Multi-dimensional seeding alert method and system for a planter

Abstract

The application relates to the technical field of seeding monitoring, and particularly discloses a multi-dimensional seeding alarm method and system for a seeding machine, which comprises the following steps: collecting images of each seed hole in the process of seed disc rotation based on an industrial camera device built in a seed sowing device of the seeding machine; performing target identification on the images of each seed hole, locating seed targets and contour features, and determining a seeding state; counting the seeding states at different moments to obtain a state sequence, performing time domain analysis and feature analysis on the state sequence, and correcting the seeding state; counting the corrected seeding states of each seed hole, combining images to construct visual information, and feeding back to a total end of the seeding machine; and the application adopts a camera monitoring and alarm monitor combination device, and a driver only needs to observe a display screen, so that the driver can see seed suction on a seed disc of the seed sowing device through the camera, and the degree of intelligence is extremely high.

Description

Technical Field

[0001] This invention relates to the field of seeding monitoring technology, specifically a multi-dimensional seeding alarm method and system for seeders. Background Technology

[0002] Seeders are essential agricultural equipment used for precise crop sowing, and their seed dispensing quality directly affects crop emergence rate and field uniformity. In actual operation, seeders often use photoelectric, mechanical, or capacitive sensors to monitor seed passage and provide alerts for abnormal conditions such as missed sowing or double sowing based on the collected signals.

[0003] However, existing seeding monitoring methods mostly rely on single sensor signals for judgment. In complex operating environments, they are easily affected by factors such as dust, soil particles, seed coating debris, and vibration, leading to false counts or false alarms, which affects the reliability of seeding quality judgment. At the same time, relying solely on seed passage signals makes it difficult to distinguish specific abnormalities such as no seeds being sucked, double seeds being sucked, and foreign objects blocking the seed, which is not conducive to drivers taking timely and targeted adjustment measures.

[0004] With the development of intelligent agricultural equipment, some seeders have begun to introduce video monitoring methods to improve the accuracy of seeding status identification. However, the video acquisition and recognition process consumes a lot of computing resources. If continuous and high-frequency video analysis is performed on the seeding process, it will significantly increase the system cost and energy consumption, limiting its practicality in agricultural fields.

[0005] Therefore, a low-power comprehensive identification scheme for sowing status is needed to perform simple and efficient multi-dimensional identification of sowing status, thereby improving the reliability and intelligence level of sowing operations. Summary of the Invention

[0006] The purpose of this invention is to provide a multi-dimensional seeding alarm method and system for a seeder, so as to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A multi-dimensional seeding alarm method for a seeder, the method comprising:

[0009] The image is captured by an industrial camera device built into the seed metering device of the seeder during the rotation of the seed tray; the image capture frequency of each seed metering hole is different.

[0010] Target recognition is performed on the images of each seed hole to locate the seed target and its outline features, and to determine the sowing status;

[0011] The sowing status at different times is statistically analyzed to obtain a state sequence. Temporal and feature analyses are performed on the state sequence to correct the sowing status. The feature analysis includes spatiotemporal analysis to obtain instantaneous features and persistent features. Instantaneous features are used to characterize the seed status in the image, while persistent features represent the changes in the seed status over a period of time. When extracting persistent features, the current image is used as the tail image to obtain an image sequence over a period of time, which is used to perform the persistent feature extraction process.

[0012] The corrected sowing status of each seeding hole is statistically analyzed, and the visualization information is constructed by combining the images and fed back to the main end of the seeder. The visualization information includes at least images under different sowing statuses and statistical information on the status of each seeding hole.

[0013] As a further aspect of the present invention: the step of acquiring images of each seed dispensing hole during the rotation of the seed disc based on an industrial camera device built into the seed metering device of the seeder includes:

[0014] Collect the seed passage signal output by the alarm monitor of the seeder; the seed passage signal is used to indicate whether there is an object passing through the seed discharge hole;

[0015] Based on the seed, a shooting command is generated through a signal and sent to an industrial camera device built into the seed metering device of the seeder;

[0016] Images of the seed discharge holes during the rotation of the seed tray are captured using industrial camera equipment, and the captured images are timestamped.

[0017] The system calculates the time points at which the seed passes through the signal in real time to determine the proprietary frequency of the industrial camera equipment; the proprietary frequency is used to periodically acquire images when there is no shooting instruction.

[0018] As a further aspect of the present invention: the step of performing target recognition on the image of each seeding hole, locating the seed target and its contour features, and determining the sowing status includes:

[0019] For any seeding hole, the latest image is read in real time, and the image is preprocessed; the preprocessing includes image cropping, size normalization, and noise suppression;

[0020] In the preprocessed image, the seed region is located, and the seed region is segmented based on the image segmentation algorithm to extract the seed target and its contour features in the seeding hole;

[0021] The sowing status is determined based on the extracted seed targets and their contour features.

