Cultivated land resource early warning system based on artificial intelligence

Abstract

The invention relates to the technical field of resource early warning, in particular to a cultivated land resource early warning system based on artificial intelligence. Comprising a data acquisition module used for acquiring geographic information data of a cultivated land parcel, a data preprocessing module used for cleaning the data, a feature calculation module used for calculating the average area and the shape index of the cultivated land parcel data, and an early warning judgment module used for generating a cultivated land fragmentation early warning signal. And the early warning output module is used for outputting the cultivated land fragmentation early warning signal, and the early warning output module comprises a visual display unit for displaying the cultivated land fragmentation condition and the early warning information. All units of the early warning judgment module cooperate with one another, operations such as threshold value reading, data receiving validity checking and threshold value comparison are strictly carried out, and an early warning signal is generated only when the average area is smaller than the average area threshold value and the shape index exceeds the shape index threshold value, namely the cultivated land fragmentation degree reaches a preset standard; the situations of false alarm and missing alarm are effectively avoided, and the early warning result is accurate and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of resource early warning, and in particular to a farmland resource early warning system based on artificial intelligence. Background Art

[0002] In today's society, the quantity and quality of cultivated land resources, as an important material basis for human survival and development, are facing severe challenges from many factors. On the one hand, the rapid advancement of urbanization has led to the occupation of a large amount of cultivated land for projects such as urban construction, infrastructure development and industrial expansion. On the other hand, the transformation of agricultural production methods, the imperfection of the rural land transfer system and the decentralization of land ownership have led to a fragmented trend in the spatial distribution of cultivated land.

[0003] Traditional methods of surveying and monitoring cultivated land resources mainly rely on manual field surveys and simple geographic information system (GIS) technology. Although manual field surveys can obtain relatively accurate field information, they are inefficient, costly, and difficult to achieve real-time and dynamic monitoring of large areas of cultivated land. Early GIS technology was relatively limited in data processing and analysis capabilities. It was difficult to conduct in-depth and accurate quantitative analysis of complex cultivated land spatial forms and changes, and it was impossible to timely and effectively discover problems such as cultivated land fragmentation and issue early warnings.

[0004] With the continuous development and popularization of satellite remote sensing technology, the acquisition of high-resolution satellite image data has become easier and more frequent. These satellite images can cover large areas and provide rich spatial distribution and morphological information of cultivated land. However, relying solely on the intuitive interpretation of satellite remote sensing data, it is difficult to automatically and accurately identify and quantify the characteristic parameters of cultivated land, such as the average area and shape index of the plot, and cannot meet the needs of precise cultivated land resource management.

[0005] At the same time, artificial intelligence technology has made significant breakthroughs and progress in the fields of image recognition, data analysis and pattern classification. Artificial intelligence technologies such as deep learning algorithms have powerful feature learning and automatic pattern recognition capabilities, and can efficiently process and analyze complex geographic spatial data to extract valuable information and laws. Combining satellite remote sensing technology with artificial intelligence technology provides new opportunities and technical means for realizing automated and intelligent monitoring and early warning of cultivated land resources.

[0006] In summary, under the background that cultivated land resources are facing many pressures and existing technical means have limitations, there is an urgent practical need to develop a cultivated land resources early warning system. Summary of the invention

[0007] In view of the above-mentioned shortcomings of the prior art, the present invention provides a farmland resource early warning system based on artificial intelligence, which can effectively solve the problems mentioned in the prior art.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: The present invention provides an artificial intelligence-based cultivated land resource early warning system, comprising: a data acquisition module, used to obtain geographic information data of cultivated land plots, wherein the geographic information data includes boundary coordinate data of cultivated land plots; a data preprocessing module, used to clean, denoise and format convert the collected geographic information data to obtain standardized cultivated land plot data; a feature calculation module, which calculates the average area and shape index of the cultivated land plot data based on an artificial intelligence algorithm, wherein the shape index is obtained by a preset irregular shape quantization algorithm according to the boundary coordinate information of the cultivated land plot; an early warning judgment module, which pre-stores at least one set of early warning thresholds, wherein the early warning thresholds include an average area threshold and a shape index threshold, and when the calculated average area is less than the average plot area threshold and the shape index exceeds the shape index threshold, a cultivated land fragmentation early warning signal is generated; an early warning output module, which is used to output the cultivated land fragmentation early warning signal, and the early warning output module includes a visual display unit to intuitively display the cultivated land fragmentation situation and early warning information.

