Supervision and adjustment system and method for drought-resistant breeding of sugarcanes

Abstract

The invention relates to the technical field of agricultural biotechnology and intelligent monitoring, and discloses a supervision and adjustment system and method.The supervision and adjustment system comprises a breeding unit, an environment simulation unit, a breeding monitoring unit, a data model unit and a data analysis unit. By integrating environment simulation, multi-parameter monitoring and intelligent regulation and control technologies, a closed-loop breeding platform capable of accurately reproducing different drought scenes is constructed, and the core problems that traditional drought-resistant breeding depends on natural conditions, multi-site sampling efficiency is low, data acquisition is inaccurate and the like are solved; the method is suitable for sugarcane germplasm resource screening, drought-resistant gene function verification and optimal planting parameter suggestion of corresponding areas.

Description

Technical Field

[0001] The present invention relates to the fields of agricultural biotechnology and intelligent monitoring technology, and more particularly to a regulatory adjustment system and method for drought-resistant sugarcane breeding. Background Art

[0002] Sugarcane belongs to the Gramineae family and is a perennial tall herbaceous plant. It is an important sugar and cash crop globally. When planted, sugarcane prefers warm and humid conditions, with an annual average temperature of 18 - 30°C and a frost-free period of over 240 days. The annual rainfall requirement is about 1200 - 1500 mm, and it needs to be evenly distributed during the growing season. However, due to frequent droughts in the existing environment (according to the IPCC report, the global drought frequency has increased by about 30% in the past 30 years), the traditional main sugarcane production areas (such as Maharashtra in India) have a 40% reduction in production due to drought. Therefore, under this background, drought-resistant sugarcane has gradually come into people's view;

[0003] Among the existing drought-resistant sugarcanes, the corresponding quality sugarcane breeding is mainly carried out through a sugarcane drought-resistant breeding system. However, in the actual operation process of the existing technology, there are still the following defects: limitations of natural environment breeding: long cycle: the natural drought environment is uncertain, which leads to uncertainty in the overall sugarcane breeding process, and thus the overall sugarcane breeding cycle is relatively long; high breeding cost: breeding usually requires a specific breeding experimental field in the existing technology. At the same time, in order to adapt to sugarcane breeding in different environments, it is necessary to purchase breeding experimental fields in multiple places simultaneously, resulting in an increase in the overall breeding cost; second, multi-site sampling and resource waste: the traditional method requires cross-regional point layout experiments, which makes data integration difficult, and the soil types and climate differences in different regions interfere with the accuracy of the results;

[0004] Therefore, there is an urgent need for a regulatory adjustment system and method for drought-resistant sugarcane breeding to solve the above-mentioned technical problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the existing technology, the embodiments of the present invention provide a regulatory adjustment system and method for drought-resistant sugarcane breeding to solve the technical problems raised in the background art.

[0006] To achieve the above object, the present invention provides the following technical solutions: A regulatory adjustment system and method for drought-resistant sugarcane breeding, including.

[0007] In a preferred embodiment, it includes a regulatory adjustment system, and the regulatory adjustment system includes a breeding unit, an environmental simulation unit, a breeding monitoring unit, a data model unit, and a data analysis unit

[0008] The breeding unit is composed of multiple groups of independently controllable breeding greenhouses, which are used to carry sugarcane plants and divide different drought resistance test groups;

[0009] Environmental simulation unit: configured to adjust the temperature, humidity, light intensity, and soil water content in different breeding greenhouses to simulate drought environments with different degrees of drought, wherein the environmental simulation unit is connected to the breeding unit;

[0010] Breeding monitoring unit: configured to collect the sugarcane breeding situation in different breeding environment greenhouses, wherein the breeding monitoring unit is integrated in each group of breeding greenhouses;

[0011] Data model unit: constructs a dynamic mapping relationship between the sugarcane growth state and environmental parameters based on real-time monitoring data, wherein the data model unit is communicatively connected to the breeding monitoring unit;

[0012] Data analysis unit: connected to the data model unit, used to analyze the sugarcane breeding varieties in the best state in different breeding greenhouses, wherein the data analysis unit is connected to the data model unit;

[0013] Each group of breeding greenhouses in the breeding unit plants sugarcane, and the environmental simulation unit adjusts the planting environment in each group of breeding greenhouses in real time; the breeding monitoring unit is used to monitor the growth of sugarcane in each group of breeding greenhouses, and calculates the best sugarcane varieties in each breeding greenhouse through the data model unit and the data analysis unit.