[0022] As a further aspect of the present invention: the step of statistically analyzing the sowing state at different times to obtain a state sequence, performing time-domain analysis and feature analysis on the state sequence, and correcting the sowing state includes:

[0023] The sowing status at different times is statistically analyzed to obtain a state sequence;

[0024] A numerical sequence is obtained by performing a numerical transformation on the state sequence;

[0025] Calculate the difference sequence of the numerical sequence, and determine the marked abnormal time based on the difference sequence; the abnormal time refers to the time point corresponding to the element index when the absolute value of the difference reaches a preset threshold.

[0026] The seed targets and their contour features of the image at the abnormal moment are read, and the geometric features are determined based on the seed targets and their contour features as instantaneous features; wherein, the instantaneous features include the number of seeds, the proportion of holes occupied, and the seed spacing;

[0027] Based on a preset time span, an image sequence is determined with the image at the latest moment as the tail image. The image sequence is then subjected to differential recognition to determine the seed change vector, which serves as a persistent feature.

[0028] Based on the extracted instantaneous and continuous features, the current sowing state of the seed discharge hole is determined as the corrected sowing state; the sowing state includes at least the normal seed suction state, the double seed suction state, the no seed suction state, and the foreign object blockage state.

[0029] As a further aspect of the present invention: the step of statistically analyzing the corrected sowing status of each seeding hole, constructing visual information by combining it with images, and feeding it back to the main end of the seeder includes:

[0030] Read the state sequence and its difference sequence;

[0031] The state sequence is segmented according to the difference sequence, and the image display ratio within each time period is determined synchronously.

[0032] Based on the image display ratio, images are randomly extracted at different time periods, and images at abnormal moments are extracted synchronously.

[0033] The state sequence and extracted images are used as visualization information and fed back to the central terminal of the seeder.

[0034] As a further aspect of the present invention, the method further includes:

[0035] Establish a connection channel with the sensors built into the seeder, acquire data from each sensor based on a preset data template, and obtain a real-time matrix;

[0036] Read the seeding status at each time point and construct a real-time matrix to a sample set of seeding status;

[0037] The pre-detection model is trained based on the sample set, and the default frequency of the industrial camera is recursively adjusted according to the error rate of the pre-detection model.

[0038] The present invention also provides a multi-dimensional seeding alarm system for a seeder, the system comprising:

[0039] The image acquisition module is used to acquire images of each seeding hole during the rotation of the seed tray based on an industrial camera device built into the seed metering device of the seeder; wherein, the image acquisition frequency of each seeding hole is different;

[0040] The sowing status determination module is used to perform target recognition on the images of each seeding hole, locate the seed target and its contour features, and determine the sowing status.

[0041] The sowing state correction module is used to statistically analyze the sowing state at different times, obtain a state sequence, perform temporal and feature analysis on the state sequence, and correct the sowing state. The feature analysis includes spatiotemporal analysis to obtain instantaneous features and persistent features. Instantaneous features are used to characterize the seed state in the image, and persistent features represent the changes in the seed state over a period of time. When extracting persistent features, the current image is used as the tail image to obtain an image sequence over a period of time, which is used to perform the persistent feature extraction process.

[0042] The information statistics and feedback module is used to statistically analyze the corrected sowing status of each seeding hole, combine it with images to construct visual information, and feed it back to the main end of the seeder; the visual information includes at least images under different sowing conditions and statistical information on the status of each seeding hole.

[0043] As a further aspect of the present invention: the image acquisition module includes:

[0044] The signal acquisition unit is used to acquire the seed passage signal output by the seeder alarm monitor; the seed passage signal is used to indicate whether there is an object passing through the seed discharge hole;

[0045] The instruction sending unit is used to generate a shooting instruction based on the seed signal and send the shooting instruction to the industrial camera device built into the seed metering device of the seeder;

[0046] The time stamping unit is used to timestamp the images captured by industrial camera equipment in the seed dispensing holes during the rotation of the seed tray.

[0047] The proprietary frequency determination unit is used to count the time points when the seed passes through the signal in real time and determine the proprietary frequency of the industrial camera equipment; the proprietary frequency is used to acquire images at regular intervals when there is no shooting instruction.

[0048] As a further aspect of the present invention: the sowing status determination module includes:

[0049] The preprocessing unit is used to read the latest image from any seeding hole in real time and preprocess the image; the preprocessing includes image cropping, size normalization and noise suppression;

[0050] The region segmentation unit is used to locate the seed region in the preprocessed image, segment the seed region based on the image segmentation algorithm, and extract the seed target and its contour features in the seeding hole.