[0009] Furthermore, the data acquisition module includes a satellite remote sensing data receiving unit and a field surveying and mapping data acquisition unit; After the system is initialized, the satellite remote sensing data receiving unit starts a satellite remote sensing data receiving program, which establishes a connection with a remote sensing satellite at a predetermined time interval to obtain high-resolution image data of cultivated land areas taken by the satellite; the image data is stored in a local cache area, and a preliminary data integrity check is performed to verify the file header information, data size and image features. If the data is damaged or incomplete, a receiving program request is resent to the satellite; The field surveying and mapping data collection unit uses a GPS device to record boundary coordinate data according to a pre-planned sampling area and route, stores the collected boundary coordinate data in a local cache area, and matches it with the satellite remote sensing data through timestamps and geographic location information.

[0010] Furthermore, the processing steps of the data preprocessing module include: S100, data cleaning: after obtaining the boundary coordinate data from the data acquisition module, check whether the number of coordinate points of each plot meets the preset minimum requirement by traversing the data records. If it is found that the number of coordinate points of a plot is less than the minimum requirement, mark the plot as invalid data and delete it; Compare the coordinate data of the same plot in the satellite remote sensing data receiving unit and the field mapping data collection unit, and use the spatial matching algorithm to fit the boundary based on satellite image interpretation with the field mapping boundary. If the deviation between the two exceeds the predetermined threshold, combine the characteristics of the surrounding plots and terrain information, and adjust the boundary through manual intervention or least squares method to make the data consistent. S101, denoising: using a filtering algorithm to smooth the noise in the boundary coordinate data, the boundary coordinate data is aggregated into a boundary coordinate sequence, a moving average filtering algorithm is used, a suitable window size is set, and the average value of the coordinate points in the window is calculated to replace the value of the central coordinate point, so as to form a new boundary coordinate sequence; S102, converting the cleaned and denoised boundary coordinate data from the original data acquisition format into a unified Shapefile format.

[0011] Furthermore, the average area calculation method in the feature calculation module is: First, the boundary coordinate data of each cultivated land plot is read from the preprocessed data and regarded as a closed polygon, which consists of n boundary coordinates. Composition, for each cultivated land plot area , the calculation formula is: ; in, represents the area of ​​the jth cultivated land plot. The formula is used to calculate the area of ​​all cultivated land plots in turn. Suppose there are m plots, the areas of the cultivated land plots are , , , ..., , then the total area of ​​all cultivated land plots for:

[0012] Then the total area Divide by the number of plots to get the average plot size , the calculation formula is: ; Finally, the average plot area Transmitted to the early warning judgment module.

[0013] Furthermore, the shape index calculation method in the feature calculation module is: First, the perimeter P of each cultivated land plot is set to the boundary coordinates , the distance between adjacent boundary coordinates The calculation formula is: ; The calculation formula for the perimeter P of the cultivated land plot is: ; Then combine the area of ​​each cultivated land plot , calculate the shape index C, the calculation formula is: ; Among them, the value range of the shape index C is 0-1, and the shape index represents the regularity of the cultivated land plot. The closer the shape index C is to 1, the more regular the shape is, and the closer it is to 0, the more irregular the shape is. Finally, the shape index C is transmitted to the early warning judgment module.

