Rotary cultivator crushed soil quality detection system and method based on image technology

Abstract

The invention provides a rotary cultivator crushed soil quality detection system and method based on an image technology, and the method comprises the steps: obtaining and preprocessing a soil image in the working process of a rotary cultivator, the soil image being a multi-channel color image; extracting a soil feature region from the preprocessed soil image through an image segmentation technology; constructing a regularization model of soil quality evaluation based on the texture information of the soil feature region, and constructing a minimization problem of the soil quality evaluation according to the regularization model; calculating parameters of soil quality evaluation based on a minimization problem, wherein the parameters comprise indexes such as soil particle size distribution and soil humidity; and according to the calculated soil quality evaluation parameters, evaluating the crushed soil quality of the rotary cultivator to obtain a final crushed soil quality evaluation result. According to the method, key indexes such as soil particle size distribution and humidity can be rapidly and accurately evaluated, and the efficiency of agricultural mechanized operation and the quality of soil cultivation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural mechanization, and more specifically, to a system and method for detecting soil crushing quality of a rotary tiller based on image technology. Background Art

[0002] The existing technology for detecting soil quality by rotary tillers mainly relies on manual observation and empirical judgment. This method is not only inefficient, but also easily affected by human factors, which limits the accuracy and consistency of the detection results. In addition, traditional detection methods cannot accurately measure key indicators such as soil particle size distribution and moisture, which to a certain extent limits the further development of agricultural mechanization and the refinement of soil management. With the development of computer vision and image processing technology, people have begun to explore methods for automatically detecting soil quality using these technologies. However, the existing image-based detection technology still faces problems such as complex image acquisition conditions, inaccurate soil feature extraction, and imperfect evaluation models in practical applications. The existence of these problems makes it difficult for existing technologies to meet the needs of modern agricultural production for rapid and accurate detection of soil quality.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: the soil image acquisition and preprocessing technology cannot adapt well to the changing field environment; the soil feature extraction and analysis methods are not accurate and comprehensive enough to fully reflect the actual quality of the soil; the existing soil quality evaluation models are often oversimplified and cannot comprehensively consider multiple influencing factors, resulting in insufficient practicality and accuracy of the evaluation results. Summary of the invention

[0004] The invention provides a system and method for detecting soil crushing quality of a rotary tiller based on image technology.

[0005] In a first aspect of the present invention, a method for detecting soil crushing quality of a rotary tiller based on image technology is provided, comprising:

[0006] Acquire a soil image during the working process of the rotary tiller and perform preprocessing, wherein the soil image is a multi-channel color image;

[0007] Extract soil characteristic areas from the preprocessed soil image using image segmentation technology;

[0008] A regularized model for soil quality evaluation is constructed based on the texture information of soil characteristic areas, and the minimization problem of soil quality evaluation is constructed accordingly.

[0009] Calculate the parameters for soil quality evaluation based on minimization problems, including soil particle size distribution and soil moisture;

[0010] According to the calculated soil quality evaluation parameters, the soil crushing quality of the rotary tiller is evaluated to obtain the final soil crushing quality evaluation result.

[0011] Furthermore, the texture information of the soil characteristic area includes:

[0012] Soil particle size distribution information;

[0013] Soil moisture distribution information;

[0014] Information on the uniformity of soil color;

[0015] Information on soil structural integrity.

[0016] Furthermore, soil quality evaluation parameters calculated based on the minimization problem include:

[0017] Applying an optimization algorithm to solve the minimization problem; wherein the optimization algorithm includes:

[0018] Introducing auxiliary variables into the objective function of the minimization problem to convert the minimization problem into an unconstrained optimization problem;

[0019] The unconstrained optimization problem is decomposed into separate sub-problems, and the soil quality evaluation parameters are obtained by alternately iterating and solving the sub-problems.

[0020] Furthermore, the auxiliary variable y subproblem is solved by an iterative algorithm, including:

[0021] Introduce dual variables to transform the solution of the auxiliary variable y subproblem into a saddle point problem;

[0022] The saddle point problem is solved by an iterative algorithm to obtain the optimal auxiliary variable y.

