Tomato yield prediction method, equipment, medium and product

Abstract

The invention relates to the technical field of information, and provides a tomato yield prediction method and device, a medium and a product. The tomato yield prediction method comprises the steps of obtaining greenhouse image data, short-term environment data and long-term environment data of tomatoes, and inputting the greenhouse image data, the short-term environment data and the long-term environment data of the tomatoes into a trained tomato yield prediction model to obtain a tomato yield prediction result. By means of the configuration mode, the precision of predicting the tomato yield can be improved on the premise that timeliness is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of information technology, and in particular to a tomato yield prediction method, device, medium and product. Background Art

[0002] In the process of greenhouse cultivation, accurate prediction of tomato yield is very important. However, the current manual recording method has great limitations. Due to the particularity and complexity of greenhouse operation scenes, the manual recording method cannot guarantee the accuracy and timeliness of tomato yield. Summary of the invention

[0003] One purpose of the present application is to provide a tomato yield prediction method, at least to solve the problem of how to improve the accuracy of predicting tomato yield while ensuring timeliness.

[0004] To achieve the above objectives, some embodiments of the present application provide the following aspects:

[0005] In a first aspect, some embodiments of the present application further provide a method for predicting tomato yield, the method comprising:

[0006] Obtain greenhouse image data, short-term environmental data, and long-term environmental data of tomatoes;

[0007] The greenhouse image data of tomatoes, the short-term environmental data and the long-term environmental data are input into the trained tomato yield prediction model to obtain a tomato yield prediction result.

[0008] In a second aspect, some embodiments of the present application further provide an electronic device, comprising: one or more processors; and a memory storing computer program instructions, wherein the computer program instructions, when executed, cause the processor to perform the steps of the method described above.

[0009] In a third aspect, some embodiments of the present application further provide a computer-readable medium having computer program instructions stored thereon, wherein the computer program instructions can be executed by a processor to implement the method as described above.

[0010] In a fourth aspect, some embodiments of the present application further provide a computer program product, comprising a computer program / instruction, which implements the steps of the method described above when executed by a processor.

[0011] Compared with the related art, in the solution provided by the embodiment of the present application, greenhouse image data, short-term environmental data and long-term environmental data of tomatoes are obtained, and the greenhouse image data, short-term environmental data and long-term environmental data of tomatoes are input into the trained tomato yield prediction model to obtain the tomato yield prediction result. Through the above configuration, the present application inputs the greenhouse image data of tomatoes obtained in real time, and the short-term environmental data and long-term environmental data into the trained tomato yield prediction model to perform a more comprehensive prediction to obtain the tomato yield prediction result. Therefore, the present application improves the accuracy of predicting tomato yield while ensuring timeliness. Thereby, the subsequent picking and sales work can be carried out more effectively and reasonably, ensuring the rational development of agricultural work. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0013] Figure 1 This is a schematic flow chart of the main steps of a method for predicting tomato yield according to an embodiment of the present application;

[0014] Figure 2 It is a schematic diagram of an exemplary structure of an electronic device according to the present application. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0016] For the convenience of description, we first define some data symbols. s ,y l ) three data to predict tomato yield. Represents greenhouse image data of tomatoes, T represents the number of data at different times, G represents the number of data in different subspaces (the image of the entire greenhouse will be divided into different subspaces to obtain more accurate feature description), and H, W, C represent the height, width, and number of color channels of the image. and Represents short-term environmental data and long-term environmental data, we can assume that y s is the daily data, yl The data is monthly. 1 Represents the number of days of short-term data, N 2 Represents how many months of long-term data there are, d y Indicates the type of environmental data (soil pH, temperature, humidity, etc. are all different types of environmental data).

[0017] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a method for predicting tomato yield according to an embodiment of the present application. Figure 1 As shown, the tomato yield prediction method in the embodiment of the present application mainly includes the following steps S101-S102.

[0018] Step S101: Acquire greenhouse image data, short-term environmental data and long-term environmental data of tomatoes.

[0019] In this embodiment, RGB monitoring obtains greenhouse image data of tomatoes in real time, and sensors (such as temperature sensors and humidity sensors) obtain short-term environmental data and long-term environmental data in real time, wherein the short-term environmental data is environmental data in days (not exceeding 30 days), such as 20 days, and the long-term environmental data is environmental data in months (not exceeding 12 months), such as 4 months.

[0020] In one embodiment, the short-term environmental data includes short-term soil pH data, short-term temperature data, and short-term humidity data.

[0021] In one embodiment, the long-term environmental data includes long-term soil pH data, long-term temperature data, and long-term humidity data.

[0022] Step S102: inputting the greenhouse image data of tomatoes, the short-term environmental data and the long-term environmental data into the trained tomato yield prediction model to obtain a tomato yield prediction result.

