Intelligent statistical method and system for sugarcane germination rate, electronic equipment and storage medium

Abstract

The invention discloses an intelligent statistical method and system for the sugarcane germination rate, electronic equipment and a storage medium, and relates to the technical field of sugarcane planting.The method comprises the steps that a target data set is constructed according to original images of multiple sugarcane seedlings; pixel distribution characteristics of the sugarcane seedlings are extracted from the target data set, the extracted pixel distribution characteristics are classified, and a germination state classification result of the sugarcane seedlings is obtained; judging whether the germination state classification result meets a preset condition or not to obtain a judgment result; and when the judgment result is yes, obtaining a final germination rate statistical result of the sugarcane seedlings according to the target data set. Through the image recognition and data analysis technology, the germination rate of the sugarcane seedlings can be automatically and accurately recognized and counted, errors caused by human factors are reduced, the reliability of statistical results is improved, the working efficiency is greatly improved, and the method is suitable for large-scale sugarcane planting scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of sugarcane planting, and in particular to an intelligent statistical method, system, electronic equipment and storage medium for sugarcane germination rate. Background Art

[0002] Accurately measuring the germination rate of sugarcane seedlings is a key issue during sugarcane cultivation. Traditional statistical methods rely primarily on manual observation and recording, and different personnel may use different judgment criteria, resulting in highly subjective statistical results that lack consistency and accuracy. Furthermore, manual statistics are time-consuming and labor-intensive, especially in large-scale plantings. Checking the germination status of each seedling individually is a massive undertaking, making it difficult to quickly obtain accurate data. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to address the deficiencies of the existing technology and specifically provide a method, system, electronic device and storage medium for intelligent statistical analysis of sugarcane germination rate, as follows:

[0004] 1) In a first aspect, the present invention provides an intelligent statistical method for sugarcane germination rate, the specific technical solution of which is as follows:

[0005] S1. Construct a target dataset based on multiple original images of sugarcane seedlings;

[0006] S2. Extracting pixel distribution features of the sugarcane seedlings from the target data set, classifying the extracted pixel distribution features, and obtaining classification results of the germination status of the sugarcane seedlings;

[0007] S3, judging whether the germination status classification result meets the preset conditions, and obtaining the judgment result;

[0008] S4. When the judgment result is yes, obtain the final germination rate statistical result of the sugarcane seedlings according to the target data set.

[0009] The beneficial effects of the intelligent statistical method for sugarcane germination rate provided by the present invention are as follows:

[0010] Through image recognition and data analysis technology, the present invention can automatically and accurately identify and count the germination rate of sugarcane seedlings, reduce errors caused by human factors, improve the reliability of statistical results, greatly improve work efficiency, and is suitable for large-scale sugarcane planting scenarios.

[0011] Based on the above solution, the intelligent statistical method for sugarcane germination rate of the present invention can be further improved as follows.

[0012] Furthermore, the extracted pixel distribution features are classified, including:

[0013] The extracted pixel distribution features are classified using support vector machine with radial basis function.

[0014] Furthermore, based on the original images of multiple sugarcane seedlings, a target dataset is constructed, including:

[0015] Collect raw images of multiple sugarcane seedlings according to a preset period, and synchronize the temperature and humidity data collected by the temperature sensor and humidity sensor. The raw images, temperature and humidity data are matched according to the acquisition timestamps to generate a synchronized data set.

[0016] Build the target dataset based on the synchronized dataset.

[0017] Furthermore, it also includes:

[0018] When the judgment result is no, the acquisition frequency of the original image of the sugarcane seedlings is adjusted, and the original images of the plurality of sugarcane seedlings are reacquired according to the adjusted acquisition frequency, and the process returns to S1 until the judgment result is yes.

