Farmland surface residual film shielding restoration and curled residual film unfolding area prediction method and system

Abstract

The invention discloses a farmland surface residual film shielding restoration and curled residual film unfolding area prediction method and system, and relates to the technical field of machine vision, and the method comprises the steps: inputting a target image into a semantic segmentation model, and obtaining an image with a mark; all the pixels marked as the soil background in the image with the mark are set to be black, all the pixels marked as the shielding object in the image with the mark are set to be white, and a to-be-restored image is obtained; performing shielding object removal and image restoration on the to-be-restored image to obtain a curled residual film restored image, and determining a curl index prediction parameter and a pixel area of the curled residual film; inputting the curl index prediction parameter into a curl index prediction model to obtain a curl index prediction value; and according to the predicted value of the curl index and the pixel area of the curl residual film, determining a predicted value of the pixel area after the curl residual film is unfolded. According to the method, the residual film shielding repair is realized, and the prediction precision of the unfolded area of the curled residual film is improved.

Description

Technical Field

[0001] The present application relates to the field of machine vision technology, and in particular to a method and system for repairing residual film occlusion on farmland surface and predicting the unfolding area of ​​curled residual film. Background Art

[0002] Agricultural mulch films have good functions of warming, moisture conservation and weed control, which can effectively solve the serious problem of "extremely low yield" in arid and cold regions, and increase yield by more than 30%. It is an important means to improve the water utilization rate of farmland crops, alleviate water shortage, reduce pests and diseases, and promote plant growth. However, agricultural mulch films are mainly made of polyethylene materials, which have a long degradation period. Excessive use of agricultural mulch films and failure to recycle them in time can easily cause residual films to accumulate in the soil year by year, causing serious white pollution.

[0003] The treatment of residual film pollution in farmland is a systematic project. In addition to developing effective residual film recycling equipment, it is also of great significance to carry out research on residual film pollution assessment. The residual film on the surface of farmland before sowing has the characteristics of natural curling and obstruction by objects, which makes it difficult to efficiently and accurately identify and assess the pollution of residual film in farmland.

[0004] At present, manual field sampling is mostly used to assess and monitor residual film pollution in farmland, which is labor-intensive and inefficient. In recent years, relevant scholars have conducted preliminary explorations on the feasibility of residual film pollution on the surface through machine vision methods. In response to the problem of residual film pollution assessment in farmland, Liang Changjiang's team conducted research based on the residual film image segmentation algorithm of drones and conducted field residual film recognition tests. Among them, the iterative threshold segmentation algorithm was the best, with a recognition accuracy of 71%; subsequently, in order to overcome the influence of light on the residual film recognition accuracy, the team used the image S component and pulse neural network segmentation method to study the assessment of residual film pollution in tobacco fields after the harvest period, with an average recognition rate of 87.49%. However, the team's research was aimed at farmland that had not been treated with film picking and plowing after the autumn harvest, and the film had good continuity and low fragmentation. Hu Can et al. estimated the degree of residual film pollution by using the threshold segmentation of low-altitude residual film images of drones and the method of counting pixel areas, with an average recognition error of 4.82%. However, most of the above studies directly segmented and extracted visible residual film fragments through simple threshold segmentation or machine learning methods, and then assessed the degree of residual film pollution on the surface of farmland.

[0005] Under the actual surface environmental conditions of farmland before sowing, the natural curling of residual film in the field and the obstruction of residual film by cotton stalks and drip irrigation tapes will have a significant impact on the accuracy of residual film pollution assessment. Summary of the invention

[0006] The purpose of this application is to provide a method and system for repairing residual film obstruction on the surface of farmland and predicting the area of ​​curled residual film expansion, so as to solve the problem of low accuracy in repairing residual film obstruction on the surface of farmland and predicting the area of ​​curled residual film expansion.