[0023] In a second aspect of the present invention, a rotary tiller soil quality detection system based on image technology is provided, comprising:

[0024] An image acquisition module is used to obtain soil images in real time during the operation of the rotary tiller;

[0025] Image preprocessing module, used to perform preprocessing operations such as noise removal and contrast enhancement on the collected soil images;

[0026] An image segmentation module is used to extract soil characteristic areas from the preprocessed soil image;

[0027] Texture analysis module, used to analyze the texture information of soil characteristic areas and build a regularized model for soil quality evaluation;

[0028] Parameter calculation module, used to calculate soil quality evaluation parameters according to the regularization model, including indicators such as soil particle size distribution and soil moisture;

[0029] The quality assessment module is used to comprehensively assess the soil crushing quality of the rotary tiller based on the calculated soil quality assessment parameters and output the assessment results.

[0030] According to the above-mentioned embodiments of the present invention, at least the following beneficial effects are achieved: the rotary tiller soil quality detection method of the present invention can collect and preprocess the soil image during the operation of the rotary tiller in real time by adopting advanced image processing technology, effectively remove image noise and enhance contrast, thereby improving the recognition accuracy of soil feature areas. The method uses image segmentation technology to accurately extract soil feature areas, and constructs a regularized model based on soil texture information, which can comprehensively evaluate key indicators such as soil particle size distribution and moisture, and achieve accurate evaluation of soil quality.

[0031] In addition, by constructing a minimization problem and applying an optimization algorithm, this method can efficiently calculate soil quality evaluation parameters, including soil particle size and moisture. This method can not only improve the automation and intelligence level of the evaluation process, but also dynamically adjust the evaluation parameters according to different soil conditions and operation requirements, and realize personalized evaluation of the soil crushing quality of the rotary tiller. Through the comprehensive evaluation results, scientific decision-making support can be provided for agricultural mechanization operations, thereby optimizing the soil cultivation process and improving agricultural production efficiency and crop yields. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, in which:

[0033] Figure 1 A schematic diagram of a flow chart of a method for detecting soil crushing quality of a rotary tiller based on image technology provided by an embodiment of the present invention;

[0034] Figure 2 A schematic diagram of the structure of a rotary tiller soil quality detection system based on image technology provided by an embodiment of the present invention;

[0035] Figure 3 The schematic diagram schematically shows the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are provided only to enable those skilled in the art to better understand and implement the present invention, and are not intended to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to fully convey the scope of the present invention to those skilled in the art.

[0037] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, apparatus, method or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: complete hardware, complete software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0038] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0039] Reference below Figure 1 , Figure 1 The following is a flow chart of a method for detecting soil quality of a rotary tiller based on image technology according to an embodiment of the present invention. Figure 1 As shown, a method 100 for detecting soil quality of a rotary tiller based on image technology includes:

[0040] Step 101, obtaining a soil image during the operation of the rotary tiller and performing preprocessing, wherein the soil image is a multi-channel color image;

[0041] Step 102, extracting soil characteristic areas from the preprocessed soil image by using image segmentation technology;

[0042] Step 103, constructing a regularized model for soil quality evaluation based on the texture information of the soil characteristic area, and constructing a minimization problem for soil quality evaluation based on the regularized model;

[0043] Step 104, calculating parameters for soil quality evaluation based on the minimization problem, including indicators such as soil particle size distribution and soil moisture;

[0044] Step 105, evaluating the soil crushing quality of the rotary tiller according to the calculated soil quality evaluation parameters to obtain a final soil crushing quality evaluation result.

[0045] It should be noted that obtaining soil images during the operation of the rotary tiller means that a high-resolution camera needs to be installed on the rotary tiller to capture the image of the soil in real time. Preprocessing steps may include denoising, contrast enhancement, etc. to improve image quality. Image segmentation technology can use edge detection, threshold segmentation and other methods to extract soil feature areas, which are the basis for subsequent analysis.