[0051] The determination execution unit is used to determine the sowing status based on the extracted seed target and its contour features.

[0052] As a further aspect of the present invention: the sowing state correction module includes:

[0053] The state statistics unit is used to count the sowing status at different times to obtain a state sequence;

[0054] The state transition unit is used to perform numerical transformation on the state sequence to obtain a numerical sequence;

[0055] An abnormal moment marking unit is used to calculate the difference sequence of a numerical sequence and determine the marking of abnormal moments based on the difference sequence; the abnormal moment refers to the moment point corresponding to the element index when the absolute value of the difference reaches a preset threshold.

[0056] The instantaneous feature determination unit is used to read the seed targets and their contour features of the image at an abnormal moment, and determine the geometric features based on the seed targets and their contour features as instantaneous features; wherein, the instantaneous features include the number of seeds, the proportion of holes occupied, and the seed spacing;

[0057] The persistent feature determination unit is used to determine an image sequence with the latest image as the tail image based on a preset time span, perform differential recognition on the image sequence, and determine the seed change vector as a persistent feature;

[0058] The correction execution unit is used to determine the current sowing state of the seed dispensing hole based on the extracted instantaneous and continuous features, and use this as the corrected sowing state; the sowing state includes at least the normal seed suction state, the double seed suction state, the no seed suction state, and the foreign object blockage state.

[0059] Compared with existing technologies, the advantages of this invention are as follows: This invention uses a combination of camera monitoring and alarm monitoring. The driver only needs to observe the display screen to see the seed suction status on the seed tray of the seed metering device through the camera. For example, whether double seeds are being sucked, whether no seeds are being sucked, or whether foreign objects are blocking the small holes of the seed tray. Even if there is dust inside the seed guide tube during operation, causing the alarm monitoring device to overcount the number of seeds, farmers can distinguish between false alarms and normal counting by observing the display screen. This greatly reduces the yield loss for farmers and also solves farmers' doubts about the inaccuracy of the alarm monitoring device. In addition, the entire process has very low requirements for the camera and recognition algorithm. It adopts a gradient recognition architecture, which has high accuracy while consuming very few resources. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention.

[0061] Figure 1 This is a flowchart of a multi-dimensional seeding alarm method for seeders.

[0062] Figure 2 This is a structural diagram of a multi-dimensional seeding alarm system used in seeders. Detailed Implementation

[0063] To make the technical problems to be solved, the technical solutions, and the beneficial effects of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present invention and are not intended to limit the present invention.

[0064] Figure 1 This is a flowchart of a multi-dimensional seeding alarm method for a seeder. In this embodiment of the invention, a multi-dimensional seeding alarm method for a seeder includes:

[0065] Step S100: Images of each seeding hole during the rotation of the seed tray are captured using an industrial camera device built into the seed metering device of the seeder; wherein the image acquisition frequency of each seeding hole is different;

[0066] The seeder's seed metering device is equipped with an industrial camera to acquire images. The images are acquired at various seed metering holes as the seed disc rotates. The image acquisition frequency of the industrial camera is different for different seed metering holes. This means that the industrial camera needs to record time stamps when acquiring images; otherwise, the time information of the images cannot be known when the frequency is variable.

[0067] Step S200: Perform target recognition on the image of each seeding hole, locate the seed target and its outline features, and determine the sowing status;

[0068] For the images of each seeding hole, the images are identified, and the seeds (image contours) in the images are located. Contour features are synchronized, and the location results and contour features are analyzed to determine the sowing status. It is worth mentioning that locating seed targets and contour recognition are actually similar image processing processes. If the contour features are extracted, it is also a feasible technical solution to determine whether it is a seed. The difference is that the location process may be a low-precision bounding box process. That is, the object in the image is first bounded, called the seed target, and then the contour of the seed target is recognized to determine the sowing status.

[0069] Step S300: Statistically analyze the sowing status at different times to obtain a status sequence, perform temporal and feature analysis on the status sequence, and correct the sowing status; the feature analysis includes spatiotemporal analysis to obtain instantaneous features and persistent features. Instantaneous features are used to characterize the seed status in the image, and persistent features represent the changes in the seed status over a period of time. When extracting persistent features, the current image is used as the tail image to obtain an image sequence over a period of time, which is used to perform the persistent feature extraction process.

