Method, device and equipment for estimating yield of melon vegetables and medium
The dynamic characteristics of melon vegetables were extracted through video streaming data, and the melon vegetables in the mature and development stages were determined, yield data was obtained and dynamic prediction was made. The problem of yield monitoring reliance on manual statistics in traditional agriculture was solved, and the yield prediction of melon vegetables was achieved with high accuracy.
Patent Information
- Application Number
- CN202510038836.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-10
- Publication Date
- 2025-05-06
AI Technical Summary
The output monitoring of open sweet potato vegetables in traditional agriculture relies on manual statistics, which is time-consuming and labor-intensive and vulnerable to subjective factors. It is difficult for the existing technology to effectively estimate the yield of melon vegetables in the future.
By obtaining the video stream data of each melon vegetables in the target planting area within the first preset period, dynamic characteristics are extracted, melon vegetables in the mature and development stages are determined, yield data is obtained, and dynamic yield prediction is performed based on these data.
Accurate continuous dynamic prediction of melon vegetable yields is achieved, the accuracy of yield estimates is improved, and the problems of time-consuming and labor-intensive and subjective factors influenced by manual statistics are solved.
Smart Images
Figure CN119942531A_ABST
Abstract
Description
Background Art
[0002] In traditional agriculture, the yield monitoring of open-field melons and vegetables mainly relies on manual statistics, which is not only time-consuming and labor-intensive, but also easily affected by subjective factors, resulting in inaccurate monitoring results. There are also some existing methods that use models to estimate yields, but these methods usually collect images in real time for recognition, which cannot effectively estimate the yield of melons and vegetables in the future. Summary of the invention
[0003] The present application provides a method, device, equipment and medium for estimating the yield of melon vegetables, which can realize accurate continuous dynamic prediction of the yield of melon vegetables.
[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.
[0005] According to one aspect of an embodiment of the present application, a method for estimating the yield of melon vegetables is provided, the method comprising:
[0006] Acquire dynamic characteristics of each melon vegetable in the target planting area within a first preset time period;
[0007] Determine, according to the dynamic characteristics of each of the melon vegetables, the first target melon vegetables that are currently in the mature stage and the second target melon vegetables that are currently in the development stage;
[0008] Acquire the first yield data of each of the first target melon vegetables, and determine the second yield data of each of the second target melon vegetables within a second preset time period according to the dynamic characteristics of each of the second target melon vegetables;
[0009] Determine the dynamic yield data of the target planting area within the second preset time period according to the first yield data and the second yield data;
[0010] The dynamic characteristics of a single melon vegetable can be obtained by the following steps:
[0011] Acquire video stream data of the melon vegetables in the first preset time period, wherein the video stream data includes a time series associated with the first preset time period;
[0012] Extracting features of the melon vegetables based on the video stream data to obtain a target feature sequence related to the time series;
[0013] Feature fusion is performed according to the target feature sequence to obtain the dynamic features of the melon vegetables.
[0014] In one embodiment of the present application, based on the above scheme, the target feature sequence includes an edge feature sequence, a texture feature sequence and a morphological feature sequence; the feature extraction of the melon vegetables based on the video stream data to obtain the target feature sequence related to the time series includes:
[0015] Extract edge features of the melon vegetables based on the video stream data to obtain an edge feature sequence related to the time series;
[0016] Extracting texture features of the melon vegetables based on the video stream data to obtain a texture feature sequence related to the time series;
[0017] The morphological features of the melon vegetables are extracted based on the video stream data to obtain a morphological feature sequence related to the time series.
[0018] In one embodiment of the present application, based on the above scheme, the feature fusion is performed according to the target feature sequence to obtain the dynamic features of the melon vegetables, including:
[0019] Feature fusion is performed according to the edge feature sequence, the texture feature sequence and the morphological feature sequence in the target feature sequence to obtain the dynamic features of the melon vegetables.
[0020] In one embodiment of the present application, based on the above scheme, determining the first target melon vegetables in the mature stage and the second target melon vegetables in the development stage at the current moment according to the dynamic characteristics of each melon vegetable includes:
[0021] Acquire the target feature of each melon vegetable at the current moment according to the dynamic feature of each melon vegetable;
[0022] The first target melon vegetables in the mature stage and the second target melon vegetables in the developing stage are determined according to the preset feature relationship library and each of the target features.
