and processed spectral data and auxiliary information, it independently trains the surface soil moisture estimation model and the deep soil moisture prediction sub-model. It coordinates and aggregates with the cloud through the federated learning protocol, enabling distributed modeling of multiple agricultural machinery without uploading raw data. The cloud-based federated aggregation server receives local model parameters uploaded by each federated learning client module, performs FedAvg model aggregation, outputs a unified prediction model, and periodically transmits it back to the federated learning client module; The human-computer interaction and equipment control module is used to display the deep soil moisture value predicted by the model, and automatically link the irrigation system and sowing control block to achieve closed-loop control of intelligent agricultural machinery operations.

[0011] As a preferred solution of the deep soil moisture prediction system based on near infrared spectroscopy and federated learning described in the present invention, the data preprocessing method of the edge data preprocessing module is: Smoothing and filtering the collected raw near-infrared reflectance data using Savitzky–Golay smoothing, wavelet transform, or moving average algorithm; In order to eliminate systematic errors such as illumination conditions, standard normal variable transformation and multivariate scatter correction method were used to perform spectral standardization and enhance the comparability between different samples; Principal component analysis and variable selection algorithm are used to extract key band features related to moisture and construct feature vectors.

[0012] As a preferred embodiment of the deep soil moisture prediction system based on near-infrared spectroscopy and federated learning described in the present invention, the surface soil moisture estimation method in the federated learning client module is as follows: different types of soil samples are collected, and while obtaining near-infrared spectral data, the actual moisture value of the soil at the corresponding depth is measured using a standard method to construct paired feature-label samples; The partial least squares regression (PLSR) algorithm was used to build the model, and the training sample set was input into the partial least squares regression (PLSR) model. K-fold cross validation was used for training evaluation, and the model parameters were adjusted to obtain the optimal performance. The newly collected near-infrared spectral data were preprocessed in the same way and then input into the trained partial least squares regression (PLSR) model to output the corresponding surface soil moisture estimation.

[0013] As a preferred solution of the deep soil moisture prediction system based on near infrared spectroscopy and federated learning described in the present invention, the specific method of training the deep prediction sub-model is: Based on the one-dimensional unsteady-state heat conduction equation, the initial shallow humidity is introduced as a boundary condition. Combined with input parameters such as soil thermal diffusivity, water-heat coupling parameters, and surface temperature changes, a spatiotemporal evolution model of deep humidity is derived. The model is described as follows:

[0014] in, Indicates time Moment Depth The moisture content at is the soil moisture diffusion coefficient, Represents the disturbance term, and the preliminary prediction value is obtained by numerically solving the equation .

[0015] As a preferred solution of the deep soil moisture prediction system based on near-infrared spectroscopy and federated learning described in the present invention, multiple soil samples are collected, and while simultaneously collecting the surface near-infrared spectrum, the true moisture value of the corresponding 5-15 cm deep soil is obtained through profile sampling to construct a surface-deep moisture paired sample set; The surface moisture value estimated by near-infrared spectroscopy is combined with auxiliary information such as location, time, soil type, and surface temperature as model input features to enhance the model's adaptability and prediction accuracy under various environmental conditions. The gradient boosting tree (GBRT) algorithm was used to establish a regression model of surface-deep humidity. The constructed sample set is used to train the model, and the cross-validation method is used to evaluate the model's prediction performance and optimize the parameter configuration; The newly observed surface humidity and its related environmental parameters are input into the trained model to obtain the machine learning prediction value .

[0016] As a preferred solution of the deep soil moisture prediction system based on near infrared spectroscopy and federated learning described in the present invention, the prediction results of the physical model are Predicting results with machine learning Perform weighted fusion, the specific formula is as follows:

[0017] in, is the weight coefficient, which is dynamically adjusted based on the average error of the model in historical samples, the uncertainty of the two models under current observation conditions, and the local model performance evaluation results fed back during the federated learning process to improve the overall prediction performance; Each agricultural machine completes the model deployment after training locally, and regularly receives the model parameters after global optimization from federated learning to continuously optimize the prediction effect. After the new round of observed surface moisture and environmental parameters are input into the fusion model, the corresponding deep soil moisture estimation is output in real time, realizing the intelligent function of prediction while working.