[0070] The image itself contains a time stamp. Image recognition yields the sowing status at the corresponding time. Statistical analysis of the sowing status at different times creates a state sequence, representing the seed passage through a sowing hole at different times. However, due to potential issues in the recognition process itself, temporal and feature analysis of the state sequence is necessary to correct the sowing status. This temporal and feature analysis process means that the accuracy of sowing status recognition does not need to be excessively high. In reality, seeds pass through the seed discharge holes at a very high speed. If only sowing status recognition is used, extremely high precision and computing power are required to accurately identify clear images in a very short time. This is clearly unsuitable for a seeder. The seeder's recognition process is merely an auxiliary function; what is needed is... Minimal resources are required; the introduction of temporal and feature analysis processes can significantly reduce the accuracy requirements for sowing status recognition. Even with a high error rate (in current technologies, image recognition accuracy is already high, such as over 90%. Reducing accuracy from 99% to 90% is a huge reduction; in other words, increasing accuracy from 90% to 99% requires significantly higher costs), the subsequent feature analysis process can correct the error. This feature analysis process is essentially a data sequence analysis, requiring only numerical processing and consuming minimal computational power. Under this architecture, the entire visual recognition process for sowing requires minimal resources and places very low demands on the performance of industrial cameras and processors.

[0071] Step S400: Statistically analyze the corrected sowing status of each seeding hole, construct visualization information by combining the image, and feed it back to the main end of the seeder; wherein, the visualization information includes at least images under different sowing statuses and statistical information on the status of each seeding hole;

[0072] The corrected sowing status of each seed hole is the final sowing status. Combined with the acquired images, the sowing status and images are converted into visual information and fed back to the seeder's central terminal, which is generally located in the cab. After being converted into visual information, it can be quickly displayed using the display device in the cab, allowing the operator to understand the sowing situation in real time.

[0073] Regarding step S100, the step of acquiring images of each seed dispensing hole during the rotation of the seed disc based on an industrial camera device built into the seed metering device of the seeder includes:

[0074] Collect the seed passage signal output by the alarm monitor of the seeder; the seed passage signal is used to indicate whether there is an object passing through the seed discharge hole;

[0075] Based on the seed, a shooting command is generated through a signal and sent to an industrial camera device built into the seed metering device of the seeder;

[0076] Images of the seed discharge holes during the rotation of the seed tray are captured using industrial camera equipment, and the captured images are timestamped.

[0077] The system calculates the time points at which the seed passes through the signal in real time to determine the proprietary frequency of the industrial camera equipment; the proprietary frequency is used to periodically acquire images when there is no shooting instruction.

[0078] In one embodiment of the technical solution of this invention, the image acquisition process is specifically described. The process involves acquiring a seed passage signal output by the seeder alarm monitor. This seed passage signal is a trigger signal, for example, determined by real-time measurement of whether there is an obstruction at the entrance using an infrared sensor, thus indicating whether an object is passing through. This seed passage signal is used to characterize whether an object is passing through the seed metering hole. Each time a seed passage signal is detected, a shooting command is generated and sent to an industrial camera device built into the seeder's seed metering unit. The industrial camera device then acquires an image either once or continuously over a period of time (at a preset maximum frequency). This process provides active image acquisition. In the control process, if no seeds pass through, image acquisition will not occur, which may lead to some missed information. Therefore, the above-mentioned method provides a passive real-time acquisition process by setting an independent frequency for the industrial camera equipment. Even if no shooting command is generated, the industrial camera equipment will still acquire images periodically. As for the independent frequency, it can certainly be a fixed frequency, but a fixed frequency may not be high enough in terms of resource utilization. For example, if images are continuously acquired during a long period of inactivity, resources will still be consumed. Therefore, the technical solution of this invention determines the temporal distribution of seed passage by statistically analyzing the time points of seed passage signals in real time, and then determines an independent frequency that fits the actual situation. This is a dynamic frequency that greatly improves resource utilization, allowing more resources to be used during more frequent sowing periods.

[0079] Regarding step S200, the step of performing target recognition on the image of each seeding hole, locating the seed target and its contour features, and determining the sowing status includes:

[0080] For any seeding hole, the latest image is read in real time, and the image is preprocessed; the preprocessing includes image cropping, size normalization, and noise suppression;

[0081] In the preprocessed image, the seed region is located, and the seed region is segmented based on the image segmentation algorithm to extract the seed target and its contour features in the seeding hole;

[0082] The sowing status is determined based on the extracted seed targets and their contour features.

[0083] In one example of the technical solution of this invention, the process of determining the sowing status is specifically described. For any seeding hole, the latest image is read in real time, and the image is preprocessed. The preprocessing includes image cropping, size normalization, and noise suppression, which are conventional pre-image processing operations and will not be described in detail here. The seed region is located in the preprocessed image, and the seed region is segmented based on an image segmentation algorithm. The seed targets and their contour features within the seeding holes are extracted. Extracting the seed targets is a box selection process, and extracting the contour features is a process of recognizing the contour within the box. The sowing status can be determined based on the extracted seed targets and their contour features.