[0019] 2) In a second aspect, the present invention further provides an intelligent statistical system for sugarcane germination rate, the specific technical solution of which is as follows:

[0020] It includes target data set construction module, germination status classification result acquisition module, judgment module and statistics module;

[0021] The target dataset construction module is used to: construct a target dataset based on multiple original images of sugarcane seedlings;

[0022] The germination status classification result acquisition module is used to: extract the pixel distribution features of the sugarcane seedlings from the target data set, classify the extracted pixel distribution features, and obtain the germination status classification results of the sugarcane seedlings;

[0023] The judgment module is used to: judge whether the germination status classification result meets the preset conditions and obtain the judgment result;

[0024] The statistical module is used to: when the judgment result is yes, obtain the final germination rate statistical results of the sugarcane seedlings according to the target data set.

[0025] Based on the above solution, the intelligent statistical system for sugarcane germination rate of the present invention can be further improved as follows.

[0026] Furthermore, the germination status classification result acquisition module is further specifically used for:

[0027] The extracted pixel distribution features are classified using support vector machine with radial basis function.

[0028] Furthermore, the target dataset construction module is specifically used to:

[0029] Collect raw images of multiple sugarcane seedlings according to a preset period, and synchronize the temperature and humidity data collected by the temperature sensor and humidity sensor. The raw images, temperature and humidity data are matched according to the acquisition timestamps to generate a synchronized data set.

[0030] Build the target dataset based on the synchronized dataset.

[0031] Furthermore, the method further includes calling an execution module, which is used to:

[0032] When the judgment result is no, the acquisition frequency of the original images of the sugarcane seedlings is adjusted, and multiple original images of the sugarcane seedlings are reacquired according to the adjusted acquisition frequency, and the target dataset construction module, germination status classification result acquisition module and judgment module are re-called until the judgment result is yes.

[0033] 3) In a third aspect, the present invention further provides an electronic device, comprising a processor coupled to a memory, wherein the memory stores at least one computer program, and the at least one computer program is loaded and executed by the processor, so that the electronic device implements any of the above-mentioned intelligent statistical methods for sugarcane germination rate.

[0034] 4) In a fourth aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-mentioned intelligent statistical methods for sugarcane germination rate.

[0035] It should be noted that the beneficial effects achieved by the technical solutions of the second to fourth aspects of the present invention and the corresponding possible implementation methods can be found in the above-mentioned technical effects of the first aspect and its corresponding possible implementation methods, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments of the present invention:

[0037] Figure 1 1 is a flow chart of an intelligent statistical method for sugarcane germination rate according to an embodiment of the present invention;

[0038] Figure 2 This is a schematic structural diagram of an intelligent statistical system for sugarcane germination rate according to an embodiment of the present invention;

[0039] Figure 3 The figure is a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0041] The following describes in detail the technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems using specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following embodiments of the present invention are described in conjunction with the accompanying drawings.

[0042] like Figure 1 As shown, an intelligent statistical method for sugarcane germination rate according to an embodiment of the present invention includes the following steps:

[0043] S1. Construct a target dataset based on multiple original images of sugarcane seedlings;

[0044] S2. Extracting pixel distribution features of the sugarcane seedlings from the target data set, classifying the extracted pixel distribution features, and obtaining a classification result of the germination status of the sugarcane seedlings;

[0045] S3, judging whether the germination status classification result meets the preset conditions, and obtaining a judgment result;

[0046] S4. When the judgment result is yes, obtaining the final germination rate statistical result of the sugarcane seedlings according to the target data set.

[0047] Optionally, the extracted pixel distribution features are classified, including:

[0048] The extracted pixel distribution features are classified using support vector machine with radial basis function.

[0049] Optionally, a target dataset is constructed based on a plurality of original images of sugarcane seedlings, including:

[0050] Collect raw images of multiple sugarcane seedlings according to a preset period, and synchronize the temperature and humidity data collected by the temperature sensor and humidity sensor. The raw images, temperature and humidity data are matched according to the acquisition timestamps to generate a synchronized data set.

[0051] Build the target dataset based on the synchronized dataset.

[0052] Optionally, it also includes:

[0053] When the judgment result is no, the acquisition frequency of the original image of the sugarcane seedlings is adjusted, and the original images of the plurality of sugarcane seedlings are reacquired according to the adjusted acquisition frequency, and the process returns to S1 until the judgment result is yes.