[0018] As a preferred solution of the deep soil moisture prediction system based on near-infrared spectroscopy and federated learning described in the present invention, the specific manner in which the cloud-based federated aggregation server issues a unified prediction model after executing the aggregation strategy is as follows: After each agricultural machine completes a certain round of local training or accumulates enough samples, it triggers a federated communication round and uploads its current local model parameters, namely the PLSR regression coefficients, without uploading any original spectral data or label information. The uploaded model parameters are perturbed by differential privacy and encrypted communication is carried out through the TLS protocol; The cloud-based federated aggregation server uses the standard FedAvg strategy to average the model weights uploaded by all federated learning client modules according to the number of samples and updates them as global model parameters. The aggregated model is distributed to each federated learning client module using a unified global model strategy; The system supports asynchronous federated training, allowing some agricultural machinery to skip a round of aggregation due to network disconnection or operation interruption, and the aggregation server automatically adjusts the weight distribution.

[0019] A method for predicting deep soil moisture based on near-infrared spectroscopy and federated learning, comprising the following steps: Step 1: Scan the target area using a near-infrared spectrum acquisition device to obtain the soil surface reflectance spectrum; Step 2: Use sensors deployed on the mobile agricultural machinery platform to synchronously record soil sampling location, time, soil type, and surface temperature data; Step 3: Receive and process the near-infrared spectral image data output by the near-infrared spectral imaging device through the data preprocessing module on each agricultural machinery terminal; Step 4: Each client builds a regression model based on its own sample data to estimate surface soil moisture; Step 5: While keeping the data locally stored, each agricultural machine periodically uploads local model parameters to the cloud federation aggregation server. The server then executes the aggregation strategy and issues the global model. Step 6: Each agricultural machine trains a deep moisture prediction model based on the surface moisture estimation obtained through federated training, auxiliary information, and historical deep moisture data; Step 7: The human-computer interaction device outputs the prediction results to the display terminal or uploads them to the smart agriculture platform, and interacts with external equipment such as planters and irrigation systems.

[0020] Compared with the existing technology, the present invention has the following beneficial effects: the present invention obtains soil surface reflectance information through a near-infrared spectral camera, realizes non-contact rapid detection of soil moisture, avoids the tedious installation and maintenance operations of traditional buried sensors, has the advantages of convenient deployment, fast response speed and non-destructiveness, and is particularly suitable for dynamic monitoring of large-scale fields.

[0021] This invention innovatively introduces a federated learning framework, enabling each agricultural machine terminal to independently complete model training locally, uploading only the model parameters to the cloud for aggregation. This effectively avoids the centralized transmission and storage of raw near-infrared spectral data, significantly enhancing the system's ability to protect farm privacy data. Furthermore, the federated aggregation mechanism enables cross-regional and cross-soil type modeling collaboration, improving the model's generalization and environmental adaptability.

[0022] Leveraging spectral modeling and auxiliary features (such as surface moisture estimates, geographic location, time of year, soil type, and surface temperature), a multi-source fusion deep moisture prediction model was constructed. This model enables highly accurate indirect inference of soil moisture at specific depths (e.g., 15 cm) without deep sampling, expanding the application depth of near-infrared technology and improving the efficiency and accuracy of acquiring deep moisture information. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be derived from these drawings without inventive effort. Among them: Figure 1 This is a schematic diagram of the structure of a deep soil moisture prediction system based on near-infrared spectroscopy and federated learning in the present invention; Figure 2 This is a flow chart of a method for predicting deep soil moisture based on near-infrared spectroscopy and federated learning according to the present invention. DETAILED DESCRIPTION

[0024] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0025] Next, the present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing the embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of protection of the present invention. Furthermore, in actual production, three-dimensional dimensions, including length, width, and depth, should be included.

[0026] To make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0027] Example 1 like Figure 1 As shown, an embodiment of the present invention proposes a deep soil moisture prediction system based on near-infrared spectroscopy and federated learning, including: A near-infrared spectral acquisition device, mounted on the front of a mobile agricultural machine, faces the ground and continuously acquires near-infrared reflectance spectral data from the soil surface during operation. The acquired spectral information covers specific moisture-sensitive bands and can reflect the changing characteristics of soil moisture content and material composition, serving as a key input for subsequent moisture prediction. The auxiliary information collection module is used to synchronously collect auxiliary parameters such as the geographic location information of the operation site, sampling timestamp, soil type, and surface temperature. These auxiliary variables are used as covariate inputs to enhance the generalization ability and environmental adaptability of the humidity prediction model. The edge data preprocessing module is deployed on a mobile agricultural machinery platform and connected to the near-infrared spectral acquisition device. It receives the raw reflectance spectral data output by the device in real time and performs preprocessing operations such as spectral smoothing, noise filtering, and standard normal transformation to improve data quality and modeling stability. The federated learning client module is embedded in each agricultural machinery terminal. Based on locally collected and processed spectral data and auxiliary information, it independently trains the surface soil moisture estimation model and the deep prediction sub-model. It then coordinates and aggregates with the cloud through the federated learning protocol, enabling distributed modeling of multiple agricultural machinery without uploading raw data, protecting farm privacy, and enhancing model generalization performance. The cloud-based federated aggregation server receives local model parameters uploaded by each federated learning client module, performs FedAvg model aggregation, outputs a unified prediction model, and periodically transmits it back to each federated learning client module; The human-computer interaction and equipment control module provides a graphical display interface and communication interface, which can present the prediction results to agricultural machinery operators in real time through the screen, or upload them to the agricultural Internet of Things platform to achieve automatic linkage and operation instruction generation with downstream control systems such as seeders and irrigation equipment.