[0007] To achieve the above objectives, this application provides the following solutions:

[0008] In a first aspect, the present application provides a method for repairing residual film on the surface of farmland and predicting the unfolding area of ​​curled residual film, comprising:

[0009] Acquire a target image; the target image is an image of the farmland to be tested when the curled residual film in the farmland to be tested is blocked by an obstruction;

[0010] The target image is input into a semantic segmentation model to obtain an image of the farmland to be tested with annotations; the annotations include: soil background, residual film and occluders; the semantic segmentation model is obtained by training an improved U-Net model using the first training set;

[0011] Each pixel marked as soil background in the labeled image of the farmland to be tested is set to black, and each pixel marked as occlusion in the labeled image of the farmland to be tested is set to white, to obtain an image to be repaired;

[0012] Using a fast frequency selective reconstruction algorithm, the occluder is removed and the image is repaired to obtain a curled residual film repair image; the curled residual film repair image is an image of the curled residual film in the tested farmland after repair;

[0013] Determine the curl index prediction parameters and pixel area of ​​the curled residual film in the curled residual film repair image; the curl index prediction parameters include: color and texture features;

[0014] Inputting the curling index prediction parameters of the curled residual film of the farmland to be tested into the curling index prediction model to obtain the predicted value of the curling index of the curled residual film of the farmland to be tested; the curling index prediction model is obtained by training the machine learning model using the second training set;

[0015] According to the predicted value of the curling index of the curled residual film of the tested farmland and the pixel area of ​​the curled residual film in the curled residual film repair image, the predicted value of the pixel area of ​​the curled residual film of the tested farmland after being unfolded is determined.

[0016] Optionally, the process of determining the semantic segmentation model includes:

[0017] Acquire a first training set; the first training set includes: images of a plurality of sample farmlands with curled residual films blocked by obstructions and corresponding annotated images;

[0018] Constructing the improved U-Net model; the improved U-Net model includes: an input module, multiple multi-scale feature extraction modules, multiple pooling modules, multiple convolution modules, multiple upsampling modules and an output module; the multi-scale feature extraction module includes a main branch of 3×3 standard convolution, an auxiliary branch of 1×1 convolution and an auxiliary branch of 5×5 convolution;

[0019] The improved U-Net model is trained with images of curled residual film of each sample farmland when it is blocked by an obstruction as input and corresponding labeled images as output to obtain the semantic segmentation model.

[0020] Optionally, using a fast frequency selective reconstruction algorithm, removing occluders and performing image restoration on the image to be restored to obtain a restored image of the curled residual film, including:

[0021] Reconstructing each white pixel in the image to be repaired by using a fast frequency selective reconstruction algorithm to obtain an initial repaired image; the initial repaired image includes black pixels and non-black pixels;

[0022] The area formed by the non-black pixels in the initial repair image is determined as the curled residual film repair image.

[0023] Optionally, the process of determining the curl index prediction model includes:

[0024] Acquire a second training set; the second training set includes: curl index prediction parameters and actual values ​​of curl index of a plurality of sample curled residual films;

[0025] Building the machine learning model;

[0026] The curl index prediction parameters of the curled residual films of each sample are used as input, and the corresponding measured values ​​of the curl index are used as output, and the machine learning model is trained to obtain the curl index prediction model.

[0027] Optionally, according to the predicted value of the curling index of the curled residual film of the tested farmland and the pixel area of ​​the curled residual film in the curled residual film repair image, the predicted value of the pixel area of ​​the curled residual film of the tested farmland after being unfolded is determined, including:

[0028] The curling index calculation formula is used to calculate the predicted value of the pixel area of ​​the curled residual film after being unfolded in the tested farmland according to the predicted value of the curling index of the curled residual film in the tested farmland and the pixel area of ​​the curled residual film in the curled residual film repair image; the curling index calculation formula is:

[0029]

[0030] Among them, RFCI is the curling index; S1 is the pixel area of ​​the curled residual film; S2 is the pixel area after the curled residual film is unfolded.

[0031] Optionally, the curl index calculation formula is obtained by fitting the measured values ​​of the pixel areas of a plurality of sample curled residual films, the measured values ​​of the pixel areas after being unfolded, and the measured values ​​of the curl index.