[0054] The following example illustrates an intelligent statistical method for sugarcane germination rate according to the present invention, which specifically includes the following steps:

[0055] S101. Collect original images of a plurality of sugarcane seedlings at a preset frequency, and enable a temperature sensor and a humidity sensor to synchronously collect temperature data and humidity data, and match the original images, temperature data, and humidity data according to acquisition timestamps to generate a synchronized data set.

[0056] S102. Extract pixel information of each original image of the sugarcane seedlings from the synchronized data set, and use the boundary gradient method to calculate and extract the edge strength of any original image, and use the Canny operator to generate an initial edge feature image corresponding to the original image based on the edge strength of the original image, and perform Gaussian filtering on the initial edge feature image to eliminate noise interference. If there is a blurred area in the initial edge feature image, use a non-local mean denoising algorithm to perform a sharpening process to obtain a final edge feature image corresponding to the original image, until a final edge feature image corresponding to each original image is obtained.

[0057] The specific implementation process of using the boundary gradient method to calculate and extract the edge strength of any original image is as follows:

[0058] The original image is converted into a grayscale image using the weighted average method. The grayscale image is smoothed using the Gaussian filter method or the median filter to reduce the influence of noise on edge detection. The gradient of the grayscale image in the x and y directions is calculated using the Sobel operator or the Prewitt operator to determine the direction and component of the edge. For each pixel in the image, the gradient operator is used to calculate the gradient components in the x and y directions. Then, the gradient component is calculated using the formula Gradient = sqrt(dx 2 +dy2) calculates the gradient magnitude, where dx and dy are the gradient components in the x and y directions respectively, and Gradient represents the edge strength.

[0059] S103. According to the spatial correlation matrix of any final edge feature image, the dependency relationship between each pixel of the final edge feature image and the surrounding pixels is obtained, and the self-attention weight corresponding to the final edge feature image is generated; the self-attention weight is multiplied by the pixel value of each pixel in the edge feature image to obtain an attention mask; the attention mask is normalized to generate a two-dimensional weight map; the two-dimensional weight map is weightedly fused with the edge feature image to obtain an initial weighted feature image corresponding to the final edge feature image. If the self-attention weight of any area in the initial weighted feature image is lower than the preset weight threshold, the non-local mean algorithm is used to perform local feature enhancement on the area to obtain the enhanced weighted feature image, which is recorded as the final weighted feature image, until the final weighted feature image corresponding to each final edge feature image is obtained.

[0060] The process of obtaining the spatial correlation matrix of any final edge feature image includes:

[0061] The grayscale values ​​of the edge feature image are quantized to a range of 0 to N_g-1, where N_g is the number of grayscale levels. The spatial relationship between pixel pairs is determined, usually expressed as a distance d and an angle θ. For example, d can be 1, 2, etc., and θ can be 0°, 45°, 90°, 135°, etc. For each pair of pixels that satisfy the spatial relationship, the number of occurrences of the grayscale value combination is counted and normalized to obtain a probability matrix P(i, j, d, θ), where i and j are grayscale levels. The spatial correlation matrix is ​​calculated based on the GLCM (Gray-Level Co-occurrence Matrix). Each element in the spatial correlation matrix represents the correlation (dependency) between pixels of different grayscale levels in the image under a specific spatial relationship.

[0062] The process of generating the self-attention weight corresponding to the final edge feature image includes:

[0063] The spatial correlation matrix is ​​used as the input feature matrix X, where each element represents the correlation (dependency) between pixels at different positions in the image. The input feature matrix X is converted into a query vector, a key vector, and a value vector through the learnable weight matrices WQ, WK, and WV. The query vector is: Q = X·WQ, the key vector is: K = X·WK, and the value vector is: V = X·WV. The similarity between the query vector and the key vector is calculated by dot product to obtain the self-attention score matrix A. The softmax function is applied to the self-attention score matrix A for normalization to obtain the self-attention weight matrix W (i.e., self-attention weight).