[0023] In one embodiment of the present application, based on the above scheme, the target features include target texture features, target edge features and target morphological features, and the step of obtaining the first yield data of each of the first target melon vegetables includes:
[0024] Determine weight estimation data of each of the first target melon vegetables according to target texture features, target edge features, and target morphological features of each of the first target melon vegetables;
[0025] The first production data is determined according to each of the weight estimation data.
[0026] In one embodiment of the present application, based on the above scheme, determining the second yield data of each of the second target melon vegetables in the second preset time period according to the dynamic characteristics of each of the second target melon vegetables includes:
[0027] Acquire environmental characteristic data in the target planting area, the environmental characteristic data including soil composition information, growth cycle information and climate information of the second target melon vegetables;
[0028] The second yield data of each of the second target melon vegetables within a second preset time period is determined according to the soil composition information, the growth cycle information, the climate information and the dynamic characteristics of each of the second target melon vegetables.
[0029] In one embodiment of the present application, based on the above scheme, determining the second yield data of each of the second target melon vegetables within a second preset time period according to the soil composition information, the growth cycle information, the climate information and the dynamic characteristics of each of the second target melon vegetables includes:
[0030] Determine the estimated time required for each of the second target melon vegetables to enter the mature stage from the development stage according to the soil composition information, the growth cycle information, the climate information and the dynamic characteristics of each of the second target melon vegetables;
[0031] Finding a target time that is shorter than the second preset time period among the estimated maturity times;
[0032] Acquire target dynamic characteristics of the second target melon vegetables corresponding to each of the target times;
[0033] The second production data is determined according to each of the target dynamic characteristics.
[0034] According to one aspect of an embodiment of the present application, a device for estimating the yield of melon vegetables is provided, the device comprising:
[0035] A first acquisition unit is used to acquire dynamic characteristics of each melon vegetable in the target planting area within a first preset time period;
[0036] A first determination unit is used to determine the first target melon vegetables that are currently in a mature stage and the second target melon vegetables that are currently in a developing stage according to the dynamic characteristics of each of the melon vegetables;
[0037] A second acquisition unit is used to acquire the first yield data of each of the first target melon vegetables, and determine the second yield data of each of the second target melon vegetables within a second preset time period according to the dynamic characteristics of each of the second target melon vegetables;
[0038] A second determining unit, configured to determine the dynamic yield data of the target planting area within the second preset time period according to the first yield data and the second yield data;
[0039] The dynamic characteristics of a single melon vegetable can be obtained by the following steps:
[0040] Acquire video stream data of the melon vegetables in the first preset time period, wherein the video stream data includes a time series associated with the first preset time period;
[0041] Extracting features of the melon vegetables based on the video stream data to obtain a target feature sequence related to the time series;
[0042] Feature fusion is performed according to the target feature sequence to obtain the dynamic features of the melon vegetables.
[0043] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored. The computer program includes executable instructions. When the executable instructions are executed by a processor, the method described in the above embodiment is implemented.
[0044] According to one aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors; and a memory for storing executable instructions of the processors, wherein when the executable instructions are executed by the one or more processors, the one or more processors implement the methods described in the above embodiments.
[0045] Beneficial effects of the present application: The present application obtains the dynamic characteristics of each melon vegetable in the target planting area within a first preset time period. The first preset time period can be the video stream data of the melon vegetables within a certain continuous period of time. By analyzing the dynamic characteristics corresponding to the video stream data, the growth dynamics of the melon vegetables can be effectively identified, so as to improve the accuracy of subsequent yield estimation.
[0046] Furthermore, by analyzing the dynamic characteristics of each melon vegetable, the first target melon vegetables that are mature at the current moment and the second target melon vegetables that are still in the development stage are obtained. By real-time collection, analysis and calculation of the yield of the first target melon vegetables, the second target melon vegetables are dynamically predicted. In the end, the dynamic yield data of all melon vegetables in the target planting area within the second preset time period can be effectively and accurately obtained, which solves the problem in the prior art that relies on manual statistics and cannot effectively estimate the yield of melon vegetables.