[0084] Regarding step S300, the steps of statistically analyzing the sowing status at different times to obtain a state sequence, performing time-domain analysis and feature analysis on the state sequence, and correcting the sowing status include:

[0085] The sowing status at different times is statistically analyzed to obtain a state sequence;

[0086] A numerical sequence is obtained by performing a numerical transformation on the state sequence;

[0087] Calculate the difference sequence of the numerical sequence, and determine the marked abnormal time based on the difference sequence; the abnormal time refers to the time point corresponding to the element index when the absolute value of the difference reaches a preset threshold.

[0088] The seed targets and their contour features of the image at the abnormal moment are read, and the geometric features are determined based on the seed targets and their contour features as instantaneous features; wherein, the instantaneous features include the number of seeds, the proportion of holes occupied, and the seed spacing;

[0089] Based on a preset time span, an image sequence is determined with the image at the latest moment as the tail image. The image sequence is then subjected to differential recognition to determine the seed change vector, which serves as a persistent feature.

[0090] Based on the extracted instantaneous and continuous features, the current sowing state of the seed discharge hole is determined as the corrected sowing state; the sowing state includes at least the normal seed suction state, the double seed suction state, the no seed suction state, and the foreign object blockage state.

[0091] The above describes the correction process for the sowing state. Sowing states at different times are statistically analyzed to obtain a state sequence. Since sowing states are generally state labels in text format, they are not suitable for numerical processing. Therefore, the state sequence needs to be converted first, transforming each sowing state into a numerical value, thus obtaining a numerical sequence. For the obtained numerical sequence, the difference sequence is calculated. The difference sequence represents the changes in each value in the numerical sequence, which in practice indicates changes in the sowing state. Analyzing the difference sequence can mark abnormal moments. An abnormal moment refers to the time point corresponding to the element index when the absolute value of the difference reaches a preset threshold, i.e., a sudden change in the sowing state. The seed target and its contour features of the image at the abnormal moment are read, and based on the seed target and its contour... The process of determining geometric features, as instantaneous features, is essentially a more precise image recognition process. Instantaneous features are the features of an image at a single moment, hence the name. Further, using the latest image as the tail image, images within a preset time span are read forward to obtain an image sequence. Differential recognition is performed on the image sequence to determine the change vector of each seed contour (the position vector can be determined based on the position of the contour center point, called the change vector). It is a feature over a period of time, hence the name persistent feature. Based on the extracted instantaneous and persistent features, it is determined whether the current sowing state of the seeding hole is accurate. If the two are different, the sowing state determined based on the extracted instantaneous and persistent features is taken as the final sowing state, that is, as the corrected sowing state.

[0092] Regarding step S400, the step of statistically analyzing the corrected sowing status of each seeding hole, constructing visual information by combining it with the image, and feeding it back to the main end of the seeder includes:

[0093] Read the state sequence and its difference sequence;

[0094] The state sequence is segmented according to the difference sequence, and the image display ratio within each time period is determined synchronously.

[0095] Based on the image display ratio, images are randomly extracted at different time periods, and images at abnormal moments are extracted synchronously.

[0096] The state sequence and extracted images are used as visualization information and fed back to the central terminal of the seeder.

[0097] In one example of the technical solution of this invention, the construction and feedback process of visualization information is specifically described. The process involves reading the state sequence and its difference sequence, segmenting the state sequence according to the difference sequence, and using the segmentation method as follows: the sequence number in the difference sequence where the absolute value of the difference is greater than a preset threshold is located and used as the segmentation time point. For two adjacent segmentation time points, if there are enough sequence numbers between them, then they can be considered as a time period. The image display ratio is determined based on the number of sequence numbers in each time period, and the image display ratio is directly proportional to the number of sequence numbers. This means that the longer the duration of a certain state, the more images need to be extracted in that time period. This extraction is for regular images. Simultaneously, for images from abnormal times, these are essential feedback contents that must be extracted and uploaded in a separate process.

[0098] As a preferred embodiment of the technical solution of the present invention, the method further includes:

[0099] Establish a connection channel with the sensors built into the seeder, acquire data from each sensor based on a preset data template, and obtain a real-time matrix;

[0100] Read the seeding status at each time point and construct a real-time matrix to a sample set of seeding status;

[0101] The pre-detection model is trained based on the sample set, and the default frequency of the industrial camera is recursively adjusted according to the error rate of the pre-detection model.