[0064] Dilation and erosion operations were performed on each final weighted feature image to extract the initial contours of multiple sugarcane seedling germination areas. The temperature data and humidity data were fused with the initial contours of each germination area using the weighted average method to obtain a multimodal fusion result. Based on the fusion result, the Otsu threshold segmentation algorithm was used to distinguish the germination area from the non-germination area to obtain the germination area recognition result of each final weighted feature image.

[0065] Among them, each final weighted feature image is expanded and eroded to extract the initial contours of multiple sugarcane seedling germination areas. The specific implementation process is as follows:

[0066] Convert the final weighted feature image to a grayscale image. Convert the grayscale image to a binary image for morphological operations. Use a threshold segmentation method to set pixel values ​​greater than a threshold to 1 (foreground) and those less than the threshold to 0 (background). In the dilation operation, select a 3×3 square kernel for the dilation operation and use the convolution kernel to dilate the binary image. The dilation operation expands the foreground area in the image, connects adjacent areas, and fills small holes. In the erosion operation, select a 3×3 square kernel for the erosion operation. Use the convolution kernel to erode the dilated image. The erosion operation shrinks the foreground area in the image and removes small noise points. Then use OpenCV's findContours function to extract the initial contours of multiple sugarcane seedling germination areas in the eroded image.

[0067] Among them, the weighted average method is used to perform multimodal fusion of temperature data and humidity data with the initial contour of each germination area to obtain the fusion result. The specific implementation process is as follows:

[0068] Normalize the temperature and humidity data to ensure that they are within the same numerical range. For example, the temperature and humidity data can be normalized to the interval [0,1], and the contour image of the germination area (the image of the area enclosed by the initial contour of the germination area) can be converted to a format that matches the temperature and humidity data. If the temperature and humidity data are time series data, the time series of the contour images need to be aligned. Assign a weight to each modality based on the importance of temperature and humidity data to sugarcane seedling germination. The weight assignment can be based on prior knowledge or determined through an optimization algorithm to ensure that the sum of all weights is 1 to maintain the rationality of the weighted average. For the contour of each germination area, multiply the temperature and humidity data by the corresponding weights respectively, and add the results to obtain the fusion result, which is a matrix.

[0069] Among them, according to the fusion results, the Otsu threshold segmentation algorithm is used to distinguish the germination area from the non-germination area. The specific implementation process is as follows:

[0070] The fusion results are normalized to ensure that the data range is between 0 and 255 to facilitate subsequent threshold segmentation. The normalized fusion results are converted to image format to form a fused image. The fused image is a grayscale image, where the pixel value represents the intensity of the fused features. The number of pixels at each grayscale level in the fused image is counted to form a grayscale histogram. All possible thresholds (from 0 to 255) are traversed. For each threshold, the inter-class variance of the foreground (pixels greater than the threshold) and background (pixels less than or equal to the threshold) in the grayscale histogram is calculated. The threshold that maximizes the inter-class variance is selected as the optimal segmentation threshold. This threshold classifies the image into two categories: germination areas and non-germination areas.

[0071] The corresponding final weighted feature image is annotated according to the recognition result of each germination area to obtain multiple semantically annotated images.

[0072] S104, traversing pixels in any semantically annotated image to determine whether their labels are consistent with those of neighboring pixels; if any pixel is labeled as a germinative label and there are germinative labels in neighboring pixels of the pixel, marking the pixel as a germinative connected region; recursively detecting all germinative connected regions in the semantically annotated image using a depth-first search algorithm to determine the boundaries of each germinative connected region; and generating an initial germinative boundary image based on the boundaries of the germinative connected regions;

[0073] Obtaining a preset area threshold, calculating the area of ​​each germination connected region in the initial germination boundary image; marking the germination connected region whose area is smaller than the area threshold as a noise region; removing the noise region from the initial germination boundary image to obtain a germination boundary image after noise removal;