[0028] Example 2 like Figure 2 As shown, this embodiment is basically the same as the previous embodiment, except that a method for predicting deep soil moisture based on near-infrared spectroscopy and federated learning includes: Step 1: Use a near-infrared spectrum acquisition device deployed on a mobile agricultural machine to scan the target farmland area and obtain the reflectance spectrum of the soil surface. The acquired near-infrared spectrum covers the water absorption sensitive band and can reflect the spectral response characteristics of the soil moisture content; Step 2: Utilize the sensor system on the mobile agricultural machinery platform to synchronously record environmental auxiliary information such as soil sampling location, collection time, soil type, and surface temperature in real time, providing feature supplements and sample labels for subsequent modeling; Step 3: Input the near-infrared spectral image data into the edge data preprocessing module, perform denoising, smoothing, baseline correction, wavelet transform, principal component analysis (PCA) and other processing on the raw spectral data to extract stable and representative spectral features; The specific methods of data preprocessing are: Smoothing and filtering the collected raw near-infrared reflectance data using Savitzky–Golay smoothing, wavelet transform, or moving average algorithms to reduce high-frequency noise interference and improve data stability; In order to eliminate systematic errors such as illumination conditions, it is preferred to use standard normal variable transformation and multivariate scatter correction method to perform spectral standardization to enhance the comparability between different samples; Principal component analysis and variable selection algorithm are used to extract key band features related to moisture and construct feature vectors. Step 4: Each client builds a regression model based on its own sample data to estimate the 0-5cm surface soil moisture; The specific method for each client to build a regression model based on its own sample data to estimate the 0-5cm surface soil moisture is as follows: Different types of soil samples were collected. While acquiring near-infrared spectral data, the actual soil moisture values ​​at the corresponding depth (0-5 cm) were measured using standard methods to construct paired feature-label samples. The partial least squares regression PLSR algorithm was used to build the model; The training sample set is input into the partial least squares regression PLSR model, and the training evaluation is performed using K-fold cross validation to adjust the model parameters to obtain the optimal performance; The newly collected near-infrared spectral data were preprocessed in the same way and then input into the trained partial least squares regression (PLSR) model to output the corresponding 0-5 cm surface soil moisture estimation. Step 5: While keeping the data locally stored, each agricultural machine periodically uploads local model parameters (such as PLSR regression coefficients or neural network weights) to the cloud federation aggregation server. The server then executes the aggregation strategy and sends the global model. Each agricultural machine periodically uploads local model parameters to the cloud federation aggregation server. The server executes the aggregation strategy and then issues the global model in the following way: After each agricultural machine completes a certain number of rounds of local training or accumulates enough samples, it triggers a federated communication round and uploads its current local model parameters, namely the PLSR regression coefficients, without uploading any raw spectral data or label information, thereby ensuring data privacy and communication efficiency. The uploaded model parameters are perturbed by differential privacy and encrypted via the TLS protocol to prevent man-in-the-middle attacks or model inversion, thus improving the federated security of the system. The cloud federation aggregation server uses the standard FedAvg strategy to average the model weights uploaded by all clients according to the number of samples and update them as global model parameters; The aggregated model is distributed to each client using a unified global model strategy; The system supports asynchronous federated training, allowing some agricultural machines to skip a round of aggregation due to network disconnection or operation interruption. The aggregation server automatically adjusts the weight distribution to improve the robustness and adaptability of the system. Step 6: Each agricultural machine trains a deep moisture prediction model based on the surface moisture estimation obtained through federated training, auxiliary information, and historical deep moisture data; The specific method of training the deep humidity prediction model is: Based on the one-dimensional unsteady heat conduction equation, the initial shallow humidity is introduced as the boundary condition, and combined with input parameters such as soil thermal diffusivity, water-heat coupling parameters, and surface temperature changes, a spatiotemporal evolution model of deep humidity is derived. The model is described as follows:

[0029] in, Indicates time Moment Depth The moisture content at is the soil moisture diffusion coefficient, Denotes the disturbance term. By numerically solving this equation, we obtain the preliminary prediction value .