[0046] Preferably, the preprocessing of soil images can use advanced machine learning algorithms to automatically identify and remove noise in the image, while enhancing the contrast of soil features. In the image segmentation process, a convolutional neural network based on deep learning can be used to improve the recognition accuracy of feature areas. In addition, when constructing a regularized model, a variety of soil quality evaluation indicators can be introduced, such as soil particle size distribution, moisture, color uniformity, and structural integrity, to form a comprehensive evaluation system.

[0047] In some embodiments, the texture information of the soil characteristic area includes:

[0048] Soil particle size distribution information;

[0049] Soil moisture distribution information;

[0050] Information on the uniformity of soil color;

[0051] Information on soil structural integrity.

[0052] It should be noted that the texture information of the soil characteristic area is one of the key factors in soil quality evaluation. This information includes not only the distribution information of soil particle size and moisture, but also the uniformity of soil color and the integrity of soil structure. The accurate extraction of these features is crucial to building an accurate soil quality evaluation model.

[0053] Specifically, the distribution information of soil particle size can be obtained through the particle size analysis algorithm in image processing technology, such as using the gray level co-occurrence matrix of the image to evaluate the distribution of different particle sizes. The distribution information of soil moisture can be inferred by analyzing the brightness and contrast of the soil image. The uniformity of soil color can be evaluated by the distribution of color histogram, and the integrity of soil structure can be determined by analyzing the connection and arrangement between soil particles.

[0054] Preferably, the extraction of soil characteristic areas can adopt an image segmentation algorithm based on deep learning, such as a convolutional neural network (CNN), to improve the accuracy of feature extraction. In terms of parameter setting, the hyperparameters of the algorithm, such as learning rate, batch size, etc., can be adjusted according to different soil types and operating conditions to obtain the best model performance. In addition, the weight coefficient of the regularization term can be dynamically adjusted according to the actual conditions of the soil to ensure the adaptability and robustness of the evaluation model. As an alternative, other image processing techniques, such as edge detection or texture analysis, can also be used to extract texture information of soil characteristic areas.

[0055] In some embodiments, the objective function of the minimization problem is:

[0056]

[0057] Where f is the preprocessed soil image, x is the soil quality evaluation parameter; ||·|| 2 is the Euclidean norm, R(x) is the regularization term of soil quality, and λ and μ are the penalty weight coefficients greater than 0.

[0058] It should be noted that the objective function of the minimization problem is built on the basis of the mathematical model of soil image processing and quality evaluation. The objective function minimizes the difference between the soil image and the soil quality evaluation parameters, and adds a regularization term to improve the robustness of the model. The regularization term here helps prevent the model from overfitting and ensures the generalization ability of the evaluation results.

[0059] Specifically, R(x) in the objective function represents the regularization term of soil quality, which is usually composed of multiple sub-terms, each of which corresponds to a soil quality evaluation index. For example, soil particle size distribution, moisture and other indicators can be used as part of the regularization term. Parameters λ and μ are regularization parameters, which control the trade-off between the model's data fitting and the regularization term. The setting of these parameters needs to be determined according to the actual soil characteristics and evaluation requirements to achieve the best model performance.

[0060] Preferably, the soil image f in the objective function may be pre-processed in a specific manner, such as removing noise by filtering, improving image quality by contrast enhancement, etc. The soil quality evaluation parameter x may include but is not limited to soil particle size distribution parameters, moisture parameters, etc., which may be obtained by image analysis. For example, the soil particle size distribution can be determined by texture analysis of the image, and the moisture can be estimated by the reflectivity characteristics of the image. The regularization parameters λ and μ can be optimized by methods such as cross-validation to ensure that the model can maintain high evaluation accuracy and robustness under different conditions. In addition, the specific form of the regularization term R(x) can be adjusted according to different soil characteristics and evaluation requirements to adapt to different application scenarios.