[0014] In a preferred embodiment, the breeding unit includes a designated experimental area, and the multiple groups of breeding greenhouses are arranged in an equidistant array in the designated experimental area. The experimental area is equally divided into multiple rectangular or square sub-areas with the same area. One breeding greenhouse is set in each sub-area, and the spacing error between adjacent sub-areas does not exceed ±2%.

[0015] In a preferred embodiment, the environmental simulation unit includes an environmental regulation module and a drought environment database. The environmental regulation module includes a temperature and humidity regulation device, a light regulation device, and a water regulation device. The temperature and humidity regulation device is used to adjust the temperature and humidity data in multiple groups of breeding greenhouses, the light regulation device is used to adjust the light intensity in multiple groups of breeding greenhouses; the water regulation device is used to adjust the water content in the breeding greenhouses.

[0016] In a preferred embodiment, the drought environment database stores historical drought environment data groups in each sugarcane planting area. The storage period of the historical drought environment data group is 30 consecutive years, and the drought environment data group includes daily average temperature, daily relative humidity, daily cumulative amount of photosynthetically active radiation, and daily rainfall. The drought environment database integrates the drought environment databases of each region and calculates the corresponding drought environment data group mean values generated by the drought environment data groups in each region;

[0017] The environmental regulation module matches the state of adjusting the ecological environment in each breeding greenhouse with the mean value of the drought environment data set corresponding to each region based on the drought environment database.

[0018] In a preferred embodiment, the breeding monitoring unit includes a data acquisition module. Inside the data acquisition module, a weight acquisition device is provided. The weight acquisition device acquires the initial weight g1 of the sugarcane before planting and the final weights g2 of its roots, stems, and leaves after the sugarcane matures, and transmits them to the data model unit.

[0019] The data acquisition module records the soil water content θ per square volume in each breeding greenhouse every day through a capacitive soil moisture sensor arranged in each breeding greenhouse, and transmits it to the data model unit.

[0020] The data acquisition module uses a photosynthesis measuring instrument to collect the adsorption rate Lv of CO2 generated by the breeding sugarcane to be measured in each breeding greenhouse, calculates the m groups of net photosynthetic rates before stress and the net photosynthetic rates after stress relief in each breeding greenhouse within m groups of cycles, with a seven-day interval between each cycle, calculates the m groups of net photosynthetic rates before stress and the net photosynthetic rates after stress relief collected, and obtains the mean value Q1 of the net photosynthetic rate before stress and the mean value Q2 of the net photosynthetic rate after stress relief, and transmits them to the data model unit.

[0021] In a preferred embodiment, the data acquisition module includes a leaf area acquisition module for acquiring the leaf area data S generated on the surface when the third leaf at the top of the sugarcane is in a fully open state.

[0022] The data acquisition module calculates the mean value Q of the net photosynthetic rate of the breeding sugarcane in each breeding greenhouse, and its formula is:

[0023]

[0024] The mean value Q1 of the net photosynthetic rate before stress is the mean value Q of the net photosynthetic rate generated by the third leaf at the top of the sugarcane in the breeding greenhouse in the measurement area within a specific time before the sugarcane in the breeding greenhouse is subjected to drought treatment. The specific time refers to: from 9:00 am to 11:00 am on the day of sunny weather before the sugarcane in the breeding greenhouse is subjected to drought treatment, and the mean value Q of the net photosynthetic rate generated at this time.

[0025] The mean value Q2 of the net photosynthetic rate after stress relief is the mean value Q of the net photosynthetic rate generated by the third leaf at the top of the sugarcane in the breeding greenhouse in the measurement area within a specific time 48 hours after the sugarcane in the breeding greenhouse resumes irrigation. The specific time refers to: from 9:00 am to 11:00 am on the day of sunny weather before the sugarcane in the breeding greenhouse is subjected to drought treatment, and the mean value Q of the net photosynthetic rate generated at this time.