[0032] In the second aspect, the present application provides a system for repairing residual film on the surface of farmland and predicting the area of ​​curled residual film, so as to realize any of the above methods for repairing residual film on the surface of farmland and predicting the area of ​​curled residual film. The system for repairing residual film on the surface of farmland and predicting the area of ​​curled residual film includes:

[0033] A target image acquisition module is used to acquire a target image; the target image is an image of the farmland to be tested when the curled residual film in the farmland to be tested is blocked by an obstruction;

[0034] A semantic segmentation module is used to input the target image into a semantic segmentation model to obtain an image with annotations of the farmland to be tested; the annotations include: soil background, residual film and occluders; the semantic segmentation model is obtained by training the improved U-Net model using the first training set;

[0035] The module for determining the image to be repaired is used to set all pixels marked as soil background in the marked image of the farmland to be tested to black, and to set all pixels marked as occlusion in the marked image of the farmland to be tested to white, so as to obtain the image to be repaired;

[0036] A residual film repair module is used to remove occluders and perform image repair on the image to be repaired using a fast frequency selective reconstruction algorithm to obtain a curled residual film repair image; the curled residual film repair image is an image of the curled residual film in the farmland to be tested after repair;

[0037] A parameter acquisition module is used to determine the curl index prediction parameters and pixel area of ​​the curled residual film in the curled residual film repair image; the curl index prediction parameters include: color and texture features;

[0038] A curling index prediction module, used for inputting the curling index prediction parameters of the curled residual film of the farmland to be tested into the curling index prediction model to obtain the predicted value of the curling index of the curled residual film of the farmland to be tested; the curling index prediction model is obtained by training the machine learning model using the second training set;

[0039] The unfolding area prediction module is used to determine the predicted value of the pixel area of ​​the curled residual film of the farmland to be tested after being unfolded according to the predicted value of the curling index of the curled residual film of the farmland to be tested and the pixel area of ​​the curled residual film in the curled residual film repair image.

[0040] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0041] The present application discloses a method and system for repairing the occlusion of residual film on the surface of farmland and predicting the unfolding area of ​​curled residual film. First, a target image is obtained; the target image is an image of the farmland to be tested when the curled residual film in the farmland to be tested is blocked by an occluding object; then, the target image is input into a semantic segmentation model to obtain an annotated image of the farmland to be tested; the annotations include: soil background, residual film and occluding object; the semantic segmentation model is obtained by training an improved U-Net model using a first training set; secondly, each pixel marked as soil background in the annotated image of the farmland to be tested is set to black, and each pixel marked as occluding object in the annotated image of the farmland to be tested is set to white, to obtain an image to be repaired; then, a fast frequency selection is used to select the target image. A selective reconstruction algorithm is used to remove occluders and perform image restoration on the image to be repaired to obtain a curled residual film restoration image; curl index prediction parameters and pixel area of ​​the curled residual film in the curled residual film restoration image are determined; curl index prediction parameters include: color and texture features; again, the curl index prediction parameters of the curled residual film of the farmland to be tested are input into the curl index prediction model to obtain the predicted value of the curl index of the curled residual film of the farmland to be tested; the curl index prediction model is obtained by training the machine learning model using the second training set; finally, according to the predicted value of the curl index of the curled residual film of the farmland to be tested and the pixel area of ​​the curled residual film in the curled residual film restoration image, the predicted value of the pixel area of ​​the curled residual film of the farmland to be tested after being unfolded is determined. This application realizes the repair of occluded residual film through multi-classification semantic segmentation and image restoration methods, and realizes the prediction of the pixel area of ​​the curled residual film after being unfolded through machine learning, thereby realizing the repair of occluded residual film on the farmland surface and improving the prediction accuracy of the unfolded area of ​​curled residual film. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A schematic diagram of a process for repairing residual film covering the farmland surface and predicting the unfolding area of ​​curled residual film provided in an embodiment of the present application;

[0044] Figure 2 This is a schematic diagram of an image of a farmland to be tested when the curled residual film is blocked by an obstruction;

[0045] Figure 3This is a schematic diagram of the image of the curled residual film of the sample farmland being blocked by an obstruction;

[0046] Figure 4 Manually annotated schematic diagrams;

[0047] Figure 5 The following is a diagram of the visualization results of the annotated image.