[0061] In some embodiments, the regularization term based on the texture information of the soil characteristic region is as shown in the following formula:

[0062]

[0063] Among them, r i (x) is the regularization function of the i-th soil quality evaluation index, w i is the corresponding weight coefficient, and n is the number of evaluation indicators.

[0064] It should be noted that the regularization term is a key component, which combines the regularization functions of multiple evaluation indicators to form a comprehensive regularization model. This model not only considers the importance of individual indicators, but also balances their impact on the final evaluation results through weight coefficients.

[0065] Specifically, each r in the regularization term i (x) represents the regularization function of the u-th soil quality evaluation index, which may include the distribution of soil particle size, uniformity of soil moisture, consistency of soil color, etc. Weight coefficient w i The parameters are used to adjust the relative importance of each indicator in the evaluation process. These parameters can be set according to the actual soil characteristics and the needs of agricultural operations to ensure the accuracy and applicability of the evaluation results.

[0066] Preferably, the setting of the regularization term can be further refined. For example, the regularization function of soil particle size distribution can use a Gaussian mixture model to describe the distribution of different particle size levels, while the regularization function of soil moisture can be designed based on the statistical characteristics of soil moisture. In addition, the uniformity of soil color can be measured by the balance of the color histogram, and the integrity of soil structure can be evaluated by texture analysis methods. In practical applications, appropriate regularization functions and weight coefficients can be selected according to the specific conditions of the soil and the working requirements of the rotary tiller to achieve the best evaluation effect.

[0067] In some embodiments, calculating soil quality evaluation parameters based on a minimization problem includes:

[0068] Applying an optimization algorithm to solve the minimization problem; wherein the optimization algorithm includes:

[0069] Introducing auxiliary variables into the objective function of the minimization problem to convert the minimization problem into an unconstrained optimization problem;

[0070] The unconstrained optimization problem is decomposed into separate sub-problems, and the soil quality evaluation parameters are obtained by alternately iterating and solving the sub-problems.

[0071] It should be noted that applying an optimization algorithm to solve a minimization problem involves using a specific mathematical method to find an optimal solution that satisfies specific conditions. In the present invention, an optimization algorithm is used to minimize the objective function while taking into account the regularization term and penalty term weight coefficients of soil quality evaluation.

[0072] Specifically, the optimization algorithm can be gradient descent, Newton's method or other advanced optimization techniques. When setting the parameters of these algorithms, the characteristics of the soil image and the complexity of the evaluation indicators need to be considered. For example, the choice of the step size parameter will directly affect the convergence speed and stability of the algorithm. In addition, the setting of the weight coefficients of the regularization term and the penalty term needs to be determined through experiments to ensure that the model can capture the key characteristics of the soil without losing its generalization ability due to overfitting.

[0073] Preferably, the optimization algorithm can adopt an adaptive step size adjustment strategy to dynamically adjust the step size according to the improvement of each iteration to speed up the convergence and avoid falling into the local optimum. More specifically, a momentum term can be introduced to help the algorithm jump out of the saddle point, or a quasi-Newton method such as L-BFGS can be used to improve the computational efficiency. In addition, it is also possible to consider using parallel computing technology to process large-scale data sets to meet the needs of rotary tillers of different sizes. In practical applications, the composition of the regularization term can also be adjusted according to the specific conditions of the soil to better adapt to different soil types and operating environments.

[0074] In some embodiments, the auxiliary variable is:

[0075] y=D(x)

[0076] Where D is the transformation operator of soil quality evaluation parameters.

[0077] It should be noted that the auxiliary variable is a key concept introduced in the optimization algorithm. It is used to transform the minimization problem into an unconstrained problem, thereby simplifying the problem-solving process. Auxiliary variables are usually associated with certain parameters or constraints in the original problem. By introducing auxiliary variables, the solution space can be explored more effectively and the solution efficiency can be improved.