[0026] In a preferred embodiment, the data model unit receives the initial weight g1 of sugarcane before planting, the final weight g2 of leaves, the soil moisture content θ per square volume, the average net photosynthetic rate Q1 before stress, and the average net photosynthetic rate Q2 after stress relief, generated by each group of breeding greenhouses, collected by the breeding monitoring unit, and calculates the drought resistance level DRU of the breeding sugarcane in each group of breeding greenhouses, wherein the sugarcane drought resistance level DRU is calculated as:

[0027] DRU = 0.6 × Ln(WUE) + 0.4 × DR;

[0028] WUE refers to water use efficiency, which is calculated as follows:

[0029] Where y is the number of days in the sugarcane production cycle, Ty is the total area of the sugarcane breeding greenhouse, and Wy is the total water irrigation for sugarcane growth;

[0030] Where DR is the mean periodic drought resilience, and the calculation formula of DR is:

[0031] The data model unit includes a data model graph, which receives the average temperature, average humidity, average light intensity, and average soil moisture content of each breeding greenhouse in real time through the internal monitoring device of the breeding unit, and generates a sugarcane growth environment change trend graph of each group of breeding greenhouses;

[0032] The data analysis unit includes a learning prediction module;

[0033] The learning prediction module uses the data model unit to generate the sugarcane drought resistance level DRU in each group of breeding greenhouses and the sugarcane growth environment change trend chart in each group of breeding greenhouses, and outputs the drought resistance adaptability prediction report and optimal planting parameter recommendations for each sugarcane variety in each breeding greenhouse.

[0034] In a preferred embodiment, a supervision and adjustment method for sugarcane drought-resistant breeding is applied to a supervision and adjustment system for sugarcane drought-resistant breeding, and the method comprises the following steps:

[0035] Step 1: The breeding unit is divided into multiple groups of breeding greenhouses with equal spacing and area in a designated area. A monitoring device is provided inside the breeding unit to monitor the temperature, humidity, light intensity and soil moisture data in each breeding greenhouse;

[0036] Step 2: The environmental simulation unit adjusts the temperature, humidity, light intensity, and soil moisture data in each group of breeding greenhouses based on the drought environment database, and adjusts the ecological environment in each group of breeding greenhouses to match the mean value of the drought environment data group corresponding to each region;

[0037] Step 3: The breeding monitoring unit collects the sugarcane drought resistance grade DRU generated by the sugarcane bred in each group of breeding greenhouses in step 2 and a trend chart of the sugarcane growth environment changes in each group of breeding greenhouses;

[0038] Step 4: The data analysis unit analyzes the drought resistance adaptability prediction report and optimal planting parameter recommendations for each sugarcane variety in each breeding greenhouse based on the sugarcane drought resistance level DRU in each breeding greenhouse and the sugarcane growth environment change trend chart in each breeding greenhouse in step 3.

[0039] Technical effects and advantages of the present invention:

[0040] 1. The present invention provides breeding units, which facilitate the division of breeding units into equally spaced and equally sized units, constructing a standardized experimental matrix and simultaneously conducting multi-region drought environment simulations. Compared with traditional multi-site single-point experiments, the present device has a higher breeding efficiency than traditional breeding methods, achieving "time-space compression" breeding. At the same time, it reduces the land expenditure required for breeding to a certain extent, further reducing the breeding cost.

[0041] 2. The present invention is equipped with a breeding monitoring unit, which facilitates the intuitive selection of each group of breeding sugarcane through the original sugarcane drought resistance rating (DRU) indicator. Compared with the traditional selection method based on manual self-inspection, the present invention has certain efficiency and significantly improves the accuracy of drought resistance trait identification.

[0042] 3. The present invention is equipped with a supervision and adjustment system, which is conducive to building a closed-loop breeding platform that can accurately reproduce different drought scenarios through integrated environmental simulation, multi-parameter monitoring and intelligent control technology. It solves the core problems of traditional drought-resistant breeding such as dependence on natural conditions, low sampling efficiency in multiple locations, and inaccurate data collection. It is suitable for screening sugarcane germplasm resources, verifying the function of drought-resistant genes, and recommending optimal planting parameters for corresponding regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 The figure is an overall flow chart of the supervision and adjustment method for drought-resistant sugarcane breeding of the present invention.