[0014] Further, the warning judgment module includes a threshold reading unit, a data receiving unit, a threshold comparison unit, a warning signal generation and transmission unit and a warning event recording unit; The threshold reading unit stores a preset average area threshold and shape index threshold , the average area of ​​the feature calculation module is received by the early warning judgment module and shape index C data, the average area threshold and shape index threshold Perform extraction; The data receiving unit establishes a communication interface or data transmission channel with the feature calculation module, and implements data transmission by using a message queue, function callback or shared memory. When receiving the average area sent by the feature calculation module When the shape index C data is obtained, the validity of the data is checked, including checking the average area Is it a non-negative value, and is the shape index C between 0 and 1? If it passes the validity check, the average area and shape index C are transmitted to the threshold comparison unit for threshold comparison judgment; otherwise, the data reception error information is recorded and the feature calculation module is notified to resend the data; The threshold comparison unit receives the average area The shape index C data is compared with the threshold, and the average area is first determined Is it less than the average area threshold? If the condition is met, continue to determine whether the shape index C is less than the shape index threshold ; If the above two conditions are met at the same time, that is, the degree of farmland fragmentation reaches the warning standard, the warning signal generation operation is executed; otherwise, no operation is performed, and the next data reception and comparison judgment is waited; The warning signal generation and transmission unit generates a warning signal for farmland fragmentation after the threshold comparison unit determines that the degree of farmland fragmentation reaches the warning standard, and the warning signal includes a warning level, a warning time, and a farmland plot related to the triggering warning data; When generating the warning signal, the warning event recording unit starts the warning event recording process to obtain the information of the plot that triggers the warning, including the plot location, plot area, shape index and warning time.

[0015] Furthermore, after receiving the farmland fragmentation warning signal transmitted by the warning judgment module, the visualization display unit starts the visualization program, marks the farmland plots with a higher degree of fragmentation with a prominent color, and displays the plot number, area, and shape index information at the same time.

[0016] Furthermore, the artificial intelligence algorithm in the feature calculation module adopts a deep learning algorithm to extract and calculate the features of the cultivated land plots by constructing a multi-layer neural network. The neural network is trained with a large amount of labeled cultivated land plot sample data to improve the accuracy and efficiency of the average area and shape index calculation.

[0017] Furthermore, the early warning output module also includes a communication unit for sending the farmland fragmentation early warning signal to the terminal equipment of relevant land management departments, agricultural production organizations or farmers, and the communication unit supports wireless communication protocols, including but not limited to 4G, 5G networks and Wi-Fi communications.

[0018] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects: (1) The data acquisition module of the present invention integrates a satellite remote sensing data receiving unit and a field surveying and mapping data acquisition unit. The two cooperate with each other. Satellite remote sensing obtains high-resolution image data of the cultivated land area at a predetermined time interval. Field surveying and mapping uses GPS equipment to record boundary coordinate data. By matching the timestamp and geographic location information, the collected data can cover cultivated land information from different angles, thereby ensuring the comprehensiveness of the data.

[0019] (2) The various units of the early warning judgment module of the present invention work together to strictly perform operations such as threshold reading, data reception validity check, and threshold comparison. An early warning signal is generated only when the average area is less than the average area threshold and the shape index exceeds the shape index threshold, that is, when the degree of cultivated land fragmentation reaches the preset standard. This effectively avoids false alarms and missed alarms, making the early warning results accurate and reliable. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 It is a system structure flow chart of the present invention. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] The present invention will be further described below in conjunction with the embodiments.

[0024] Example: Reference Figure 1 , an artificial intelligence-based cultivated land resource early warning system, comprising: a data acquisition module, used to obtain geographic information data of cultivated land plots, the geographic information data including boundary coordinate data of cultivated land plots; a data preprocessing module, used to clean, denoise and format convert the collected geographic information data to obtain standardized cultivated land plot data; a feature calculation module, based on an artificial intelligence algorithm to calculate the average area and shape index of cultivated land plot data, wherein the shape index is obtained by a preset irregular shape quantization algorithm according to the boundary coordinate information of the cultivated land plot; an early warning judgment module, pre-stored with at least one set of early warning thresholds, the early warning thresholds including an average area threshold and a shape index threshold, when the calculated average area is less than the average plot area threshold and the shape index exceeds the shape index threshold, a cultivated land fragmentation early warning signal is generated; an early warning output module, used to output a cultivated land fragmentation early warning signal, the early warning output module includes a visual display unit to intuitively display the cultivated land fragmentation situation and early warning information.