[0102] In one embodiment of the technical solution of this invention, a pre-identification scheme is also introduced. The seeder itself has a large number of sensors, which can be used to acquire operational data, reflecting the working status of the seeder. A connection channel is established with the sensors built into the seeder, and data from each sensor is acquired based on a preset data template to obtain a real-time matrix. The core of the data template is the correspondence between the row and column positions and the sensors. One row and column position corresponds to one sensor. Once determined, the data template is not changed. The correspondence method is not complicated; it can be arranged sequentially according to the sensor number, corresponding to each row and column position. The data template acquires data from each sensor to obtain a matrix for each time moment, called the real-time matrix. Specifically, it typically involves first setting time points, then for each row and column position, querying the data from the sensor at that position at the most recent time, and inserting it into the matrix. Simultaneously, the sowing status at the same time is read, constructing a sample set from the real-time matrix to the sowing status. A pre-detection model is trained based on this sample set, and the inherent frequency of the industrial camera is recursively adjusted according to the error rate of the pre-detection model. This adjustment process is recursive; the pre-detection model is a mapping from the seeder's sensor data to the sowing status. This process does not involve image acquisition and recognition. The process simply involves reading data, inputting the mapping relationship, and performing calculations. This is essentially a one-dimensional processing step, consuming far fewer resources compared to image recognition. Therefore, considering the error rate of the pre-detection model, a higher error rate requires more image acquisition, while a lower error rate requires less. The amount of image acquisition used depends on two main processes: active image capture (detecting the seed signal) and passive image capture based on the image's own frequency. The former is not adjustable, while the latter is. The frequency is directly proportional to the error rate, meaning a higher error rate requires more frequent image acquisition to maintain accuracy. The real-time performance of the verification is enhanced; the smaller the error rate, the more the pre-detection model can be applied, reducing the self-frequency and decreasing the number of times high-resource-demand processes are used within the preset time period. Of course, the priority of this process and the self-frequency adjustment process in step S100 is a concern. Generally, these can be considered as two influencing factors to determine the self-frequency simultaneously. Alternatively, a priority can be set, with the self-frequency adjustment process based on the error rate having a higher priority. In this case, the self-frequency is affected by the error rate. The optimal solution is to pre-set weights to adjust both the self-frequency adjustment process based on the time point and the self-frequency adjustment process based on the error rate.

[0103] Figure 2 This is a structural diagram of a multi-dimensional seeding alarm system for a seeder. In this embodiment of the invention, a multi-dimensional seeding alarm system for a seeder, system 10, includes:

[0104] The image acquisition module 11 is used to acquire images of each seeding hole during the rotation of the seed tray based on an industrial camera device built into the seed metering device of the seeder; wherein, the image acquisition frequency of each seeding hole is different;

[0105] The sowing status determination module 12 is used to perform target recognition on the image of each seeding hole, locate the seed target and its outline features, and determine the sowing status.

[0106] The sowing state correction module 13 is used to statistically analyze the sowing state at different times, obtain a state sequence, perform temporal and feature analysis on the state sequence, and correct the sowing state. The feature analysis includes spatiotemporal analysis to obtain instantaneous features and persistent features. Instantaneous features are used to characterize the seed state in the image, and persistent features represent the changes in the seed state over a period of time. When extracting persistent features, the current image is used as the tail image to obtain an image sequence over a period of time, which is used to perform the persistent feature extraction process.

[0107] The information statistics feedback module 14 is used to statistically analyze the corrected sowing status of each seeding hole, combine the image to construct visualization information, and feed it back to the main end of the seeder; wherein, the visualization information includes at least images under different sowing statuses and status statistics information of each seeding hole.

[0108] Furthermore, the image acquisition module 11 includes:

[0109] The signal acquisition unit is used to acquire the seed passage signal output by the seeder alarm monitor; the seed passage signal is used to indicate whether there is an object passing through the seed discharge hole;

[0110] The instruction sending unit is used to generate a shooting instruction based on the seed signal and send the shooting instruction to the industrial camera device built into the seed metering device of the seeder;

[0111] The time stamping unit is used to timestamp the images captured by industrial camera equipment in the seed dispensing holes during the rotation of the seed tray.

[0112] The proprietary frequency determination unit is used to count the time points when the seed passes through the signal in real time and determine the proprietary frequency of the industrial camera equipment; the proprietary frequency is used to acquire images at regular intervals when there is no shooting instruction.

[0113] Specifically, the sowing status determination module 12 includes:

[0114] The preprocessing unit is used to read the latest image from any seeding hole in real time and preprocess the image; the preprocessing includes image cropping, size normalization and noise suppression;

[0115] The region segmentation unit is used to locate the seed region in the preprocessed image, segment the seed region based on the image segmentation algorithm, and extract the seed target and its contour features in the seeding hole.