[0030] Collect multiple soil samples, and simultaneously collect near-infrared spectra of the surface layer. Use profile sampling to obtain the true moisture value of the corresponding 5-15 cm deep soil layer, and construct a surface moisture-deep moisture paired sample set. The surface moisture value estimated by near-infrared spectroscopy is combined with auxiliary information such as location, time, soil type, and surface temperature as model input features to enhance the model's adaptability and prediction accuracy under various environmental conditions. The gradient boosting tree (GBRT) algorithm was used to establish a regression model of surface-deep humidity. The constructed sample set is used to train the model, and the cross-validation method is used to evaluate the model's prediction performance and optimize the parameter configuration; The newly observed surface humidity and its related environmental parameters are input into the trained model to obtain the machine learning prediction value .

[0031] Prediction results of physical models Predicting results with machine learning Perform weighted fusion, the specific formula is as follows:

[0032] in, The weight coefficient is dynamically adjusted according to the average error of the model in historical samples, the uncertainty of the two models under current observation conditions, and the local model performance evaluation results fed back during the federated learning process to improve the overall prediction performance.

[0033] Each agricultural machine deploys the trained model locally and regularly receives globally optimized model parameters from federated learning to continuously optimize prediction results. Newly observed surface moisture and environmental parameters are fed into the fusion model, which outputs a corresponding deep soil moisture estimate in real time, enabling intelligent prediction while operating. Step 7: The prediction results are displayed on the agricultural machinery terminal interface via a human-computer interaction device or uploaded to a remote agricultural management platform. The system can then link smart devices such as planters and irrigation controllers based on current soil moisture and crop needs, enabling intelligent adjustment of agricultural machinery operating parameters and optimization of operation plans.

[0034] Although the present invention has been described above with reference to embodiments, various modifications may be made thereto and equivalent components may be substituted without departing from the scope of the present invention. In particular, as long as there are no structural conflicts, the various features of the embodiments disclosed herein may be combined with each other in any manner, and the omission of an exhaustive description of such combinations in this specification is solely for the sake of space and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A deep soil moisture prediction system based on near infrared spectroscopy and federated learning, characterized in that: include: A near-infrared spectrum acquisition device, integrated into a mobile agricultural machinery platform, is used to dynamically collect reflectance spectrum data of the soil surface in the near-infrared band during operation; An auxiliary information collection module, integrated into the mobile agricultural machinery platform, is used to synchronously record soil sampling location, time, soil type, and surface temperature data, providing contextual support for subsequent modeling; An edge data preprocessing module, deployed on a mobile agricultural machinery platform and connected to a signal output terminal of the near-infrared spectrum acquisition device, for receiving and processing the reflectance spectrum data collected by the near-infrared spectrum acquisition device; The federated learning client module is embedded in each agricultural machinery terminal. Based on locally collected and processed spectral data and auxiliary information, it independently trains the surface soil moisture estimation model and the deep soil moisture prediction sub-model. It coordinates and aggregates with the cloud through the federated learning protocol, enabling distributed modeling of multiple agricultural machinery without uploading raw data. The cloud-based federated aggregation server receives local model parameters uploaded by each federated learning client module, performs FedAvg model aggregation, outputs a unified prediction model, and periodically transmits it back to the federated learning client module; The human-computer interaction and equipment control module is used to display the deep soil moisture value predicted by the model, and automatically link the irrigation system and sowing control block to achieve closed-loop control of intelligent agricultural machinery operations.

2. The deep soil moisture prediction system based on near infrared spectroscopy and federated learning according to claim 1 is characterized in that: The data preprocessing method of the edge data preprocessing module is: Smoothing and filtering the collected raw near-infrared reflectance data using Savitzky–Golay smoothing, wavelet transform, or moving average algorithm; In order to eliminate systematic errors such as illumination conditions, standard normal variable transformation and multivariate scatter correction method were used to perform spectral standardization and enhance the comparability between different samples; Principal component analysis and variable selection algorithm are used to extract key band features related to moisture and construct feature vectors.

3. The deep soil moisture prediction system based on near infrared spectroscopy and federated learning according to claim 1 is characterized in that: In the federated learning client module, the surface soil moisture estimation method is as follows: different types of soil samples are collected, and while obtaining near-infrared spectral data, the actual soil moisture values ​​at the corresponding depths are measured using standard methods to construct paired feature-label samples; The partial least squares regression (PLSR) algorithm was used to build the model, and the training sample set was input into the partial least squares regression (PLSR) model. K-fold cross validation was used for training evaluation, and the model parameters were adjusted to obtain the optimal performance. The newly collected near-infrared spectral data were preprocessed in the same way and then input into the trained partial least squares regression (PLSR) model to output the corresponding surface soil moisture estimation.