[0025] Further, the data acquisition module includes a satellite remote sensing data receiving unit and a field surveying and mapping data acquisition unit; After the system is initialized, the satellite remote sensing data receiving unit starts the satellite remote sensing data receiving program. The program establishes a connection with the remote sensing satellite at a predetermined time interval to obtain high-resolution image data of the cultivated land area taken by the satellite. The image data is stored in the local cache area, and a preliminary data integrity check is performed to verify the file header information, data size and image features. If the data is damaged or incomplete, the receiving program request is resent to the satellite. The field surveying and mapping data collection unit uses GPS equipment to record boundary coordinate data according to the pre-planned sampling area and route, stores the collected boundary coordinate data in a local cache area, and matches it with the satellite remote sensing data through timestamps and geographic location information.

[0026] Furthermore, the processing steps of the data preprocessing module include: S100, data cleaning: after obtaining the boundary coordinate data from the data acquisition module, check whether the number of coordinate points of each plot meets the preset minimum requirement by traversing the data records. If it is found that the number of coordinate points of a plot is less than the minimum requirement, mark the plot as invalid data and delete it; Compare the coordinate data of the same plot in the satellite remote sensing data receiving unit and the field mapping data collection unit, and use the spatial matching algorithm to fit the boundary based on satellite image interpretation with the field mapping boundary. If the deviation between the two exceeds the predetermined threshold, combine the characteristics of the surrounding plots and terrain information, and adjust the boundary through manual intervention or least squares method to make the data consistent. S101, denoising: using a filtering algorithm to smooth the noise in the boundary coordinate data, the boundary coordinate data is aggregated into a boundary coordinate sequence, a moving average filtering algorithm is used, a suitable window size is set, and the average value of the coordinate points in the window is calculated to replace the value of the central coordinate point, so as to form a new boundary coordinate sequence; S102, converting the cleaned and denoised boundary coordinate data from the original data acquisition format into a unified Shapefile format.

[0027] Furthermore, the average area calculation method in the feature calculation module is: First, read the boundary coordinate data of each cultivated land plot from the preprocessed data and treat it as a closed polygon. The closed polygon consists of n boundary coordinates. Composition, for each cultivated land plot area , the calculation formula is: ; in, represents the area of ​​the jth cultivated land plot. The formula is used to calculate the area of ​​all cultivated land plots in turn. Suppose there are m plots, the areas of the cultivated land plots are , , , ..., , then the total area of ​​all cultivated land plots for: ; Then the total area Divide by the number of plots to get the average plot size , the calculation formula is: ; Finally, the average plot area Transmitted to the early warning judgment module.

[0028] Furthermore, the shape index calculation method in the feature calculation module is: First, the perimeter P of each cultivated land plot is set to the boundary coordinates , the distance between adjacent boundary coordinates The calculation formula is: ; The calculation formula for the perimeter P of the cultivated land plot is: ; Then combine the area of ​​each cultivated land plot , calculate the shape index C, the calculation formula is: ; The value range of the shape index C is 0-1. The shape index indicates the regularity of the cultivated land. The closer the shape index C is to 1, the more regular the shape is, and the closer it is to 0, the more irregular the shape is. Finally, the shape index C is transmitted to the early warning judgment module.

[0029] Further, the warning judgment module includes a threshold reading unit, a data receiving unit, a threshold comparison unit, a warning signal generation and transmission unit and a warning event recording unit; The threshold reading unit stores the preset average area threshold and shape index threshold , the average area of ​​the feature calculation module is received by the early warning judgment module and shape index C data, the average area threshold and shape index threshold Perform extraction; The data receiving unit establishes a communication interface or data transmission channel with the feature calculation module, and uses message queue, function callback or shared memory to realize data transmission. When receiving the average area sent by the feature calculation module When the shape index C data is obtained, the validity of the data is checked, including checking the average area Is it a non-negative value, and is the shape index C between 0 and 1? If it passes the validity check, the average area and shape index C are transmitted to the threshold comparison unit for threshold comparison judgment; otherwise, the data reception error information is recorded and the feature calculation module is notified to resend the data; The threshold comparison unit receives the average area The shape index C data is compared with the threshold, and the average area is first determined Is it less than the average area threshold? If the condition is met, continue to determine whether the shape index C is less than the shape index threshold ; If the above two conditions are met at the same time, that is, the degree of farmland fragmentation reaches the warning standard, the warning signal generation operation is executed; otherwise, no operation is performed, and the next data reception and comparison judgment is waited; The warning signal generation and transmission unit generates a warning signal for farmland fragmentation after the threshold comparison unit determines that the degree of farmland fragmentation reaches the warning standard. The warning signal includes a warning level, a warning time, and farmland plots related to the triggering warning data. When generating the warning signal, the warning event recording unit starts the warning event recording process to obtain the information of the plot that triggered the warning, including the plot location, plot area, shape index and warning time.