[0044] Figure 2 This is an overall flow chart of the supervision and adjustment system for sugarcane drought-resistant breeding of the present invention. DETAILED DESCRIPTION

[0045] The following will clearly and completely describe the technical solutions in the present invention in combination with the accompanying drawings in the present invention. In addition, the forms of the various structures described in the following embodiments are merely examples, and a supervision and adjustment system and method for sugarcane drought-resistant breeding involved in the present invention are not limited to the various structures described in the following embodiments. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0046] The present invention provides a supervision and adjustment system and method for sugarcane drought-resistant breeding, including.

[0047] Referring Figure 1 As shown, it includes a supervision and adjustment system, and the supervision and adjustment system includes a breeding unit, an environmental simulation unit, a breeding monitoring unit, a data model unit, and a data analysis unit <I

[0048] The breeding unit consists of multiple groups of independently controllable breeding greenhouses, which are used to carry sugarcane plants and divide different drought-resistant test groups;

[0049] The environmental simulation unit: is configured to adjust the temperature, humidity, light intensity, and soil water content in different breeding greenhouses to simulate drought environments with different drought degrees, and the environmental simulation unit is connected to the breeding unit;

[0050] The breeding monitoring unit: is configured to collect the sugarcane breeding situation in different breeding environment greenhouses, and the breeding monitoring unit is integrated in each group of breeding greenhouses;

[0051] The data model unit: constructs a dynamic mapping relationship between the sugarcane growth state and environmental parameters based on real-time monitoring data, and the data model unit is communicatively connected to the breeding monitoring unit;

[0052] The data analysis unit: is connected to the data model unit and is used to analyze the sugarcane breeding varieties in the best state in different breeding greenhouses, and the data analysis unit is connected to the data model unit;

[0053] Sugarcane is planted in each group of breeding greenhouses in the breeding unit, and the environmental simulation unit adjusts the planting environment in each group of breeding greenhouses in real time; the breeding monitoring unit is used to monitor the growth of sugarcane in each group of breeding greenhouses, and through the data model unit and the data analysis unit, a drought resistance adaptability prediction report and optimal planting parameter suggestions for sugarcane varieties in each breeding greenhouse are analyzed;

[0054] The breeding unit includes a designated experimental area, and the multiple groups of breeding greenhouses are arranged in an equidistant array in the designated experimental area. The experimental area is evenly divided into multiple rectangular or square sub-areas with the same area, and one breeding greenhouse is set in each sub-area, and the spacing error between adjacent sub-areas does not exceed ±2%;

[0055] The environmental simulation unit includes an environmental regulation module and a drought environment database. The environmental regulation module includes a temperature and humidity regulation device, a light regulation device, and a water regulation device. Among them, the temperature and humidity regulation device is used to regulate the temperature and humidity data in multiple groups of breeding greenhouses. The light regulation device is used to regulate the light intensity in multiple groups of breeding greenhouses. The water regulation device is used to regulate the water content in the breeding greenhouses.

[0056] In the embodiment of the present application, the breeding monitoring unit collects each group of data generated by the breeding sugarcane in the middle area of each breeding greenhouse, and the number of breeding sugarcane collected is 80-100 plants.

[0057] The drought environment database stores historical drought environment data groups in each sugarcane planting area. The storage period of the historical drought environment data group is 30 consecutive years, and the drought environment data group includes daily average temperature, daily relative humidity, daily cumulative amount of photosynthetically active radiation, and daily rainfall. The drought environment database integrates the drought environment databases of each region and calculates the corresponding mean values of the drought environment data groups generated in each group of regions.

[0058] The internal data of the drought environment database can be updated and replaced by itself.

[0059] Based on the drought environment database, the environmental regulation module adjusts the ecological environment in each group of breeding greenhouses to match the mean values of the corresponding drought environment data groups in each region.

[0060] Refer to Figure 1 As shown, the breeding monitoring unit includes a data acquisition module. Among them, a weight acquisition device is arranged inside the data acquisition module. The weight acquisition device acquires the initial weight g1 of the sugarcane before planting and the final weights g2 of its roots, stems, and leaves after the sugarcane matures, and transmits them to the data model unit.

[0061] The data acquisition module records the soil water content θ in each cubic volume in each group of breeding greenhouses every day through a capacitive soil moisture sensor arranged in each group of breeding greenhouses, and transmits it to the data model unit.