[0047] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0048] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:
[0049] Figure 1 It is a flow chart of a method for estimating the yield of melon vegetables according to an embodiment of the present application;
[0050] Figure 2 A schematic diagram of a process for obtaining dynamic characteristics of a single melon vegetable according to an embodiment of the present application;
[0051] Figure 3 It is a block diagram of a yield estimation device for melon vegetables according to an embodiment of the present application;
[0052] Figure 4 It is a schematic diagram of the system structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION
[0053] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.
[0054] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.
[0055] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or micro-control node devices.
[0056] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.
[0057] It should be noted that the "multiple" mentioned in this article refers to two or more. "And / or" describes the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the associated objects before and after are in an "or" relationship.
[0058] The implementation details of the technical solution of the embodiment of the present application are described in detail below:
[0059] According to one aspect of the present application, a method for estimating the yield of melon vegetables is provided. Figure 1 This is a flow chart of a method for estimating the yield of melon vegetables according to an embodiment of the present application. The method for estimating the yield of melon vegetables includes at least steps 110 to 140, which are described in detail as follows:
[0060] In step 110, dynamic characteristics of each melon vegetable in the target planting area within a first preset time period are obtained.
[0061] Specifically, the user can freely set the target planting area, which can be a certain piece of farmland or a certain planting plot in the open field. The target planting area is not limited here. The first preset time period can be set according to user needs, which can be from 8 am to 6 pm, or a specific time period such as 8:20 am to 12:20 am, etc. In the target planting area, a plurality of high-definition cameras (such as CMOS sensor cameras with a resolution of 1080P or higher) with waterproof, dustproof, and UV-proof properties are provided. These high-definition cameras can capture the growth environment of open-field melon vegetables to obtain video stream data. The camera can be mounted on a bracket with adjustable height and angle to obtain the best shooting angle, so that the collected video stream data is more accurate and easier to extract features.
[0062] For each high-definition camera, high dynamic range technology is configured for each camera to improve the contrast of the image in strong light and shadow environments; at the same time, adaptive frame rate adjustment technology is used to automatically adjust the shooting frame rate according to the lighting conditions (such as reducing the frame rate when the light is sufficient and increasing the frame rate when the light is insufficient) to reduce power consumption. In addition, the camera also has a night vision function and uses infrared fill light technology to ensure that clear images can be obtained at night or in low light conditions.
[0063] In one embodiment of the present application, Figure 2 As shown, the dynamic characteristics of a single melon vegetable can be obtained by following steps S1-S3:
[0064] Step S1: acquiring video stream data of the melon vegetables in the first preset time period, wherein the video stream data includes a time series associated with the first preset time period;
[0065] Step S2: extracting features of the melon vegetables based on the video stream data to obtain a target feature sequence related to the time series;
[0066] Step S3: performing feature fusion according to the target feature sequence to obtain the dynamic features of the melon vegetables.
[0067] Specifically, the video stream data is first preprocessed, including denoising, contrast enhancement, color correction, etc. The preprocessing module also has an image cropping function, which can automatically crop out the effective image area according to the shooting range of the camera to reduce the amount of calculation for subsequent processing. The video stream data includes a time series associated with the first preset time period, that is, each time data in the time series during this period corresponds to each frame of the picture collected by the high-definition camera. For example, there are five time data in the time series, namely 8:20, 8:22, 8:24, 8:26, and 8:28, then the high-definition camera corresponding to these five time points collected five frames of pictures of melon vegetables in the planting area.
[0068] Furthermore, feature extraction is performed on melon vegetables to obtain a target feature sequence related to the time series. That is to say, at each time point in the time series, image data of each melon vegetable in the target planting area is collected, and feature extraction is performed on the image data at these different time points one by one to obtain a target feature sequence formed by multiple features. Finally, the feature sequence is fused to obtain the feature changes brought about by melon vegetables at different time points, that is, the dynamic features of melon vegetables.