[0030] Furthermore, after the visualization display unit receives the farmland fragmentation warning signal transmitted by the warning judgment module, it starts the visualization program, marks the farmland plots with a higher degree of fragmentation with a prominent color, and displays the plot number, area, and shape index information at the same time.

[0031] Furthermore, the artificial intelligence algorithm in the feature calculation module adopts a deep learning algorithm to extract and calculate the features of cultivated land plots by constructing a multi-layer neural network. The neural network is trained with a large amount of labeled cultivated land plot sample data to improve the accuracy and efficiency of the average area and shape index calculation.

[0032] Furthermore, the early warning output module also includes a communication unit for sending farmland fragmentation early warning signals to relevant land management departments, agricultural production organizations or farmers' terminal devices. The communication unit supports wireless communication protocols, including but not limited to 4G, 5G networks and Wi-Fi communications.

[0033] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A farmland resource early warning system based on artificial intelligence, characterized in that: include: A data acquisition module, used to obtain geographic information data of the cultivated land parcel, wherein the geographic information data includes boundary coordinate data of the cultivated land parcel; The data preprocessing module is used to clean, remove noise and convert the format of the collected geographic information data to obtain standardized cultivated land plot data; A feature calculation module, which calculates the average area and shape index of the cultivated land plot data based on an artificial intelligence algorithm, wherein the shape index is obtained according to the boundary coordinate information of the cultivated land plot through a preset irregular shape quantization algorithm; An early warning judgment module pre-stores at least one set of early warning thresholds, wherein the early warning thresholds include an average area threshold and a shape index threshold, and generates a farmland fragmentation early warning signal when the calculated average area is less than the average plot area threshold and the shape index exceeds the shape index threshold; The early warning output module is used to output the farmland fragmentation early warning signal. The early warning output module includes a visual display unit to display the farmland fragmentation situation and early warning information.

2. The artificial intelligence-based farmland resource early warning system according to claim 1 is characterized in that: The data acquisition module includes a satellite remote sensing data receiving unit and a field surveying and mapping data acquisition unit; After the system is initialized, the satellite remote sensing data receiving unit starts a satellite remote sensing data receiving program, which establishes a connection with a remote sensing satellite at a predetermined time interval to obtain high-resolution image data of cultivated land areas taken by the satellite; the image data is stored in a local cache area, and a preliminary data integrity check is performed to verify the file header information, data size and image features. If the data is damaged or incomplete, a receiving program request is resent to the satellite; The field surveying and mapping data collection unit uses a GPS device to record boundary coordinate data according to a pre-planned sampling area and route, stores the collected boundary coordinate data in a local cache area, and matches it with the satellite remote sensing data through timestamps and geographic location information.

3. The artificial intelligence-based farmland resource early warning system according to claim 1 is characterized in that: The processing steps of the data preprocessing module include: S100, data cleaning: after obtaining the boundary coordinate data from the data acquisition module, check whether the number of coordinate points of each plot meets the preset minimum requirement by traversing the data records. If it is found that the number of coordinate points of a plot is less than the minimum requirement, mark the plot as invalid data and delete it; Compare the coordinate data of the same plot in the satellite remote sensing data receiving unit and the field mapping data collection unit, and use the spatial matching algorithm to fit the boundary based on satellite image interpretation with the field mapping boundary. If the deviation between the two exceeds the predetermined threshold, combine the characteristics of the surrounding plots and terrain information, and adjust the boundary through manual intervention or least squares method to make the data consistent. S101, denoising: using a filtering algorithm to smooth the noise in the boundary coordinate data, the boundary coordinate data is aggregated into a boundary coordinate sequence, a moving average filtering algorithm is used, a suitable window size is set, and the average value of the coordinate points in the window is calculated to replace the value of the central coordinate point, so as to form a new boundary coordinate sequence; S102, converting the cleaned and denoised boundary coordinate data from the original data acquisition format into a unified Shapefile format.