[0074] Extract the contour information of the germination area in the germination boundary image after noise removal; determine whether there is a break in the contour information; if there is a break in the contour information, use the morphological closing operation method to repair the contour information to obtain a repaired germination area contour image; calculate the area of ​​the germination area in the repaired germination area contour image, and compare the calculated area with a preset area difference threshold; if the difference between the calculated area and the preset area difference threshold is greater than a preset adjustment threshold, adjust the area difference threshold according to the difference, and use the adjusted area difference threshold to re-label and detect the germination area to generate a final germination boundary image corresponding to the semantically labeled image, until a final germination boundary image corresponding to each semantically labeled image is obtained.

[0075] S105. Obtain any final germination boundary image and the corresponding temperature and humidity sensor data, and perform normalization processing on the final germination boundary image according to the pre-established temperature and humidity comprehensive influencing factor model to obtain the normalized final germination boundary image. Segment the normalized final germination boundary image, extract the pixel coordinates of the segmentation edge, and use the pixel coordinates of the segmentation edge as the position information of the segmentation edge. Use the least squares method to perform polynomial fitting on the position information of the segmentation edge to obtain a preliminary smooth edge curve. Determine whether there is a curvature mutation point in the preliminary smooth edge curve. If so, use Gaussian filtering to smooth the curvature mutation point to obtain an optimized smooth edge curve. Based on the optimized smooth edge curve, redraw the boundary of the germination area in the final germination boundary image to generate an initial smooth boundary image. Extract the contour in the initial smooth boundary image, and calculate the length and area of ​​the contour to obtain the length-to-area ratio. Determine whether the length-to-area ratio is within a preset range. If not, use the Otsu adaptive threshold to perform secondary optimization on the boundary of the germination area in the final germination boundary image to adjust the distribution of the segmentation edge and regenerate the initial smooth boundary image until the length-to-area ratio is within the preset range. The final smooth boundary image corresponding to the final germination boundary image is obtained, and the final smooth boundary image corresponding to each final germination boundary image is obtained.

[0076] S106. Segment each final germination boundary image according to the final smooth boundary image corresponding to each final germination boundary image to obtain a plurality of germination region images and a plurality of non-germination region images, perform random rotation and scaling processing on each germination region image to obtain a plurality of data-enhanced germination region images, use a bilinear interpolation algorithm to fill in the regions with missing edges or discontinuous pixels in the data-enhanced germination region images to obtain a plurality of complete germination region images, and combine the plurality of complete germination region images and the plurality of non-germination region images to form a target data set.

[0077] S107. Extract pixel distribution features of the sugarcane seedlings from the target data set; classify the extracted pixel distribution features using a radial basis function support vector machine to obtain a germination status classification result of the sugarcane seedlings; determine whether the germination status classification result meets a preset condition to obtain a determination result; and when the determination result is yes, obtain a final germination rate statistical result of the sugarcane seedlings based on the multiple complete germination area images and the multiple non-germination area images.

[0078] The specific implementation process of extracting the pixel distribution features of sugarcane seedlings from the target dataset is as follows:

[0079] The image was segmented using the Otsu threshold segmentation algorithm, with the complete germination region image and multiple non-germination region images divided into foreground (green) and background, respectively. The pixel value distribution of the germination and non-germination regions was statistically analyzed, including the mean, variance, and histogram of the pixel values. The texture features of the image, such as the gray-level co-occurrence matrix (GLCM), can be calculated. Specifically, the pixel distribution features include both pixel value distribution and texture features.

[0080] Among them, the classification results of the germination status of sugarcane seedlings include: the distribution and proportion of green pixels, among which the preset condition is: the proportion of green pixels is greater than 18 or 20%. When the proportion of green pixels is greater than 18 or 20%, it is determined that the preset conditions are met and the judgment result is yes.

[0081] Based on multiple complete germination area images and multiple non-germination area images, the final germination rate statistics of sugarcane seedlings are obtained. The specific calculation process is as follows:

[0082] Germination rate=ratio between the number of complete germination area images and the sum of a plurality of complete germination area images and a plurality of non-germination area images×100%.