[0069] In one embodiment of the present application, the target feature sequence includes an edge feature sequence, a texture feature sequence and a morphological feature sequence; the feature extraction of the melon vegetables based on the video stream data to obtain the target feature sequence related to the time series includes:
[0070] Extract edge features of the melon vegetables based on the video stream data to obtain an edge feature sequence related to the time series;
[0071] Extracting texture features of the melon vegetables based on the video stream data to obtain a texture feature sequence related to the time series;
[0072] The morphological features of the melon vegetables are extracted based on the video stream data to obtain a morphological feature sequence related to the time series.
[0073] Specifically, taking a certain time point in the time series as an example, at this time point, the operations required are to perform edge feature extraction, texture feature extraction and morphological feature extraction on melon vegetables respectively. Then, an extraction is performed at each time point to obtain the edge feature sequence, texture feature sequence and morphological feature sequence related to the time series.
[0074] In one embodiment of the present application, the step of performing feature fusion according to the target feature sequence to obtain the dynamic features of the melon vegetables includes:
[0075] Feature fusion is performed according to the edge feature sequence, the texture feature sequence and the morphological feature sequence in the target feature sequence to obtain the dynamic features of the melon vegetables.
[0076] Specifically, feature fusion is performed on the data corresponding to each time point in the time series, and multi-scale feature extraction is performed on the preprocessed image using a deep learning algorithm (such as a convolutional neural network CNN). Convolution kernels of different scales are used to capture the edge features, texture features, and shape features of melon vegetables at different scales. Then, weighted fusion is performed based on the edge features, texture features, and shape features to obtain the dynamic features of melon vegetables, so as to improve the feature expression effect of melon vegetables.
[0077] In step 120, first target melon vegetables that are currently in a mature stage and second target melon vegetables that are currently in a developing stage are determined based on the dynamic characteristics of each of the melon vegetables.
[0078] In one embodiment of the present application, determining the first target melon vegetables that are currently in the mature stage and the second target melon vegetables that are currently in the development stage according to the dynamic characteristics of each melon vegetable includes:
[0079] Acquire the target feature of each melon vegetable at the current moment according to the dynamic feature of each melon vegetable;
[0080] The first target melon vegetables in the mature stage and the second target melon vegetables in the developing stage are determined according to the preset feature relationship library and each of the target features.
[0081] Specifically, in order to count the melon vegetables that are currently in the mature stage in the target planting area, the target characteristics of each melon vegetable at the current moment are obtained according to the dynamic characteristics of each melon vegetable, that is, the target characteristics of the melon vegetables at the current moment are obtained through the dynamic characteristics. Then, by analyzing the target characteristics, it is possible to distinguish the melon vegetables in the mature stage and the developmental stage, that is, the first target melon vegetables in the mature stage and the second target melon vegetables in the developmental stage.
[0082] In step 130, the first yield data of each of the first target melon vegetables is obtained, and the second yield data of each of the second target melon vegetables within a second preset time period is determined according to the dynamic characteristics of each of the second target melon vegetables.
[0083] In one embodiment of the present application, the target features include target texture features, target edge features, and target morphological features, and the step of obtaining the first yield data of each of the first target melon vegetables includes:
[0084] Determine weight estimation data of each of the first target melon vegetables according to target texture features, target edge features, and target morphological features of each of the first target melon vegetables;
[0085] The first production data is determined according to each of the weight estimation data.
[0086] Specifically, the first yield data is used to count the weight of mature melon vegetables, that is, the number of kilograms. Then, the estimated weight data of each of the first target melon vegetables is determined according to the target texture characteristics, target edge characteristics and target morphological characteristics of each of the first target melon vegetables, and the first yield data can be obtained by adding up the estimated weights.
[0087] In one embodiment of the present application, determining the second yield data of each of the second target melon vegetables within a second preset time period according to the dynamic characteristics of each of the second target melon vegetables includes:
[0088] Acquire environmental characteristic data in the target planting area, the environmental characteristic data including soil composition information, growth cycle information and climate information of the second target melon vegetables;
[0089] The second yield data of each of the second target melon vegetables within a second preset time period is determined according to the soil composition information, the growth cycle information, the climate information and the dynamic characteristics of each of the second target melon vegetables.
[0090] Specifically, the prediction accuracy is improved by considering multiple factors such as the growth cycle of melon vegetables, the climatic conditions, soil conditions, etc., that is, the second yield data of each of the second target melon vegetables within the second preset time period is determined based on the soil composition information, the growth cycle information, the climate information and the dynamic characteristics of each of the second target melon vegetables.