[0116] The determination execution unit is used to determine the sowing status based on the extracted seed target and its contour features.

[0117] Furthermore, the sowing status correction module 13 includes:

[0118] The state statistics unit is used to count the sowing status at different times to obtain a state sequence;

[0119] The state transition unit is used to perform numerical transformation on the state sequence to obtain a numerical sequence;

[0120] An abnormal moment marking unit is used to calculate the difference sequence of a numerical sequence and determine the marking of abnormal moments based on the difference sequence; the abnormal moment refers to the moment point corresponding to the element index when the absolute value of the difference reaches a preset threshold.

[0121] The instantaneous feature determination unit is used to read the seed targets and their contour features of the image at an abnormal moment, and determine the geometric features based on the seed targets and their contour features as instantaneous features; wherein, the instantaneous features include the number of seeds, the proportion of holes occupied, and the seed spacing;

[0122] The persistent feature determination unit is used to determine an image sequence with the latest image as the tail image based on a preset time span, perform differential recognition on the image sequence, and determine the seed change vector as a persistent feature;

[0123] The correction execution unit is used to determine the current sowing state of the seed dispensing hole based on the extracted instantaneous and continuous features, and use this as the corrected sowing state; the sowing state includes at least the normal seed suction state, the double seed suction state, the no seed suction state, and the foreign object blockage state.

[0124] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-dimensional seeding alarm method for a seeder, characterized in that, The method includes: The image is captured by an industrial camera device built into the seed metering device of the seeder during the rotation of the seed tray; the image capture frequency of each seed metering hole is different. Target recognition is performed on the images of each seed hole to locate the seed target and its outline features, and to determine the sowing status; The sowing status at different times is statistically analyzed to obtain a state sequence. Temporal and feature analyses are performed on the state sequence to correct the sowing status. The feature analysis includes spatiotemporal analysis to obtain instantaneous features and persistent features. Instantaneous features are used to characterize the seed status in the image, while persistent features represent the changes in the seed status over a period of time. When extracting persistent features, the current image is used as the tail image to obtain an image sequence over a period of time, which is used to perform the persistent feature extraction process. The corrected sowing status of each seed row hole is statistically analyzed, and visualization information is constructed by combining the images and fed back to the main end of the seeder. The visualization information includes at least images under different sowing statuses and statistical information on the status of each seed row hole. The steps of statistically analyzing the sowing status at different times to obtain a state sequence, performing time-domain analysis and feature analysis on the state sequence, and correcting the sowing status include: The sowing status at different times is statistically analyzed to obtain a state sequence; A numerical sequence is obtained by performing a numerical transformation on the state sequence; Calculate the difference sequence of the numerical sequence, and determine the marked abnormal time based on the difference sequence; the abnormal time refers to the time point corresponding to the element index when the absolute value of the difference reaches a preset threshold. The seed targets and their contour features of the image at the abnormal moment are read, and the geometric features are determined based on the seed targets and their contour features as instantaneous features; wherein, the instantaneous features include the number of seeds, the proportion of holes occupied, and the seed spacing; Based on a preset time span, an image sequence is determined with the image at the latest moment as the tail image. The image sequence is then subjected to differential recognition to determine the seed change vector, which serves as a persistent feature. Based on the extracted instantaneous and continuous features, the current sowing state of the seed discharge hole is determined as the corrected sowing state; the sowing state includes at least the normal seed suction state, the double seed suction state, the no seed suction state, and the foreign object blockage state.

2. The multi-dimensional seeding alarm method for a seeder according to claim 1, characterized in that, The steps for acquiring images of each seed dispensing hole during the rotation of the seed disc using an industrial camera device built into the seed metering device of the seeder include: Collect the seed passage signal output by the alarm monitor of the seeder; the seed passage signal is used to indicate whether there is an object passing through the seed discharge hole; Based on the seed, a shooting command is generated through a signal and sent to an industrial camera device built into the seed metering device of the seeder; Images of the seed discharge holes during the rotation of the seed tray are captured using industrial camera equipment, and the captured images are timestamped. The system calculates the time points at which the seed passes through the signal in real time to determine the proprietary frequency of the industrial camera equipment; the proprietary frequency is used to periodically acquire images when there is no shooting instruction.

3. The multi-dimensional seeding alarm method for a seeder according to claim 1, characterized in that, The steps of performing target recognition on the images of each seed-rowing hole, locating the seed target and its contour features, and determining the sowing status include: For any seeding hole, the latest image is read in real time, and the image is preprocessed; the preprocessing includes image cropping, size normalization, and noise suppression; In the preprocessed image, the seed region is located, and the seed region is segmented based on the image segmentation algorithm to extract the seed target and its contour features in the seeding hole; The sowing status is determined based on the extracted seed targets and their contour features.