[0083] Optionally, the final germination rate statistical results can also be corrected, specifically:

[0084] The absolute value of the deviation between the temperature data and the corresponding preset temperature reference value, as well as the absolute value of the deviation between the humidity data and the corresponding preset humidity reference value, are used as comprehensive temperature and humidity impact factors. Multiple comprehensive temperature and humidity impact factors are then calculated using a weighted average method to normalize each factor. The difference between this normalized value and 1 is used as a correction factor, and the final germination rate statistics are corrected based on this correction factor. For example, if the calculated germination rate is 80%, the corresponding correction factor is 0.9, and the corrected germination rate is 80% × 0.9 = 72%. By taking the comprehensive effects of temperature and humidity into account, the germination status classification results of sugarcane seedlings are rationally corrected to better reflect actual growth conditions.

[0085] The calculated germination rate is analyzed to assess the overall germination status of the sugarcane seedlings and determine whether the germination rate has reached the target. The germination rate statistical results are applied to sugarcane planting decisions, such as adjusting sugarcane planting density and optimizing irrigation and fertilization plans, to improve sugarcane planting efficiency.

[0086] When the judgment result is no, the time interval for acquiring the original images of the sugarcane seedlings is adjusted, and the original images of the plurality of sugarcane seedlings are acquired again according to the adjusted time interval, and the process returns to S101 until the judgment result is yes.

[0087] Among them, a PID control algorithm is used to dynamically adjust the time interval for collecting original images of sugarcane seedlings according to environmental change information, obtain an adjusted time interval, and collect multiple original images of sugarcane seedlings according to the adjusted time interval.

[0088] Environmental change information includes temperature and humidity changes. For example, if the temperature rises from 25°C to 35°C and the humidity drops from 70% to 50%, sugarcane seedling germination may accelerate. The PID algorithm uses proportional, integral, and differential parameters to adjust the data collection interval from once per hour to once every 30 minutes, ensuring timely capture of changes in germination characteristics. This dynamic control effectively adapts to environmental fluctuations and improves the targeted nature of data collection.

[0089] The PID control algorithm is used to dynamically adjust the time interval for collecting the original images of the sugarcane seedlings according to environmental change information. The specific implementation process is as follows:

[0090] Based on temperature and humidity change information and the set target values, the system calculates an error value and uses a PID control algorithm to calculate an output value. Based on this output value, the time interval for capturing raw images of the sugarcane seedlings is dynamically adjusted. For example, if the difference between the output value and the target value exceeds a preset deviation threshold, the acquisition interval is shortened; otherwise, it is extended. The adjusted image acquisition interval is then applied to the actual acquisition process, and temperature and humidity changes are continuously monitored, forming a closed-loop control system that continuously optimizes the image acquisition interval.

[0091] The target value may specifically be a desired germination rate or a desired growth rate, and correspondingly, the output value is the output germination rate or the output growth rate.

[0092] In the above embodiments, although the steps are numbered S1, S2, etc., these are only specific embodiments given by the present invention. Those skilled in the art may adjust the execution order of S1, S2, etc. according to actual conditions, which is also within the scope of protection of the present invention. It can be understood that in some embodiments, some or all of the above embodiments may be included.

[0093] like Figure 2 As shown, an intelligent statistical system 200 for sugarcane germination rate according to an embodiment of the present invention includes a target data set construction module 201, a germination status classification result acquisition module 202, a judgment module 203 and a statistical module 204;

[0094] The target data set construction module 201 is used to: construct a target data set based on a plurality of original images of sugarcane seedlings;

[0095] The germination status classification result acquisition module 202 is used to: extract the pixel distribution features of the sugarcane seedlings from the target data set, classify the extracted pixel distribution features, and obtain the germination status classification results of the sugarcane seedlings;

[0096] The judgment module 203 is used to: judge whether the germination status classification result meets the preset conditions and obtain a judgment result;

[0097] The statistical module 204 is used to obtain the final germination rate statistical results of the sugarcane seedlings according to the target data set when the judgment result is yes.