[0091] In one embodiment of the present application, determining the second yield data of each of the second target melon vegetables within a second preset time period according to the soil composition information, the growth cycle information, the climate information and the dynamic characteristics of each of the second target melon vegetables includes:
[0092] Determine the estimated time required for each of the second target melon vegetables to enter the mature stage from the development stage according to the soil composition information, the growth cycle information, the climate information and the dynamic characteristics of each of the second target melon vegetables;
[0093] Finding a target time that is shorter than the second preset time period among the estimated maturity times;
[0094] Acquire target dynamic characteristics of the second target melon vegetables corresponding to each of the target times;
[0095] The second production data is determined according to each of the target dynamic characteristics.
[0096] Specifically, the estimated time for maturity of each of the second target melon vegetables to enter the mature stage from the development stage is determined by combining the dynamic characteristics of each of the second target melon vegetables with environmental factors, that is, soil composition information, the growth cycle information, and the climate information. By finding a target time that is less than the length of the second preset period in each of the estimated maturity times, for example, the length of the second preset period is 3 days, and the estimated time for maturity of a second target melon vegetable is 4 days, then the estimated time for maturity of the second target melon vegetable cannot be used as the target time and cannot be included in the subsequent second yield data. If the estimated time for maturity is less than 3 days as exemplified above, then these estimated times for maturity are determined as target times, the target dynamic characteristics of the second target melon vegetables corresponding to each of the target times are obtained, and the second yield data of these second target melon vegetables are determined.
[0097] In step 140, dynamic yield data of the target planting area within the second preset time period is determined based on the first yield data and the second yield data.
[0098] Specifically, the second yield data within the second preset time period is constantly changing, and the first yield data can be combined with the constantly changing second yield data to generate dynamic yield data at each time point to achieve continuous dynamic prediction of melon vegetable yields and improve the accuracy of melon vegetable yield predictions.
[0099] In one embodiment of the present application, the feature library preset in the present application needs to be calibrated regularly to reflect changes in actual production conditions. The sliding window technology is used to smooth the time series data to improve the stability of the prediction. In addition, the output estimation results can be displayed to the user in the form of a chart to facilitate user understanding and decision-making.
[0100] A low-power, high-performance embedded processor (such as Cortex-M7 or higher in the ARM Cortex-M series) is selected as the core, which is responsible for data processing and decision output of the control unit of the entire estimation method.
[0101] The entire prediction system model of the embodiment of the present application also has multi-task processing capabilities, and can run multiple tasks (such as data collection, preprocessing, feature extraction, recognition and matching, mapping and prediction, etc.) at the same time to improve the overall performance of the system. In addition, low-power power management strategies are designed, such as dynamic voltage adjustment, sleep mode, etc., to reduce overall power consumption.
[0102] In one embodiment of the present application, the power management module of the entire estimation system model: optimizes power management by adopting a high-efficiency battery management system to ensure that the device operates in a low-power state. The power management module must have functions such as intelligent charging, power monitoring, and low-power standby. The power management module also has a solar-assisted charging system that uses solar panels to convert solar energy into electrical energy to provide a continuous power supply for the device. Solar panels must have high efficiency, waterproof, dustproof and other characteristics to adapt to complex open-air environments. In addition, a battery power monitoring algorithm is designed to monitor the battery power status in real time, and automatically switch to low-power standby mode or remind the user to charge when the power is low.
[0103] Figure 3 This is a block diagram of a yield estimation device 300 for melon vegetables according to an embodiment of the present application. According to the yield estimation device 300 for melon vegetables according to an embodiment of the present application, the device 300 includes: a first acquisition unit 301, a first determination unit 302, a second acquisition unit 303, and a second determination unit 304.