[0078] Specifically, the auxiliary variable y is defined as the output of the transformation operator D of the soil quality evaluation parameter x, that is, y=D(x). The transformation operator D here can be linear or nonlinear, and the specific form depends on the characteristics of the soil quality evaluation parameter and the requirements of the problem. For example, if the soil quality evaluation parameter x includes soil particle size and moisture, the transformation operator D may include normalization or standardization of these parameters.

[0079] Preferably, the choice of transformation operator D can be customized based on prior knowledge of soil properties. For example, if the soil particle size distribution has specific statistical properties, a transformation operator can be designed to enhance the expression of these properties. In addition, the introduction of auxiliary variables y can also include regularization of soil quality evaluation parameters to avoid overfitting and improve the generalization ability of the model. In practical applications, L1 or L2 regularization methods can be used to control model complexity by adjusting the regularization coefficient.

[0080] In some embodiments, the separate sub-problems include an auxiliary variable y sub-problem and a soil quality evaluation parameter x sub-problem, as shown in the following formula:

[0081]

[0082] In the formula, <·,·> represents the dot product of vectors, and D(x) is the transformation operator of soil quality evaluation parameters.

[0083] It should be noted that the individual sub-problems refer to the sub-problems that need to be solved independently after the minimization problem is decomposed. These sub-problems are independent of each other but interrelated. Through alternate iterative solutions, the optimal solution of the soil quality evaluation parameters can be gradually approached.

[0084] Specifically, the auxiliary variable subproblem and the soil quality evaluation parameter subproblem correspond to different parts of the minimization problem. The auxiliary variable subproblem focuses on the difference between the soil image and the image after the soil quality evaluation parameter transformation, while the soil quality evaluation parameter subproblem focuses on the optimization of the soil quality evaluation parameter itself. During the solution process, the specific number of iterations, step size parameters, and regularization term weight coefficients can be set. The selection of these parameters will directly affect the efficiency of the solution and the accuracy of the results.

[0085] Preferably, in order to improve the efficiency and accuracy of the solution, some specific iterative algorithms can be used, such as the gradient descent method, the Newton method or the quasi-Newton method. In the solution of the auxiliary variable subproblem, the dual variable can be introduced to transform the problem into a saddle point problem, and a specific iterative algorithm can be used to solve it. For example, the augmented Lagrange multiplier method (ALM) can be used to solve it, and the optimal solution is gradually approached by iteratively updating the auxiliary variables and the dual variables. In the solution of the soil quality evaluation parameter subproblem, an appropriate step size adjustment strategy, such as an adaptive step size or momentum method, can be used to accelerate convergence and improve the stability of the solution. In addition, it is also possible to consider introducing some regularization techniques, such as L1 regularization or L2 regularization, to enhance the generalization ability of the model and prevent the occurrence of overfitting.

[0086] In some embodiments, the auxiliary variable y subproblem is solved by an iterative algorithm, including:

[0087] Introduce dual variables to transform the solution of the auxiliary variable y subproblem into a saddle point problem;

[0088] The saddle point problem is solved by an iterative algorithm to obtain the optimal auxiliary variable y.

[0089] It should be noted that the solution process of the auxiliary variable subproblem involves introducing dual variables to transform the problem into a saddle point problem. This process is to solve the optimization problem more efficiently by introducing additional variables to balance different parts of the objective function, thereby simplifying the solution of the problem.

[0090] Specifically, the introduction of dual variables mentioned in the auxiliary variable subproblem refers to adding a new variable in the optimization problem, which is associated with a constraint in the original problem to form a Lagrangian function. The saddle point problem refers to finding a point in the optimization problem that is both a local minimum and a local maximum of the function among all possible variable values. In the present invention, the saddle point problem is solved by an iterative algorithm, that is, by continuously iteratively updating the value of the variable, the optimal solution is gradually approached.