[0098] Optionally, in the above technical solution, the germination status classification result acquisition module 202 is further specifically used to:

[0099] The extracted pixel distribution features are classified using support vector machine with radial basis function.

[0100] Optionally, in the above technical solution, the target dataset construction module 201 is specifically configured to:

[0101] Collect raw images of multiple sugarcane seedlings according to a preset period, and synchronize the temperature and humidity data collected by the temperature sensor and humidity sensor. The raw images, temperature and humidity data are matched according to the acquisition timestamps to generate a synchronized data set.

[0102] Build the target dataset based on the synchronized dataset.

[0103] Optionally, the above technical solution further includes a calling execution module, which is used to:

[0104] When the judgment result is no, the acquisition frequency of the original images of the sugarcane seedlings is adjusted, and multiple original images of the sugarcane seedlings are reacquired according to the adjusted acquisition frequency. The target dataset construction module 201, the germination status classification result acquisition module 202, and the judgment module 203 are re-called until the judgment result is yes.

[0105] It should be noted that the beneficial effects of the sugarcane germination rate intelligent statistical system 200 provided in the above embodiment are the same as those of the sugarcane germination rate intelligent statistical method described above, and will not be further elaborated here. Furthermore, the above embodiment only illustrates the functional implementation of the system by dividing the functional modules described above. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the system can be divided into different functional modules according to actual circumstances to complete all or part of the functions described above. Furthermore, the system and method embodiments provided in the above embodiment are based on the same concept. The specific implementation process is detailed in the method embodiment and will not be further elaborated here.

[0106] The sugarcane germination rate intelligent statistical system of the present invention may be a computer program (including program code) running on a computer device. For example, the sugarcane germination rate intelligent statistical system of the present invention is an application software that can be used to execute the corresponding steps of the sugarcane germination rate intelligent statistical method of the present invention.

[0107] In some embodiments, the intelligent statistical system for sugarcane germination rate of the present invention can be implemented by combining software and hardware. As an example, the intelligent statistical system for sugarcane germination rate of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the intelligent statistical method for sugarcane germination rate of the present invention. For example, the processor in the form of a hardware decoding processor can be one or more application specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field programmable gate arrays (FPGAs), or other electronic components.

[0108] The modules described in the embodiments of the present invention may be implemented in software or hardware, and the name of a module does not necessarily limit the module itself.

[0109] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the aforementioned intelligent statistical methods for sugarcane germination rates is implemented. In other words, an electronic device according to an embodiment of the present invention may include, but is not limited to, a processor and a memory; the memory is configured to store the computer program; and the processor is configured to execute the intelligent statistical method for sugarcane germination rates according to any of the embodiments of the present invention by invoking the computer program.

[0110] In an alternative embodiment, an electronic device is provided, such as Figure 3 As shown, Figure 3The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data exchange between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the number of transceivers 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0111] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the present disclosure. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0112] Bus 4002 may include a path for transmitting information between the above components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 In the figure, only one thick line is used to represent the bus 4002, but this does not mean that there is only one bus or one type of bus.

[0113] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0114] The memory 4003 is used to store application code (computer program) for executing the solution of the present invention, and is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0115] Among them, the electronic device can also be a terminal device, and the terminal device can be any device that can install applications, including at least one of a smartphone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart car device.

[0116] It should be noted that Figure 3 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0117] A computer-readable storage medium according to an embodiment of the present invention stores a computer program, which, when executed by a processor, implements any of the above-mentioned intelligent statistical methods for sugarcane germination rates.

[0118] Alternatively, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, or the like.

[0119] In an exemplary embodiment, a computer program product or computer program is also provided. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to implement any of the aforementioned intelligent statistical methods for calculating sugarcane germination rates.

[0120] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0121] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0122] The computer-readable storage medium provided in the embodiment of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EEPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or component.

[0123] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0124] The above description is merely a preferred embodiment of the present invention and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present invention.