[0062] The data acquisition module uses a photosynthesis measuring instrument to collect the adsorption rate Lv of CO2 generated by the breeding sugarcane to be measured in each group of breeding greenhouses, and calculates the m groups of pre-stress net photosynthetic rates and post-stress-relieved net photosynthetic rates generated in each group of breeding greenhouses within m groups of cycles. The interval between each cycle is seven days. The m groups of pre-stress net photosynthetic rates and post-stress-relieved net photosynthetic rates collected are calculated, and the mean value Q1 of the pre-stress net photosynthetic rate and the mean value Q2 of the post-stress-relieved net photosynthetic rate are obtained and transmitted to the data model unit.

[0063] The data acquisition module includes a leaf area acquisition module for acquiring the leaf area data S generated on the surface of the third leaf at the top of the sugarcane when it is in a fully opened state.

[0064] The data acquisition module calculates the average net photosynthetic rate Q of the sugarcane for breeding in each breeding greenhouse, and its formula is:

[0065]

[0066] The average net photosynthetic rate Q1 before stress is the average net photosynthetic rate Q generated by the third leaf at the top of the sugarcane in the breeding greenhouse within a specific time in the measurement area before the sugarcane in the breeding greenhouse is subjected to drought treatment. The specific time refers to: from 9 am to 11 am on the day of sunny weather before the sugarcane in the breeding greenhouse is subjected to drought treatment, and the average net photosynthetic rate Q generated at this time.

[0067] The average net photosynthetic rate Q2 after stress relief is the average net photosynthetic rate Q generated by the third leaf at the top of the sugarcane in the breeding greenhouse within a specific time in the measurement area 48 hours after the sugarcane in the breeding greenhouse resumes irrigation. The specific time refers to: from 9 am to 11 am on the day of sunny weather before the sugarcane in the breeding greenhouse is subjected to drought treatment, and the average net photosynthetic rate Q generated at this time.

[0068] The data model unit receives the initial weight g1 of the sugarcane before planting, the final weight g2 of the leaves, the soil water content θ per cubic volume, the average net photosynthetic rate Q1 before stress, and the average net photosynthetic rate Q2 after stress relief generated by each group of breeding greenhouses collected by the breeding monitoring unit, and calculates the drought resistance level DRU of the sugarcane for breeding in each group of breeding greenhouses. The calculation of the drought resistance level DRU of the sugarcane is as follows:

[0069] DRU = 0.6×Ln(WUE)+0.4×DR;

[0070] Where WUE refers to the water use efficiency, and its calculation formula is:

[0071] Where y is the number of days in the sugarcane production cycle, Ty is the total area of the sugarcane breeding greenhouse, and Wy is the total water irrigation amount for the growth of the sugarcane.

[0072] Where DR is the average value of the periodic drought resilience, and the calculation formula of DR is:

[0073] The data model unit includes a data model diagram, which receives, in real time through the internal monitoring device of the breeding unit, the average temperature, average humidity, average light intensity, and average soil moisture content generated by each breeding greenhouse every day, and generates a trend chart of the changes in the sugarcane growth environment of each group of breeding greenhouses;

[0074] The data analysis unit includes a learning and prediction module;

[0075] The learning and prediction module uses the drought resistance level DRU of sugarcane in each group of breeding greenhouses and the trend chart of the changes in the sugarcane growth environment in each group of breeding greenhouses generated in the data model unit to output a prediction report on the drought resistance adaptability of sugarcane varieties in each breeding greenhouse and suggestions on the optimal planting parameters.

[0076] Refer to Figure 2 As shown, it includes a supervision and adjustment method for sugarcane drought resistance breeding, which is applied to a supervision and adjustment system for sugarcane drought resistance breeding. The method includes the following steps:

[0077] Step 1: The breeding unit divides a designated area into equal distances and equal areas, dividing it into multiple groups of breeding greenhouses. A monitoring device is arranged inside the breeding unit to monitor the temperature, humidity, light intensity, and soil water content data in each breeding greenhouse;

[0078] Step 2: The environmental simulation unit adjusts the temperature, humidity, light intensity, and soil water content data in each group of breeding greenhouses according to the drought environment database to match the ecological environment in each group of breeding greenhouses with the average value of the corresponding drought environment data group in each area;

[0079] Step 3: The breeding monitoring unit receives the data collected in Step 1 and Step 2, and generates the drought resistance level DRU of the sugarcane bred in each group of breeding greenhouses and a trend chart of the changes in the sugarcane growth environment in each group of breeding greenhouses;

[0080] Step 4: The data analysis unit analyzes a prediction report on the drought resistance adaptability of sugarcane varieties in each breeding greenhouse and suggestions on the optimal planting parameters according to the drought resistance level DRU of the sugarcane in each group of breeding greenhouses and the trend chart of the changes in the sugarcane growth environment in each group of breeding greenhouses in Step 3.