[0023] In this embodiment, the trained tomato yield prediction model mainly includes a multimodal spatiotemporal visual converter and a linear classifier. The multimodal converter is used to extract and fuse the features of the greenhouse image data of the tomato, the short-term environmental data and the long-term environmental data, and the linear classifier is used to predict the output result of the multimodal converter to obtain the tomato yield prediction result. Among them, the multimodal converter includes a multimodal spatiotemporal visual converter that includes three main components: a multimodal converter, which is used to capture the impact of short-term environmental data changes on crop growth through images and short-term environmental data; a spatial converter, which uses the output of the multimodal converter to learn the global spatial information of the entire greenhouse; and a temporal converter, which combines the output of the spatial converter with the long-term environmental data to describe the impact of global temporal information and long-term environmental changes on crop yields.

[0024] In one implementation, step S102 may further include steps S1021 to S1023:

[0025] Step S1021: extracting features from the greenhouse image data of the tomatoes and the short-term environmental data to obtain feature data of the greenhouse image data of the tomatoes and feature data of the short-term environmental data.

[0026] Step S1022: Fusing the feature data of the greenhouse image data of the tomatoes with the feature data of the short-term environmental data to obtain a first fused feature.

[0027] In this embodiment, the multimodal converter is composed of a visual backbone network and a multimodal attention layer. The visual backbone network is responsible for extracting visual feature representations from images, and the multimodal attention layer is responsible for capturing the relationship between visual representations and environmental data. Therefore, the input of this part is the greenhouse image data of tomatoes and short-term environmental data. After that, feature extraction is performed on the greenhouse image data of tomatoes and the short-term environmental data to obtain feature data of the greenhouse image data of tomatoes and feature data of the short-term environmental data. Feature fusion is performed through the multimodal converter, which is expressed as Among them, v mis the first fusion feature; θ refers to the entire parameter set of the multimodal converter, including all parameters of this part (including the visual backbone network and the multimodal attention layer). For example, all parameters of the Pyrami d Visual Transformer (PVT) model of the visual backbone network, as well as the parameter matrices in the multimodal attention layer mentioned below. d represents the dimension of the hidden layer, and those skilled in the art can customize d according to actual usage. This application uses a pre-trained Pyrami d Visual Transformer (PVT) as the visual backbone network. For the multimodal attention layer, this application uses a multimodal multi-head attention mechanism (MM-MHA) to learn the impact of short-term environmental factors on crop growth:

[0028]

[0029] in, is the visual feature encoded by PVT, N p Represents the number of blocks of the image (the image will be divided into multiple small blocks vertically and horizontally for encoding). It is short-term environmental data for linear mapping. is the learnable parameter matrix ( refer to ), Q, K, and V are the symbols of the three matrices, which are the core matrices in the attention mechanism.

[0030] Step S1023: Obtain a tomato yield prediction result based on the first fusion feature and the long-term environmental data.

[0031] In one implementation, step S1023 further includes steps S10231 to S10232:

[0032] Step S10231: Perform global spatial features based on the first fusion features to obtain global spatial features of the greenhouse.

[0033] In this embodiment, the greenhouse image data of tomatoes in this application is divided into G grids for more accurate data analysis. In order to learn the spatial dependencies between these grids, this application proposes a spatial transformer Extract global spatial features, where φ represents the parameters of this part. The spatial transformer first prepends a learnable classification mark to the target feature space Right now:

[0034]

[0035] in, is a variable spatial position encoding, which can be

[0036]

[0037] or other types of spatial position encodings.

[0038] In addition, this application proposes a spatial multi-head attention mechanism (S-MHA) to learn the spatial dependencies between grids:

[0039]

[0040] in, is the learnable parameter matrix, φ s The definition of has been explained above.

[0041] The final result of this part is v s represents the global spatial features, and d represents the hidden layer vector representation containing the global spatial information.

[0042] Step S10232: Based on the global spatial characteristics of the greenhouse and the long-term environmental data, the tomato yield prediction result is obtained.

[0043] In one implementation, step S10232 further includes steps S102321 to S102322:

[0044] Step S102321: Fusing the global spatial features of the greenhouse and the long-term environmental data to obtain a second fused feature.

[0045] In this embodiment, the temporal transformer will learn the temporal dependency of the hidden layer vector output by the spatial transformer, while capturing the impact of long-term environmental data on tomato yield. First, in order to learn the temporal dependency, the present application similarly inserts a learnable temporal classification marker

[0046]

[0047] in It is a variable temporal position encoding, similar to the spatial position encoding mentioned above. At the same time, this application also proposes a similar temporal multi-head attention mechanism (T-MHA):

[0048]

[0049] Among them, y l represents long-term environmental data, π t (·) represents the linear projection function. is a learnable parameter matrix. The final result of this part is The result is a hidden layer vector that contains both the time information and the environmental information on tomato yield.t Represents the second fusion feature.