[0104] The first acquisition unit 301 is used to acquire the dynamic characteristics of each melon vegetable in the target planting area within a first preset time period;
[0105] The first determination unit 302 is used to determine the first target melon vegetables that are currently in the mature stage and the second target melon vegetables that are currently in the development stage according to the dynamic characteristics of each of the melon vegetables;
[0106] The second acquisition unit 303 is used to acquire the first yield data of each of the first target melon vegetables, and determine the second yield data of each of the second target melon vegetables within a second preset time period according to the dynamic characteristics of each of the second target melon vegetables;
[0107] A second determining unit 304 is used to determine the dynamic yield data of the target planting area within the second preset time period according to the first yield data and the second yield data;
[0108] The dynamic characteristics of a single melon vegetable can be obtained by the following steps:
[0109] Acquire video stream data of the melon vegetables in the first preset time period, wherein the video stream data includes a time series associated with the first preset time period;
[0110] Extracting features of the melon vegetables based on the video stream data to obtain a target feature sequence related to the time series;
[0111] Feature fusion is performed according to the target feature sequence to obtain the dynamic features of the melon vegetables.
[0112] As another aspect, the present application further provides a computer-readable storage medium on which a program product capable of implementing the method provided above in this specification is stored. In some possible implementations, various aspects of the present application may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to cause the terminal device to execute the steps according to various exemplary implementations of the present application described in the above "Embodiment Method" section of this specification.
[0113] According to the program product for implementing the above method in the embodiment of the present application, it can adopt a portable compact disk read-only memory (CD-ROM) and include program code, and can be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto. In this document, a readable storage medium can be any tangible medium containing or storing a program, which can be used by or in combination with an instruction execution system, an apparatus or a device.
[0114] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM 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 of the above.
[0115] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0116] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.
[0117] Program code for performing the operations of the present application may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., using an Internet service provider to connect through the Internet).
[0118] As another aspect, the present application also provides an electronic device capable of implementing the above method.
[0119] Those skilled in the art will appreciate that various aspects of the present application may be implemented as a system, method or program product. Therefore, various aspects of the present application may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to as "circuit", "module" or "system" herein.
[0120] Refer to the following Figure 4 The electronic device 400 according to this embodiment of the present application is described. Figure 4 The electronic device 400 shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0121] like Figure 4 As shown, the electronic device 400 is in the form of a general computing device. The components of the electronic device 400 may include but are not limited to: at least one processing unit 410, at least one storage unit 420, and a bus 430 connecting different system components (including the storage unit 420 and the processing unit 410).
[0122] The storage unit stores program codes, which can be executed by the processing unit 410, so that the processing unit 410 executes the steps described in the above “Example Method” section of this specification according to various exemplary implementations of the present application.
[0123] The storage unit 420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .
[0124] The storage unit 420 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.
[0125] Bus 430 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller node, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.
[0126] The electronic device 400 may also communicate with one or more external devices 1200 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 400, and / or communicate with any device that enables the electronic device 400 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 450. In addition, the electronic device 400 may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 460. As shown, the network adapter 460 communicates with other modules of the electronic device 400 via a bus 430. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0127] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation methods of the present application.
[0128] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to the exemplary embodiments of the present application, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.
[0129] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be performed without departing from the scope thereof. The scope of the present application is limited only by the appended claims.
Claims
1. A method for estimating the yield of melon vegetables, characterized in that: The method comprises: Acquire dynamic characteristics of each melon vegetable in the target planting area within a first preset time period; Determine, according to the dynamic characteristics of each of the melon vegetables, the first target melon vegetables that are currently in the mature stage and the second target melon vegetables that are currently in the development stage; Acquire the first yield data of each of the first target melon vegetables, and determine the second yield data of each of the second target melon vegetables within a second preset time period according to the dynamic characteristics of each of the second target melon vegetables; Determine the dynamic yield data of the target planting area within the second preset time period according to the first yield data and the second yield data; The dynamic characteristics of a single melon vegetable can be obtained by the following steps: Acquire video stream data of the melon vegetables in the first preset time period, wherein the video stream data includes a time series associated with the first preset time period; Extracting features of the melon vegetables based on the video stream data to obtain a target feature sequence related to the time series; Feature fusion is performed according to the target feature sequence to obtain the dynamic features of the melon vegetables.