[0048] Figure 6 To improve the U-Net model structure diagram;

[0049] Figure 7 It is a schematic diagram of the structure of the multi-scale feature extraction module;

[0050] Figure 8 Schematic diagram of annotated images obtained by the semantic segmentation model;

[0051] Fig. 9 is a schematic diagram of an image to be repaired;

[0052] Fig.10 This is a schematic diagram of the image repair of the curled residual film;

[0053] Fig.11 This is a schematic diagram of the curled residual film of the sample;

[0054] Fig.12 A schematic diagram of the pixel area of ​​the curled residual film in the curled residual film repair image;

[0055] Fig.13 This is a schematic diagram of the image of the curled residual film unfolding state;

[0056] Fig.14 Schematic diagram of the pixel area after the curled residual film is unfolded. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] The purpose of this application is to provide a method and system for repairing residual film obstruction on the surface of farmland and predicting the expansion area of ​​curled residual film, aiming to achieve repair of residual film obstruction on the surface of farmland and improve the prediction accuracy of the expansion area of ​​curled residual film.

[0059] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0060] In an exemplary embodiment, Figure 1 As shown, a method for repairing residual film on the surface of farmland and predicting the unfolding area of ​​curled residual film is provided, including:

[0061] Step 1: Acquire a target image; the target image is an image of the farmland to be tested when the curled residual film in the farmland to be tested is blocked by an obstruction.

[0062] Specifically, the collected image of the farmland to be tested when the curled residual film is blocked by an obstruction is as follows: Figure 2 shown.

[0063] Step 2: Input the target image into the semantic segmentation model to obtain an annotated image of the farmland to be tested.

[0064] The annotations include: soil background, residual film and occlusions; the semantic segmentation model is obtained by training the improved U-Net model using the first training set.

[0065] As an optional implementation, the process of determining the semantic segmentation model includes:

[0066] Step 21: Obtain a first training set; the first training set includes: images of curled residual film of multiple sample farmlands when they are blocked by obstructions and corresponding annotated images.

[0067] Specifically, a residual film sample collection area (i.e., multiple sample farmlands) was set up in a 1m×1m sample box, and a digital camera was used to collect images of the curled residual film of multiple sample farmlands when they were blocked by obstructions such as straw, drip irrigation tape, cotton, and boll shells. The image of the curled residual film of one sample farmland when it was blocked by an obstruction is shown in the figure below. Figure 3 As shown, using Figure 4 After manual annotation, we get Figure 5 Visualization results of the corresponding annotated images are shown.

[0068] Step 22: Construct an improved U-Net model; the improved U-Net model includes: an input module, multiple multi-scale feature extraction modules, multiple pooling modules, multiple convolution modules, multiple upsampling modules and an output module; the multi-scale feature extraction module includes a main branch of 3×3 standard convolution, an auxiliary branch of 1×1 convolution and an auxiliary branch of 5×5 convolution.

[0069] Specifically, Figure 6As shown in the figure, the improved U-Net model adopts the classic semantic segmentation network U-Net network framework, and replaces the 3×3 standard convolution in the downsampling process with a multi-scale feature extraction module to extract the multi-scale information of the residual film and its occluders. At the same time, in order to further speed up the operation and reduce the amount of model calculation, compared with the original U-Net network, the improved U-Net model deletes one convolution layer in the downsampling and upsampling processes, making the model more lightweight.

[0070] like Figure 7 As shown in the figure, the main branch of the multi-scale feature extraction module is 3×3 standard convolution, and 1×1 convolution and 5×5 convolution are added as auxiliary branches, so that while extracting 3×3 size scale image features, 1×1 and 5×5 size scale image features can also be extracted, enhancing the image feature extraction capability, improving the model's utilization of image information, broadening the nonlinear layer, and enhancing the nonlinear expression capability. Finally, the output results of the three branches, 1×1 convolution, 3×3 standard convolution, and 5×5 convolution, are fused by using 1×1 convolution.

[0071] Step 23: Using the image of the curled residual film of each sample farmland when it is blocked by an obstruction as input and the corresponding labeled image as output, the improved U-Net model is trained to obtain a semantic segmentation model.

[0072] Specifically, after training, the semantic segmentation model is used to Figure 3 The image of the curled residual film of the sample farmland shown in the figure is blocked by an obstruction. The image with annotations is as follows Figure 8 shown.

[0073] Step 3: Set all pixels marked as soil background in the annotated image of the farmland to be tested to black, and set all pixels marked as occlusions in the annotated image of the farmland to be tested to white, to obtain the image to be repaired.