[0050] Step S102322: predicting the tomato yield based on the second fusion feature to obtain the tomato yield prediction result.

[0051] In this embodiment, v t Input into a linear classifier to get the predicted tomato yield:

[0052]

[0053] Where W and b are learnable parameter matrices. It is the tomato yield prediction result.

[0054] In one embodiment, the trained tomato yield prediction model is obtained according to the following steps S103 to S104:

[0055] Step S103: Obtain an initialized tomato yield prediction model and sample data set.

[0056] In this embodiment, the sample data set includes a training set and a test set. The training set is used to train the initialized tomato yield prediction model, and the test set is used to test the trained tomato yield prediction model to ensure the accuracy of the model. The ratio of the training set to the test set is nine to one.

[0057] Step S104: training the initialized tomato yield prediction model based on the sample data set to obtain the trained tomato yield prediction model.

[0058] In one embodiment, the sample data set includes historical greenhouse image data of tomatoes, historical short-term environmental data, historical long-term environmental data and historical tomato yield data. Step S104 further includes steps S1041 to S1043:

[0059] Step S1041: inputting the historical greenhouse image data of tomatoes, the historical short-term environmental data and the historical long-term environmental data into the initialized tomato yield prediction model to obtain the current predicted tomato yield data.

[0060] Step S1042: Obtain a loss function based on the currently predicted tomato yield data and the historical tomato yield data.

[0061] In this embodiment, the loss function of the model is calculated as the actual tomato yield z and the predicted yield of Loss function:

[0062]

[0063] Among them, smooth L1 for:

[0064]

[0065] Step S1043: back-propagating the initialized tomato yield prediction model based on the loss function to obtain the trained tomato yield prediction model.

[0066] In this embodiment, the initialized tomato yield prediction model is back-propagated through the loss function to update the parameter matrix, thereby making the output of the model more accurate. When the model reaches the preset accuracy, the training is stopped to obtain the trained tomato yield prediction model.

[0067] Based on the above steps S101-S102, the greenhouse image data, short-term environmental data and long-term environmental data of tomatoes are obtained, and the greenhouse image data, short-term environmental data and long-term environmental data of tomatoes are input into the trained tomato yield prediction model to obtain the tomato yield prediction result. Through the above configuration, the present application inputs the greenhouse image data of tomatoes obtained in real time, and the short-term environmental data and long-term environmental data into the trained tomato yield prediction model to perform a more comprehensive prediction to obtain the tomato yield prediction result. Therefore, the present application improves the accuracy of predicting tomato yield while ensuring timeliness. Thereby, the subsequent picking and sales work can be carried out more effectively and reasonably, ensuring the rational development of agricultural work.

[0068] The step division of the above methods is only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this patent; adding insignificant modifications to the algorithm or process or introducing insignificant designs without changing the core design of the algorithm and process are all within the scope of protection of this patent.

[0069] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0070] In addition, some embodiments of the present application also provide an electronic device. The electronic device may be a digital computer in various forms, such as a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, etc. The electronic device may also be a mobile device in various forms, such as a personal digital processing, a cellular phone, a smart phone, a wearable device, and other similar computing devices.

[0071] The electronic device includes: one or more processors; and a memory storing computer program instructions, wherein when the computer program instructions are executed, the processor executes the steps of the method provided in any one or more of the above embodiments. Figure 2 An exemplary structural diagram of the electronic device is disclosed. Figure 2 As shown, the electronic device includes: one or more processors 1101, memory 1102, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses, and can be installed on a common mainboard or installed in other ways as needed. The processor can process instructions executed in the electronic device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple electronic devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Among them, the components shown in this article, their connections and relationships, and their functions are only used as examples, and are not intended to limit the implementation of the present application described and / or required herein.

[0072] The electronic device may further include: an input device 1103 and an output device 1104. The processor 1101, the memory 1102, the input device 1103 and the output device 1104 may be connected via a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0073] The input device 1103 can receive input digital or character information, and generate key signal input related to the user settings and function control of the electronic device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick and other input devices. The output device 1104 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The display device may include, but is not limited to, a liquid crystal display (LCD), a light emitting diode (LED) display and a plasma display. In some embodiments, the display device may be a touch screen.

[0074] To provide interaction with a user, the electronic device may be a computer. The computer has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball), through which the user can provide input to the computer. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0075] In the embodiments of the present application, a computer program / instruction is stored on a computer-readable medium, and when the computer program / instruction is executed by a processor, the steps of the method provided by any one or more of the above embodiments are implemented. The computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently without being assembled into the device. The above computer-readable medium carries one or more computer-readable instructions.