[0091] Preferably, the iterative solution process of the auxiliary variable subproblem can be further refined. For example, during the iteration process, a specific step size parameter adjustment strategy can be adopted, such as using an adaptive step size or a learning rate decay strategy, to accelerate the convergence speed or improve the solution stability. In addition, for the proximal operator of the regularization term, different algorithms can be selected to implement it, such as a soft threshold algorithm or a gradient projection algorithm. These algorithms can be selected according to the characteristics of the actual problem to achieve a better solution effect. At the same time, the iterative algorithm can be improved, such as introducing a momentum term or using nonlinear convergence acceleration technology to further improve the efficiency and robustness of the algorithm.

[0092] In some embodiments, the calculation formula of the auxiliary variable y sub-problem is:

[0093]

[0094] Where prox μR represents the proximal operator of the regularization term R, α is the step size parameter, For x l The objective function gradient at ;

[0095] The calculation formula of the soil quality evaluation parameter x sub-problem is:

[0096]

[0097] In the formula, β is the step size parameter, Represents the gradient operator with respect to x.

[0098] It should be noted that the solution process of the auxiliary variable subproblem involves introducing dual variables, converting the problem into a saddle point problem, and solving it through an iterative algorithm. This method can effectively find the optimal auxiliary variable and thus improve accuracy.

[0099] Specifically, the calculation formula of the auxiliary variable subproblem involves the setting of the proximal operator of the regularization term, the objective function gradient, and the step size parameter. In practical applications, the proximal operator of the regularization term can be a form of projection operation, the objective function gradient can be obtained by differentiating the objective function, and the step size parameter needs to be adjusted according to the specific situation of the problem to ensure the convergence and stability of the algorithm.

[0100] Preferably, the iterative update formula of the auxiliary variables can be further refined. For example, an adaptive step size strategy can be adopted to dynamically adjust the step size parameter according to the gradient change of each iteration. In addition, it is also possible to consider introducing some regularization techniques, such as L1 or L2 regularization, to enhance the generalization ability of the model. In some cases, it is also possible to consider using nonlinear optimization algorithms, such as Newton's method or quasi-Newton's method, to improve the solution efficiency. These alternatives can be selected and adjusted according to different application scenarios and performance requirements.

[0101] The above-mentioned embodiments of the present invention have the following beneficial effects: The rotary tiller soil crushing quality detection method of the present invention can realize the accurate extraction of soil characteristics and the automation of quality evaluation through the application of image technology. The method first pre-processes the soil image to ensure the quality and accuracy of the image data, laying a solid foundation for the subsequent soil characteristic analysis. Through image segmentation technology, the soil characteristic area can be effectively separated from the complex background, which helps to more accurately analyze the texture information of the soil. Furthermore, the regularized model constructed based on the texture information of the soil characteristic area can systematically evaluate the soil quality, including key indicators such as particle size distribution and humidity, thereby providing a scientific evaluation method for the soil crushing quality of the rotary tiller.

[0102] In addition, the method of the present invention also includes constructing a minimization problem and applying an optimization algorithm to solve soil quality evaluation parameters, which can improve the efficiency and accuracy of parameter calculation. By converting the minimization problem into an unconstrained optimization problem and adopting an alternating iterative solution strategy, soil quality evaluation parameters, including soil particle size and moisture, etc., can be effectively solved. This method can not only improve the accuracy of soil quality evaluation, but also enhance the robustness and adaptability of the model by introducing auxiliary variables and transformation operators, making the evaluation results more reliable. Through this method, the quality of soil crushing by the rotary tiller can be evaluated more finely and comprehensively, thereby providing more accurate guidance for agricultural mechanization operations.

[0103] like Figure 2 As shown, in some embodiments, a rotary tiller soil quality detection system 200 based on image technology includes:

[0104] An image acquisition module 201 is used to obtain soil images in real time during the operation of the rotary tiller;

[0105] The image preprocessing module 202 is used to perform preprocessing operations such as noise removal and contrast enhancement on the collected soil image;

[0106] An image segmentation module 203 is used to extract soil characteristic regions from the preprocessed soil image;

[0107] Texture analysis module 204, used to analyze the texture information of soil characteristic areas and build a regularized model for soil quality evaluation;

[0108] The parameter calculation module 205 is used to calculate soil quality evaluation parameters according to the regularization model, including indicators such as soil particle size distribution and soil moisture;

[0109] The quality assessment module 206 is used to comprehensively assess the soil crushing quality of the rotary tiller according to the calculated soil quality assessment parameters, and output the assessment result.