[0074] Specifically, the RGB values ​​of all pixels marked as soil background in the labeled image of the farmland to be tested are set to (0, 0, 0), so that the corresponding pixels are set to black, and the RGB values ​​of all pixels marked as occluders in the labeled image of the farmland to be tested are set to (256, 256, 256), so that the corresponding pixels are set to white, and the image to be repaired is obtained. Figure 2 The image to be repaired corresponding to the image of the farmland to be tested when the curled residual film is blocked by an obstruction is as shown in FIG. Fig. 9 shown.

[0075] Step 4: Use a fast frequency selective reconstruction algorithm to remove occlusions and perform image restoration on the image to be restored, and obtain a curled residual film restoration image; the curled residual film restoration image is an image of the curled residual film in the tested farmland after restoration.

[0076] As an optional implementation, step 4 includes:

[0077] Step 41: Reconstruct each white pixel in the image to be repaired using a fast frequency selective reconstruction algorithm to obtain an initial repaired image; the initial repaired image includes black pixels and non-black pixels.

[0078] Step 42: Determine the area formed by the non-black pixels in the initial repair image as the curled residual film repair image.

[0079] Specifically, Figure 2 The curled residual film repair image corresponding to the image of the farmland to be tested when the curled residual film is blocked by an obstruction is as shown in Fig.10 shown.

[0080] Step 5: Determine the curl index prediction parameters and pixel area of ​​the curled residual film in the curled residual film repair image; the curl index prediction parameters include: color and texture features.

[0081] Step 6: Input the curling index prediction parameters of the curled residual film of the tested farmland into the curling index prediction model to obtain the predicted value of the curling index of the curled residual film of the tested farmland.

[0082] Among them, the curl index prediction model is obtained by training the machine learning model using the second training set.

[0083] As an optional implementation, the process of determining the curl index prediction model includes:

[0084] Step 61: Obtain a second training set; the second training set includes: curl index prediction parameters and actual measured values ​​of curl index of multiple sample curled residual films.

[0085] Step 62: Build a machine learning model.

[0086] Step 63: Taking the curl index prediction parameters of the curled residual films of each sample as input and the corresponding measured value of the curl index as output, the machine learning model is trained to obtain a curl index prediction model.

[0087] Step 7: Determine the predicted value of the pixel area of ​​the curled residual film after the curled residual film of the tested farmland is unfolded according to the predicted value of the curled residual film index of the tested farmland and the pixel area of ​​the curled residual film in the curled residual film repair image.

[0088] As an optional implementation, step 7 includes:

[0089] The curling index calculation formula is used to calculate the predicted value of the pixel area of ​​the curled residual film after being unfolded in the tested farmland according to the predicted value of the curling index of the curled residual film in the tested farmland and the pixel area of ​​the curled residual film in the curled residual film repair image; the curling index calculation formula is:

[0090]

[0091] Among them, RFCI is the curling index; S1 is the pixel area of ​​the curled residual film; S2 is the pixel area after the curled residual film is unfolded.

[0092] As an optional implementation, the curl index calculation formula is obtained by fitting the measured values ​​of the pixel areas of multiple sample curled residual films, the measured values ​​of the pixel areas after being unfolded, and the measured values ​​of the curl index.

[0093] Specifically, when fitting the curl index calculation formula, one of the images of the curled residual film of the sample is as follows: Fig.11 As shown, Fig.11 Perform semantic segmentation model to obtain the corresponding annotated image. Fig.11 In the corresponding annotated image, all pixels marked as soil background are set to black. Fig.11 In the corresponding annotated image, all pixels marked as occluders are set to white, and we get Fig.11 The corresponding image to be repaired is reconstructed using a fast frequency selective reconstruction algorithm. Fig.11 The corresponding image to be repaired is subjected to occlusion removal and image repair to obtain Fig.11 The corresponding curled residual film repair image is used to determine Fig.12 shown Fig.11 The pixel area of ​​the curled residual film in the corresponding curled residual film repair image. Fig.12 The pixel area in is 11976Pixel (i.e. 11976 pixels). Fig.11 After the curled residual film of the sample is cleaned and unfolded, the image of the residual film unfolding state is as follows Fig.13 As shown, the Otsu threshold segmentation algorithm is used to segment the residual film expansion state image to determine Fig.14 The pixel area of ​​the sample curled residual film after being unfolded is shown. Fig.14 The pixel area is 30226Pixel (i.e. 30226 pixels).