[0076] The memory 1102 can be used as a non-transient computer-readable storage medium, which can be used to store non-transient software programs, non-transient computer executable programs and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transient software programs, instructions and modules stored in the memory 1102, so as to implement the program instructions / modules corresponding to the method provided by any one or more embodiments in the embodiments of the present application.

[0077] The memory 1102 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 1102 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory 1102 may optionally include a memory remotely arranged relative to the processor 1101, and these remote memories may be connected to the electronic device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0078] It should be noted that the computer-readable medium described in the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable 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 of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (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. In the present application, a computer-readable medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device.

[0079] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, modules of programs or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0080] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).

[0081] In the above-described embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. For example, it can be implemented by using an application specific integrated circuit (ASIC), a general-purpose computer or any other similar hardware device. In certain embodiments, the software program of the present application can be executed by a processor to implement the above steps or functions. Similarly, the software program of the present application (including relevant data structures) can be stored in a computer-readable recording medium, for example, a RAM memory, a magnetic or optical drive or a floppy disk and similar devices. In addition, some steps or functions of the present application can be implemented by hardware, for example, as a circuit that cooperates with a processor to perform each step or function.

[0082] The computer program product provided in the embodiment of the present application includes one or more computer programs / instructions, and when the computer program / instructions are executed by the processor, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive so lid state di sk (SSD)), etc.

[0083] The flow chart or block diagram in the accompanying drawings shows the possible architecture, function and operation of the equipment, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated system for hardware that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0084] The scope of the present application is defined by the appended claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. Any figure mark in the claims should not be regarded as limiting the claims involved. In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the device claim may also be implemented by one unit or device through software or hardware. The words "first", "second", etc. are only used to distinguish the description, and do not indicate any particular order, nor can they be understood as indicating or implying relative importance.

[0085] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily mention changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims, and the above embodiments should be regarded as exemplary and non-restrictive.

Claims

1. A method for predicting tomato yield, characterized in that: The method comprises: Obtain greenhouse image data, short-term environmental data, and long-term environmental data of tomatoes; The greenhouse image data of tomatoes, the short-term environmental data and the long-term environmental data are input into the trained tomato yield prediction model to obtain a tomato yield prediction result.

2. The tomato yield prediction method according to claim 1, characterized in that: The step of inputting the greenhouse image data of tomatoes, the short-term environmental data and the long-term environmental data into the trained tomato yield prediction model to obtain a tomato yield prediction result includes: Extracting features from the greenhouse image data of the tomatoes and the short-term environmental data to obtain feature data of the greenhouse image data of the tomatoes and feature data of the short-term environmental data; Performing feature fusion on the feature data of the greenhouse image data of tomatoes and the feature data of the short-term environmental data to obtain a first fusion feature; Based on the first fusion feature and the long-term environmental data, a tomato yield prediction result is obtained.

3. The tomato yield prediction method according to claim 2, characterized in that: The obtaining of a tomato yield prediction result based on the first fusion feature and the long-term environmental data further includes: Performing global spatial features based on the first fusion features to obtain global spatial features of the greenhouse; The tomato yield prediction result is obtained based on the global spatial characteristics of the greenhouse and the long-term environmental data.

4. The tomato yield prediction method according to claim 3, characterized in that: The step of obtaining the tomato yield prediction result based on the global spatial characteristics of the greenhouse and the long-term environmental data further includes: Performing feature fusion on the global spatial feature of the greenhouse and the long-term environmental data to obtain a second fused feature; The tomato yield is predicted based on the second fusion feature to obtain the tomato yield prediction result.

5. The tomato yield prediction method according to claim 1, characterized in that: Also includes: The short-term environmental data includes short-term soil pH data, short-term temperature data, and short-term humidity data; The long-term environmental data includes long-term soil pH data, long-term temperature data, and long-term humidity data.

6. The tomato yield prediction method according to claim 1, characterized in that: The method further includes obtaining the trained tomato yield prediction model according to the following steps: Obtain the initialized tomato yield prediction model and sample data set; Training the initialized tomato yield prediction model based on the sample data set to obtain the trained tomato yield prediction model; The sample data set includes a training set and a test set.

7. The tomato yield prediction method according to claim 6, characterized in that: The sample data set includes historical greenhouse image data of tomatoes, historical short-term environmental data, historical long-term environmental data and historical tomato yield data; The initialized tomato yield prediction model is trained based on the sample data set to obtain the trained tomato yield prediction model, and further includes Inputting the historical greenhouse image data of tomatoes, the historical short-term environmental data and the historical long-term environmental data into the initialized tomato yield prediction model to obtain the current predicted tomato yield data; Obtaining a loss function based on the currently predicted tomato yield data and the historical tomato yield data; The initialized tomato yield prediction model is back-propagated based on the loss function to obtain the trained tomato yield prediction model.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as claimed in any one of claims 1 to 7.

9. A computer readable medium having a computer program / instructions stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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