2. The method for estimating the yield of melon vegetables according to claim 1, characterized in that: The target feature sequence includes an edge feature sequence, a texture feature sequence and a morphological feature sequence; the feature extraction of the melon vegetables based on the video stream data to obtain a target feature sequence related to the time series includes: Extract edge features of the melon vegetables based on the video stream data to obtain an edge feature sequence related to the time series; Extracting texture features of the melon vegetables based on the video stream data to obtain a texture feature sequence related to the time series; The morphological features of the melon vegetables are extracted based on the video stream data to obtain a morphological feature sequence related to the time series.
3. The method for estimating the yield of melon vegetables according to claim 2, characterized in that: The step of performing feature fusion according to the target feature sequence to obtain the dynamic features of the melon vegetables includes: Feature fusion is performed according to the edge feature sequence, the texture feature sequence and the morphological feature sequence in the target feature sequence to obtain the dynamic features of the melon vegetables.
4. The method for estimating the yield of melon vegetables according to claim 2, characterized in that: The method of determining the first target melon vegetables that are currently in the mature stage and the second target melon vegetables that are currently in the development stage according to the dynamic characteristics of each melon vegetable comprises: Acquire the target feature of each melon vegetable at the current moment according to the dynamic feature of each melon vegetable; The first target melon vegetables in the mature stage and the second target melon vegetables in the developing stage are determined according to the preset feature relationship library and each of the target features.
5. The method for estimating the yield of melon vegetables according to claim 4, characterized in that: The target features include target texture features, target edge features and target morphological features, and the step of obtaining the first yield data of each of the first target melon vegetables includes: Determine weight estimation data of each of the first target melon vegetables according to target texture features, target edge features, and target morphological features of each of the first target melon vegetables; The first production data is determined according to each of the weight estimation data.
6. The method for estimating the yield of melon vegetables according to claim 5, characterized in that: The determining of the second yield data of each of the second target melon vegetables within a second preset time period according to the dynamic characteristics of each of the second target melon vegetables includes: Acquire environmental characteristic data in the target planting area, wherein the environmental characteristic data includes soil composition information, growth cycle information of the second target melon vegetables, and climate information; The second yield data of each of the second target melon vegetables within a second preset time period is determined according to the soil composition information, the growth cycle information, the climate information and the dynamic characteristics of each of the second target melon vegetables.
7. The method for estimating the yield of melon vegetables according to claim 6, characterized in that: The determining of the second yield data of each of the second target melon vegetables within a second preset time period according to the soil composition information, the growth cycle information, the climate information and the dynamic characteristics of each of the second target melon vegetables comprises: Determine the estimated time required for each of the second target melon vegetables to enter the mature stage from the development stage according to the soil composition information, the growth cycle information, the climate information and the dynamic characteristics of each of the second target melon vegetables; Finding a target time that is shorter than the second preset time period among the estimated maturity times; Acquire target dynamic characteristics of the second target melon vegetables corresponding to each of the target times; The second production data is determined according to each of the target dynamic characteristics.
8. A yield estimation device for melon vegetables, characterized in that: The device comprises: A first acquisition unit is used to acquire dynamic characteristics of each melon vegetable in the target planting area within a first preset time period; A first determination unit is used to determine the first target melon vegetables that are currently in a mature stage and the second target melon vegetables that are currently in a developing stage according to the dynamic characteristics of each of the melon vegetables; A second acquisition unit is used to acquire the first yield data of each of the first target melon vegetables, and determine the second yield data of each of the second target melon vegetables within a second preset time period according to the dynamic characteristics of each of the second target melon vegetables; A second determining unit, configured to determine the dynamic yield data of the target planting area within the second preset time period according to the first yield data and the second yield data; The dynamic characteristics of a single melon vegetable can be obtained by the following steps: Acquire video stream data of the melon vegetables in the first preset time period, wherein the video stream data includes a time series associated with the first preset time period; Extracting features of the melon vegetables based on the video stream data to obtain a target feature sequence related to the time series; Feature fusion is performed according to the target feature sequence to obtain the dynamic features of the melon vegetables.
9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one program code, and the at least one program code is loaded and executed by a processor to implement the operations performed by the method according to any one of claims 1 to 7.
10. An electronic device, characterized in that: The electronic device includes one or more processors and one or more memories, wherein the one or more memories store at least one program code, and the at least one program code is loaded and executed by the one or more processors to implement the operations performed by the method according to any one of claims 1 to 7.