[0125] It should be noted that the terms "first," "second," and the like in the specification and claims of this application are used to distinguish similar objects and to define a specific order or precedence. Where appropriate, the order used for similar objects may be interchanged, such that the embodiments of the present application described herein can be implemented in an order other than the order shown or described.

[0126] Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Therefore, the present invention may be implemented in the following forms: entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software, generally referred to herein as a "circuit," "module," or "system." Furthermore, in some embodiments, the present invention may be implemented in the form of a computer program product embodied in one or more computer-readable media containing computer-readable program code.

[0127] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. An intelligent statistical method for sugarcane germination rate, characterized in that: include: S1. Construct a target dataset based on multiple original images of sugarcane seedlings; S2. Extracting pixel distribution features of the sugarcane seedlings from the target data set, classifying the extracted pixel distribution features, and obtaining a classification result of the germination status of the sugarcane seedlings; S3, judging whether the germination status classification result meets the preset conditions, and obtaining a judgment result; S4. When the judgment result is yes, obtaining the final germination rate statistical result of the sugarcane seedlings according to the target data set.

2. The intelligent statistical method for sugarcane germination rate according to claim 1, characterized in that: Classify the extracted pixel distribution features, including: The extracted pixel distribution features are classified using support vector machine with radial basis function.

3. The intelligent statistical method for sugarcane germination rate according to claim 1 or 2, characterized in that: Based on multiple original images of sugarcane seedlings, a target dataset is constructed, including: Collect raw images of multiple sugarcane seedlings according to a preset period, and synchronize the temperature and humidity data collected by the temperature sensor and humidity sensor. The raw images, temperature and humidity data are matched according to the acquisition timestamps to generate a synchronized data set. A target dataset is constructed according to the synchronized dataset.

4. The intelligent statistical method for sugarcane germination rate according to claim 1 or 2, characterized in that: Also includes: When the judgment result is no, the acquisition frequency of the original images of the sugarcane seedlings is adjusted, and a plurality of original images of the sugarcane seedlings are reacquired according to the adjusted acquisition frequency, and the process returns to S1 until the judgment result is yes.

5. An intelligent statistical system for sugarcane germination rate, characterized in that: It includes target data set construction module, germination status classification result acquisition module, judgment module and statistics module; The target data set construction module is used to: construct a target data set based on a plurality of original images of sugarcane seedlings; The germination status classification result acquisition module is used to: extract the pixel distribution features of the sugarcane seedlings from the target data set, classify the extracted pixel distribution features, and obtain the germination status classification results of the sugarcane seedlings; The judgment module is used to: judge whether the germination status classification result meets the preset conditions and obtain a judgment result; The statistical module is used to obtain the final germination rate statistical result of the sugarcane seedlings according to the target data set when the judgment result is yes.

6. The sugarcane germination rate intelligent statistical system according to claim 5, characterized in that: The germination status classification result acquisition module is further specifically used for: The extracted pixel distribution features are classified using support vector machine with radial basis function.

7. The intelligent statistical system for sugarcane germination rate according to claim 5 or 6, characterized in that: The target dataset construction module is specifically used to: Collect raw images of multiple sugarcane seedlings according to a preset period, and synchronize the temperature and humidity data collected by the temperature sensor and humidity sensor. The raw images, temperature and humidity data are matched according to the acquisition timestamps to generate a synchronized data set. A target dataset is constructed according to the synchronized dataset.

8. The sugarcane germination rate intelligent statistical system according to claim 5 or 6, characterized in that: It also includes a call execution module, which is used to: When the judgment result is no, the acquisition frequency of the original images of the sugarcane seedlings is adjusted, and multiple original images of the sugarcane seedlings are reacquired according to the adjusted acquisition frequency, and the target dataset construction module, the germination status classification result acquisition module and the judgment module are re-called until the judgment result is yes.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method realizes the intelligent statistical method for sugarcane germination rate according to any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the intelligent statistical method for sugarcane germination rate according to any one of claims 1 to 4 is implemented.

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