[0094] In an exemplary embodiment, a system for repairing residual film on the surface of a farmland and predicting the area of ​​a curled residual film is provided to implement any of the above methods for repairing residual film on the surface of a farmland and predicting the area of ​​a curled residual film. The system for repairing residual film on the surface of a farmland and predicting the area of ​​a curled residual film includes:

[0095] The target image acquisition module is used to obtain a target image; the target image is an image of the farmland to be tested when the curled residual film in the farmland to be tested is blocked by an obstruction.

[0096] The semantic segmentation module is used to input the target image into the semantic segmentation model to obtain an annotated image of the farmland to be tested; the annotations include: soil background, residual film and occlusions; the semantic segmentation model is obtained by training the improved U-Net model using the first training set.

[0097] The module for determining the image to be repaired is used to set all pixels marked as soil background in the annotated image of the farmland to be tested to black, and set all pixels marked as occlusions in the annotated image of the farmland to be tested to white, so as to obtain the image to be repaired.

[0098] The residual film repair module is used to remove occlusions and perform image repair on the image to be repaired using a fast frequency selective reconstruction algorithm to obtain a curled residual film repair image; the curled residual film repair image is an image of the curled residual film in the farmland to be tested after repair.

[0099] The parameter acquisition module is used to determine the curling index prediction parameters and pixel area of ​​the curling residual film in the curling residual film repair image; the curling index prediction parameters include: color and texture features.

[0100] The curling index prediction module is used to input the curling index prediction parameters of the curled residual film of the farmland to be tested into the curling index prediction model to obtain the predicted value of the curling index of the curled residual film of the farmland to be tested; the curling index prediction model is obtained by training the machine learning model using the second training set.

[0101] The unfolding area prediction module is used to determine the predicted value of the pixel area of ​​the curled residual film of the farmland to be tested after being unfolded according to the predicted value of the curling index of the curled residual film of the farmland to be tested and the pixel area of ​​the curled residual film in the curled residual film repair image.

[0102] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0103] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for repairing residual film on farmland surface and predicting the unfolding area of ​​curled residual film, characterized in that: The method for repairing the residual film covering the farmland surface and predicting the unfolding area of ​​the curled residual film comprises: Acquire a target image; the target image is an image of the farmland to be tested when the curled residual film in the farmland to be tested is blocked by an obstruction; The target image is input into a semantic segmentation model to obtain an image of the farmland to be tested with annotations; the annotations include: soil background, residual film and occluders; the semantic segmentation model is obtained by training an improved U-Net model using the first training set; Each pixel marked as soil background in the labeled image of the farmland to be tested is set to black, and each pixel marked as occlusion in the labeled image of the farmland to be tested is set to white, to obtain an image to be repaired; Using a fast frequency selective reconstruction algorithm, the occluder is removed and the image is repaired to obtain a curled residual film repair image; the curled residual film repair image is an image of the curled residual film in the tested farmland after repair; Determine the curl index prediction parameters and pixel area of ​​the curled residual film in the curled residual film repair image; the curl index prediction parameters include: color and texture features; Inputting the curling index prediction parameters of the curled residual film of the farmland to be tested into the curling index prediction model to obtain the predicted value of the curling index of the curled residual film of the farmland to be tested; the curling index prediction model is obtained by training the machine learning model using the second training set; According to the predicted value of the curling index of the curled residual film of the tested farmland and the pixel area of ​​the curled residual film in the curled residual film repair image, the predicted value of the pixel area of ​​the curled residual film of the tested farmland after being unfolded is determined.

2. The method for repairing residual film on the farmland surface and predicting the unfolding area of ​​curled residual film according to claim 1 is characterized in that: The process of determining the semantic segmentation model includes: Acquire a first training set; the first training set includes: images of a plurality of sample farmlands with curled residual films blocked by obstructions and corresponding annotated images; Constructing the improved U-Net model; the improved U-Net model includes: an input module, multiple multi-scale feature extraction modules, multiple pooling modules, multiple convolution modules, multiple upsampling modules and an output module; the multi-scale feature extraction module includes a main branch of 3×3 standard convolution, an auxiliary branch of 1×1 convolution and an auxiliary branch of 5×5 convolution; The improved U-Net model is trained with images of curled residual film of each sample farmland when it is blocked by an obstruction as input and corresponding labeled images as output to obtain the semantic segmentation model.