4. The artificial intelligence-based farmland resource early warning system according to claim 1 is characterized in that: The average area calculation method in the feature calculation module is: First, the boundary coordinate data of each cultivated land plot is read from the preprocessed data and regarded as a closed polygon, which consists of n boundary coordinates. Composition, for each cultivated land plot area , the calculation formula is: ； in, represents the area of ​​the jth cultivated land plot. The formula is used to calculate the area of ​​all cultivated land plots in turn. Suppose there are m plots, the areas of the cultivated land plots are , , , ..., , then the total area of ​​all cultivated land plots for: ； Then the total area Divide by the number of plots to get the average plot size , the calculation formula is: ； Finally, the average plot area Transmitted to the early warning judgment module.

5. The artificial intelligence-based farmland resource early warning system according to claim 1 is characterized in that: The shape index calculation method in the feature calculation module is: First, the perimeter P of each cultivated land plot is set to the boundary coordinates , the distance between adjacent boundary coordinates The calculation formula is: ； The calculation formula for the perimeter P of the cultivated land plot is: ； Then combine the area of ​​each cultivated land plot , calculate the shape index C, the calculation formula is: ； Among them, the value range of the shape index C is 0-1, and the shape index represents the regularity of the cultivated land plot. The closer the shape index C is to 1, the more regular the shape is, and the closer it is to 0, the more irregular the shape is. Finally, the shape index C is transmitted to the early warning judgment module.

6. The artificial intelligence-based farmland resource early warning system according to claim 1 is characterized in that: The warning judgment module includes a threshold reading unit, a data receiving unit, a threshold comparison unit, a warning signal generation and transmission unit and a warning event recording unit; The threshold reading unit stores a preset average area threshold and shape index threshold , the average area of ​​the feature calculation module is received by the early warning judgment module and shape index C data, the average area threshold and shape index threshold Perform extraction; The data receiving unit establishes a communication interface or data transmission channel with the feature calculation module, and implements data transmission by using a message queue, function callback or shared memory. When receiving the average area sent by the feature calculation module When the shape index C data is obtained, the validity of the data is checked, including checking the average area Is it a non-negative value, and is the shape index C between 0 and 1? If the validity check passes, the average area and shape index C are transmitted to the threshold comparison unit for threshold comparison judgment; Otherwise, record the data receiving error information and notify the feature calculation module to resend the data; The threshold comparison unit receives the average area The shape index C data is compared with the threshold, and the average area is first determined Is it less than the average area threshold? If the condition is met, continue to determine whether the shape index C is less than the shape index threshold ; If the above two conditions are met at the same time, that is, the degree of farmland fragmentation reaches the warning standard, the warning signal generation operation is executed; otherwise, no operation is performed, and the next data reception and comparison judgment is waited; The warning signal generation and transmission unit generates a warning signal for farmland fragmentation after the threshold comparison unit determines that the degree of farmland fragmentation reaches the warning standard, and the warning signal includes a warning level, a warning time, and a farmland plot related to the triggering warning data; When generating the warning signal, the warning event recording unit starts the warning event recording process to obtain the information of the plot that triggers the warning, including the plot location, plot area, shape index and warning time.

7. The artificial intelligence-based farmland resource early warning system according to claim 1 is characterized in that: After receiving the farmland fragmentation warning signal transmitted by the warning judgment module, the visualization display unit starts the visualization program, marks the farmland plots with a higher degree of fragmentation with a prominent color, and displays the number, area, and shape index information of the plots.

8. The artificial intelligence-based farmland resource early warning system according to claim 1 is characterized in that: The artificial intelligence algorithm in the feature calculation module adopts a deep learning algorithm to extract and calculate the features of the cultivated land plots by constructing a multi-layer neural network.

9. The artificial intelligence-based farmland resource early warning system according to claim 1 is characterized in that: The early warning output module also includes a communication unit, which is used to send the farmland fragmentation early warning signal to the terminal equipment of the relevant land management department, agricultural production organization or farmer, and the communication unit supports wireless communication protocols, including 4G, 5G networks and Wi-Fi communications.

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