4. The multi-dimensional seeding alarm method for a seeder according to claim 1, characterized in that, The steps of statistically analyzing the corrected sowing status of each seeding hole, constructing visual information by combining it with images, and feeding it back to the central end of the seeder include: Read the state sequence and its difference sequence; The state sequence is segmented according to the difference sequence, and the image display ratio within each time period is determined synchronously. Based on the image display ratio, images are randomly extracted at different time periods, and images at abnormal moments are extracted synchronously. The state sequence and extracted images are used as visualization information and fed back to the central terminal of the seeder.

5. The multi-dimensional seeding alarm method for a seeder according to claim 1, characterized in that, The method further includes: Establish a connection channel with the sensors built into the seeder, acquire data from each sensor based on a preset data template, and obtain a real-time matrix; Read the seeding status at each time point and construct a real-time matrix to a sample set of seeding status; The pre-detection model is trained based on the sample set, and the default frequency of the industrial camera is recursively adjusted according to the error rate of the pre-detection model.

6. A multi-dimensional seeding alarm system for a seeder, characterized in that, The system includes: The image acquisition module is used to acquire images of each seeding hole during the rotation of the seed tray based on an industrial camera device built into the seed metering device of the seeder; wherein, the image acquisition frequency of each seeding hole is different; The sowing status determination module is used to perform target recognition on the images of each seeding hole, locate the seed target and its contour features, and determine the sowing status. The sowing state correction module is used to statistically analyze the sowing state at different times, obtain a state sequence, perform temporal and feature analysis on the state sequence, and correct the sowing state. The feature analysis includes spatiotemporal analysis to obtain instantaneous features and persistent features. Instantaneous features are used to characterize the seed state in the image, and persistent features represent the changes in the seed state over a period of time. When extracting persistent features, the current image is used as the tail image to obtain an image sequence over a period of time, which is used to perform the persistent feature extraction process. The information statistics and feedback module is used to statistically analyze the corrected sowing status of each seeding hole, combine it with images to construct visual information, and feed it back to the main end of the seeder; the visual information includes at least images under different sowing conditions and statistical information on the status of each seeding hole; The sowing status correction module includes: The state statistics unit is used to count the sowing status at different times to obtain a state sequence; The state transition unit is used to perform numerical transformation on the state sequence to obtain a numerical sequence; An abnormal moment marking unit is used to calculate the difference sequence of a numerical sequence and determine the marking of abnormal moments based on the difference sequence; the abnormal moment refers to the moment point corresponding to the element index when the absolute value of the difference reaches a preset threshold. The instantaneous feature determination unit is used to read the seed targets and their contour features of the image at an abnormal moment, and determine the geometric features based on the seed targets and their contour features as instantaneous features; wherein, the instantaneous features include the number of seeds, the proportion of holes occupied, and the seed spacing; The persistent feature determination unit is used to determine an image sequence with the latest image as the tail image based on a preset time span, perform differential recognition on the image sequence, and determine the seed change vector as a persistent feature; The correction execution unit is used to determine the current sowing state of the seed dispensing hole based on the extracted instantaneous and continuous features, and use this as the corrected sowing state; the sowing state includes at least the normal seed suction state, the double seed suction state, the no seed suction state, and the foreign object blockage state.

7. The multi-dimensional seeding alarm system for a seeder according to claim 6, characterized in that, The image acquisition module includes: The signal acquisition unit is used to acquire the seed passage signal output by the seeder alarm monitor; the seed passage signal is used to indicate whether there is an object passing through the seed discharge hole; The instruction sending unit is used to generate a shooting instruction based on the seed signal and send the shooting instruction to the industrial camera device built into the seed metering device of the seeder; The time stamping unit is used to timestamp the images captured by industrial camera equipment in the seed dispensing holes during the rotation of the seed tray. The proprietary frequency determination unit is used to count the time points when the seed passes through the signal in real time and determine the proprietary frequency of the industrial camera equipment; the proprietary frequency is used to acquire images at regular intervals when there is no shooting instruction.

8. The multi-dimensional seeding alarm system for a seeder according to claim 6, characterized in that, The sowing status determination module includes: The preprocessing unit is used to read the latest image from any seeding hole in real time and preprocess the image; the preprocessing includes image cropping, size normalization and noise suppression; The region segmentation unit is used to locate the seed region in the preprocessed image, segment the seed region based on the image segmentation algorithm, and extract the seed target and its contour features in the seeding hole. The determination execution unit is used to determine the sowing status based on the extracted seed target and its contour features.

Images