3. The method for repairing residual film on the farmland surface and predicting the unfolding area of ​​curled residual film according to claim 1 is characterized in that: Using a fast frequency selective reconstruction algorithm, the image to be repaired is subjected to occlusion removal and image repair to obtain a warped residual film repair image, including: Reconstructing each white pixel in the image to be repaired by using a fast frequency selective reconstruction algorithm to obtain an initial repaired image; the initial repaired image includes black pixels and non-black pixels; The area formed by the non-black pixels in the initial repair image is determined as the curled residual film repair image.

4. The method for repairing residual film on farmland surface and predicting the unfolding area of ​​curled residual film according to claim 1, characterized in that: The determination process of the curl index prediction model includes: Acquire a second training set; the second training set includes: curl index prediction parameters and actual values ​​of curl index of a plurality of sample curled residual films; Building the machine learning model; The curl index prediction parameters of the curled residual films of each sample are used as input, and the corresponding measured values ​​of the curl index are used as output, and the machine learning model is trained to obtain the curl index prediction model.

5. The method for repairing residual film on the farmland surface and predicting the unfolding area of ​​curled residual film according to claim 1, characterized in that: According to the predicted value of the curling index of the curled residual film of the tested farmland and the pixel area of ​​the curled residual film in the curled residual film repair image, the predicted value of the pixel area of ​​the curled residual film of the tested farmland after being unfolded is determined, including: The curling index calculation formula is used to calculate the predicted value of the pixel area of ​​the curled residual film after being unfolded in the tested farmland according to the predicted value of the curling index of the curled residual film in the tested farmland and the pixel area of ​​the curled residual film in the curled residual film repair image; the curling index calculation formula is: Among them, RFCI is the curling index; S1 is the pixel area of ​​the curled residual film; S2 is the pixel area after the curled residual film is unfolded.

6. The method for repairing residual film on the farmland surface and predicting the unfolding area of ​​curled residual film according to claim 5 is characterized in that: The curl index calculation formula is obtained by fitting the measured values ​​of the pixel areas of a plurality of sample curled residual films, the measured values ​​of the pixel areas after being unfolded, and the measured values ​​of the curl index.

7. A system for repairing residual film on the surface of farmland and predicting the area of ​​curled residual film, to implement the method for repairing residual film on the surface of farmland and predicting the area of ​​curled residual film as described in any one of claims 1 to 6, characterized in that: The farmland surface residual film blocking repair and curled residual film expansion area prediction system includes: A target image acquisition module is used to acquire a target image; the target image is an image of the farmland to be tested when the curled residual film in the farmland to be tested is blocked by an obstruction; A semantic segmentation module is used to input the target image into a semantic segmentation model to obtain an image with annotations of the farmland to be tested; the annotations include: soil background, residual film and occluders; the semantic segmentation model is obtained by training the improved U-Net model using the first training set; The module for determining the image to be repaired is used to set all pixels marked as soil background in the marked image of the farmland to be tested to black, and to set all pixels marked as occlusion in the marked image of the farmland to be tested to white, so as to obtain the image to be repaired; A residual film repair module is used to remove occluders and perform image repair on the image to be repaired using a fast frequency selective reconstruction algorithm to obtain a curled residual film repair image; the curled residual film repair image is an image of the curled residual film in the farmland to be tested after repair; A parameter acquisition module is used to determine the curl index prediction parameters and pixel area of ​​the curled residual film in the curled residual film repair image; the curl index prediction parameters include: color and texture features; A curling index prediction module, used for inputting the curling index prediction parameters of the curled residual film of the farmland to be tested into the curling index prediction model to obtain the predicted value of the curling index of the curled residual film of the farmland to be tested; the curling index prediction model is obtained by training the machine learning model using the second training set; The unfolding area prediction module is used to determine the predicted value of the pixel area of ​​the curled residual film of the farmland to be tested after being unfolded according to the predicted value of the curling index of the curled residual film of the farmland to be tested and the pixel area of ​​the curled residual film in the curled residual film repair image.

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