[0081] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more collections of available media. The available medium can be a magnetic medium, an optical medium, or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0082] Those of ordinary skill in the art will appreciate that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are executed in hardware or software depends on the specific application and design constraints of the technical solution. A person skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0083] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0084] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0085] The unit described as a separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] In addition, in each embodiment of the present application, each functional unit may be integrated in a processing unit, may exist separately as individual physical units, or two or more units may be integrated in one unit.

[0087] As mentioned above, this is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed in the present application can easily think of changes or substitutions, which should all be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0088] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A regulatory adjustment system for drought-resistant breeding of sugarcane, characterized in that: Including a regulatory adjustment system, which includes a breeding unit, an environmental simulation unit, a breeding monitoring unit, a data model unit, and a data analysis unit; The breeding unit consists of multiple groups of independently controllable breeding greenhouses, which are used to carry sugarcane plants and divide different drought resistance test groups; The environmental simulation unit is configured to adjust the temperature, humidity, light intensity, and soil water content in different breeding greenhouses to simulate drought environments with different degrees of drought, and the environmental simulation unit is connected to the breeding unit; The breeding monitoring unit is configured to collect the sugarcane breeding conditions in different breeding environment greenhouses, and the breeding monitoring unit is integrated in each group of breeding greenhouses; The data model unit constructs a dynamic mapping relationship between the sugarcane growth state and environmental parameters based on real-time monitoring data, and the data model unit is communicatively connected to the breeding monitoring unit; The data analysis unit is connected to the data model unit and is used to analyze the sugarcane breeding varieties in the best state in different breeding greenhouses, and the data analysis unit is connected to the data model unit; Sugarcane is planted in each group of breeding greenhouses in the breeding unit, and the environmental simulation unit adjusts the planting environment in each group of breeding greenhouses in real time; the breeding monitoring unit is used to monitor the growth of sugarcane in each group of breeding greenhouses, and through the data model unit and the data analysis unit, a drought resistance adaptability prediction report and optimal planting parameter suggestions for the sugarcane varieties in each breeding greenhouse are analyzed.

2. The regulatory adjustment system for drought-resistant sugarcane breeding according to claim 1, characterized in that: The breeding unit includes a designated experimental area, and the multiple groups of breeding greenhouses are arranged in an equidistant array in the designated experimental area. The experimental area is evenly divided into multiple rectangular or square sub-areas with the same area, and one breeding greenhouse is set in each sub-area, and the spacing error between adjacent sub-areas does not exceed ±2%.

3. The regulatory adjustment system for drought-resistant sugarcane breeding according to claim 1, wherein: The environmental simulation unit includes an environmental regulation module and a drought environment database. The environmental regulation module includes a temperature and humidity regulation device, a light regulation device, and a water regulation device. The temperature and humidity regulation device is used to adjust the temperature and humidity data in multiple groups of breeding greenhouses, and the light regulation device is used to adjust the light intensity in multiple groups of breeding greenhouses; the water regulation device is used to adjust the water content in the breeding greenhouses.

4. A regulatory adjustment system for drought-resistant sugarcane breeding according to claim 3, characterized in that: The drought environment database stores historical drought environment data groups in each sugarcane planting area. The storage period of the historical drought environment data group is 30 consecutive years, and the drought environment data group includes daily average temperature, daily relative humidity, daily cumulative amount of photosynthetically active radiation, and daily rainfall. The drought environment database integrates the drought environment databases of each region and calculates the corresponding average value of the drought environment data groups generated in each region's drought environment data groups.

5. The regulatory adjustment system for drought-resistant sugarcane breeding according to claim 4, characterized in that: Based on the drought environment database, the environmental regulation module adjusts the ecological environment in each group of breeding greenhouses to match the average value of the drought environment data groups corresponding to each region.