[0110] It can be understood that the modules recorded in the rotary tiller soil quality detection system 200 based on image technology are similar to those in the reference Figure 1 The steps in the described rotary tiller soil quality detection method based on image technology correspond to each other. Therefore, the operations, features and beneficial effects described above for the rotary tiller soil quality detection method based on image technology are also applicable to the rotary tiller soil quality detection system 200 based on image technology and the modules contained therein, and will not be repeated here.

[0111] Reference below Figure 3 , which shows a schematic diagram of a structure 300 of an electronic device suitable for implementing some embodiments of the present invention. The electronic devices in some embodiments of the present invention may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 3 The terminal device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0112] like Figure 3As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0113] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0114] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods, wherein the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or terminal devices such as a computer, a server, a mobile phone, and a tablet.

[0115] The above descriptions are only some preferred embodiments of the present invention and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the above features are replaced with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention to form a technical solution.

Claims

1. A method for detecting soil quality of a rotary tiller based on image technology, characterized in that: The method comprises: Acquire a soil image during the working process of the rotary tiller and perform preprocessing, wherein the soil image is a multi-channel color image; Extract soil characteristic areas from the preprocessed soil image using image segmentation technology; A regularized model for soil quality evaluation is constructed based on the texture information of soil characteristic areas, and the minimization problem of soil quality evaluation is constructed accordingly. Calculate the parameters for soil quality evaluation based on minimization problems, including soil particle size distribution and soil moisture index; According to the calculated soil quality evaluation parameters, the soil crushing quality of the rotary tiller is evaluated to obtain the final soil crushing quality evaluation result.

2. The method for detecting soil quality of a rotary tiller based on image technology according to claim 1 is characterized in that: The texture information of the soil characteristic area includes: Soil particle size distribution information; Soil moisture distribution information; Information on the uniformity of soil color; Information on soil structural integrity.

3. The method for detecting soil quality of a rotary tiller based on image technology according to claim 2 is characterized in that: Soil quality evaluation parameters calculated based on minimization problems include: Applying an optimization algorithm to solve the minimization problem; wherein the optimization algorithm includes: Introducing auxiliary variables into the objective function of the minimization problem to convert the minimization problem into an unconstrained optimization problem; The unconstrained optimization problem is decomposed into separate sub-problems, and the soil quality evaluation parameters are obtained by alternately iterating and solving the sub-problems.

4. The method for detecting soil quality of a rotary tiller based on image technology according to claim 3 is characterized in that: The individual sub-problems include the auxiliary variable y sub-problem and the soil quality evaluation parameter x sub-problem.

5. The method for detecting soil crushing quality of a rotary tiller based on image technology according to claim 4 is characterized in that: The auxiliary variable y subproblem is solved by an iterative algorithm, including: Introduce dual variables to transform the solution of the auxiliary variable y subproblem into a saddle point problem; The saddle point problem is solved by an iterative algorithm to obtain the optimal auxiliary variable y.

6. A soil quality detection system for a rotary tiller based on image technology, the system implementing the method according to claim 1, characterized in that: The system comprises: An image acquisition module is used to obtain soil images in real time during the operation of the rotary tiller; Image preprocessing module, used to perform preprocessing operations such as noise removal and contrast enhancement on the collected soil images; An image segmentation module is used to extract soil characteristic areas from the preprocessed soil image; Texture analysis module, used to analyze the texture information of soil characteristic areas and build a regularized model for soil quality evaluation; A parameter calculation module is used to calculate soil quality evaluation parameters, including soil particle size distribution and soil moisture index, based on the regularization model; The quality assessment module is used to comprehensively assess the soil crushing quality of the rotary tiller based on the calculated soil quality assessment parameters and output the assessment results.

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