4. The deep soil moisture prediction system based on near infrared spectroscopy and federated learning according to claim 1 is characterized in that: The specific method of training the deep prediction sub-model is: Based on the one-dimensional unsteady-state heat conduction equation, the initial shallow humidity is introduced as a boundary condition. Combined with input parameters such as soil thermal diffusivity, water-heat coupling parameters, and surface temperature changes, a spatiotemporal evolution model of deep humidity is derived. The model is described as follows: ； in, Indicates time Moment Depth The moisture content at is the soil moisture diffusion coefficient, Represents the disturbance term, and the preliminary prediction value is obtained by numerically solving the equation .

5. The deep soil moisture prediction system based on near infrared spectroscopy and federated learning according to claim 4 is characterized in that: Collect multiple soil samples, and simultaneously collect near-infrared spectra of the surface layer. Use profile sampling to obtain the true moisture values ​​of the corresponding 5-15 cm deep soil layer, and construct a surface-deep moisture paired sample set. The surface moisture value estimated by near-infrared spectroscopy is combined with auxiliary information such as location, time, soil type, and surface temperature as model input features to enhance the model's adaptability and prediction accuracy under various environmental conditions. The gradient boosting tree (GBRT) algorithm was used to establish a regression model of surface-deep humidity. The constructed sample set is used to train the model, and the cross-validation method is used to evaluate the model's prediction performance and optimize the parameter configuration; The newly observed surface humidity and its related environmental parameters are input into the trained model to obtain the machine learning prediction value .

6. The deep soil moisture prediction system based on near infrared spectroscopy and federated learning according to claim 5 is characterized in that: Prediction results of physical models Predicting results with machine learning Perform weighted fusion, the specific formula is as follows: ； in, is the weight coefficient, which is dynamically adjusted based on the average error of the model in historical samples, the uncertainty of the two models under current observation conditions, and the local model performance evaluation results fed back during the federated learning process to improve the overall prediction performance; Each agricultural machine completes the model deployment after training locally, and regularly receives the model parameters after global optimization from federated learning to continuously optimize the prediction effect. After the new round of observed surface moisture and environmental parameters are input into the fusion model, the corresponding deep soil moisture estimation is output in real time, realizing the intelligent function of prediction while working.

7. The deep soil moisture prediction system based on near infrared spectroscopy and federated learning according to claim 1 is characterized in that: The specific method in which the cloud-based federated aggregation server issues a unified prediction model after executing the aggregation strategy is as follows: After each agricultural machine completes a certain round of local training or accumulates enough samples, it triggers a federated communication round and uploads its current local model parameters, namely the PLSR regression coefficients, without uploading any original spectral data or label information. The uploaded model parameters are perturbed by differential privacy and encrypted communication is carried out through the TLS protocol; The cloud-based federated aggregation server uses the standard FedAvg strategy to average the model weights uploaded by all federated learning client modules according to the number of samples and updates them as global model parameters. The aggregated model is distributed to each federated learning client module using a unified global model strategy; The system supports asynchronous federated training, allowing some agricultural machinery to skip a round of aggregation due to network disconnection or operation interruption, and the aggregation server automatically adjusts the weight distribution.

8. A method for predicting deep soil moisture based on near infrared spectroscopy and federated learning, used to implement a system for predicting deep soil moisture based on near infrared spectroscopy and federated learning as described in any one of claims 1 to 7, characterized in that: The following steps are involved: Step 1: Scan the target area using a near-infrared spectrum acquisition device to obtain the soil surface reflectance spectrum; Step 2: Use sensors deployed on the mobile agricultural machinery platform to synchronously record soil sampling location, time, soil type, and surface temperature data; Step 3: Receive and process the near-infrared spectral image data output by the near-infrared spectral imaging device through the data preprocessing module on each agricultural machinery terminal; Step 4: Each client builds a regression model based on its own sample data to estimate surface soil moisture; Step 5: While keeping the data locally stored, each agricultural machine periodically uploads local model parameters to the cloud federation aggregation server. The server then executes the aggregation strategy and issues the global model. Step 6: Each agricultural machine trains a deep moisture prediction model based on the surface moisture estimation obtained through federated training, auxiliary information, and historical deep moisture data; Step 7: The human-computer interaction device outputs the prediction results to the display terminal or uploads them to the smart agriculture platform, and interacts with external equipment such as planters and irrigation systems.

Images