6. The regulatory adjustment system for drought-resistant sugarcane breeding according to claim 1, characterized in that: The breeding monitoring unit includes a data collection module. Inside the data collection module, there is a weight collection device. The weight collection device collects the initial weight g1 of the sugarcane before planting and the final weights g2 of its roots, stems, and leaves after the sugarcane matures, and transmits them to the data model unit; The data acquisition module records the soil water content θ per square volume in each breeding greenhouse every day through capacitive soil moisture sensors set in each group of breeding greenhouses, and transmits it to the data model unit; The data acquisition module uses a photosynthesis measuring instrument to collect the adsorption rate Lv of CO2 generated by the breeding sugarcane to be measured in each group of breeding greenhouses, calculates the net photosynthetic rate before stress and the net photosynthetic rate after stress relief in m groups within m groups of cycles in each group of breeding greenhouses. The interval between each cycle is seven days, and the m groups of net photosynthetic rates before stress and the net photosynthetic rates after stress relief collected are calculated to obtain the average value Q1 of the net photosynthetic rate before stress and the average value Q2 of the net photosynthetic rate after stress relief, and transmits them to the data model unit.

7. A regulatory adjustment system for drought-resistant sugarcane breeding according to claim 6, characterized in that: The data acquisition module includes a leaf area acquisition module for acquiring the leaf area data S generated on the surface when the third leaf at the top of the sugarcane is in a fully opened state; The data acquisition module calculates the average value Q of the net photosynthetic rate of the breeding sugarcane in each group of breeding greenhouses. The formula is: The average value Q1 of the net photosynthetic rate before stress is the average value Q of the net photosynthetic rate generated by the third leaf at the top of the sugarcane in the measurement area in a specific time before the sugarcane in the breeding greenhouse is subjected to drought treatment. The specific time refers to: from 9:00 am to 11:00 am on the day of sunny weather before the sugarcane in the breeding greenhouse is subjected to drought treatment, and the average value Q of the net photosynthetic rate generated at this time; The average value Q2 of the net photosynthetic rate after stress relief is the average value Q of the net photosynthetic rate generated by the third leaf at the top of the sugarcane in the measurement area in a specific time 48 hours after the sugarcane in the breeding greenhouse resumes irrigation. The specific time refers to: from 9:00 am to 11:00 am on the day of sunny weather before the sugarcane in the breeding greenhouse is subjected to drought treatment, and the average value Q of the net photosynthetic rate generated at this time.

8. A regulatory adjustment system for drought-resistant sugarcane breeding according to claim 7, characterized in that: The data model unit receives the initial weight g1 of the sugarcane before planting, the final weight g2 of the leaves, the soil water content θ per square volume, the average value Q1 of the net photosynthetic rate before stress, and the average value Q2 of the net photosynthetic rate after stress relief generated by each group of breeding greenhouses collected by the breeding monitoring unit, and calculates the drought resistance level DRU of the breeding sugarcane in each group of breeding greenhouses. The calculation of the drought resistance level DRU of the sugarcane is: DRU = 0.6×Ln(WUE)+0.4×DR; Where WUE refers to the water use efficiency, and the calculation formula is: where y is the number of days in the sugarcane production cycle, Ty is the total area of the sugarcane breeding greenhouse, and Wy is the total water irrigation amount for sugarcane growth; Among them, DR is the mean of periodic drought resilience, and the calculation formula of DR is as follows: The data model unit includes a data model diagram. The data model diagram receives the average temperature, average humidity, average light intensity, and average soil water content generated by each breeding greenhouse every day through the internal monitoring device of the breeding unit, and generates a trend chart of the growth environment change of the sugarcane in each group of breeding greenhouses.

9. The regulatory adjustment system for drought-resistant sugarcane breeding according to claim 1, wherein: The data analysis unit includes a learning and prediction module; The learning and prediction module uses the drought resistance level DRU of the sugarcane in each group of breeding greenhouses and the trend chart of the growth environment change of the sugarcane in each group of breeding greenhouses generated in the data model unit to output a prediction report on the drought resistance adaptability of the sugarcane variety in each breeding greenhouse and suggestions on the optimal planting parameters.

10. A regulatory adjustment method for drought-resistant breeding of sugarcane, applied to a regulatory adjustment system for drought-resistant breeding of sugarcane according to any one of claims 1-9, characterized in that, The method includes the following steps: Step 1: The breeding unit divides a designated area into equal-distance and equal-area parts, forming multiple groups of breeding greenhouses. A monitoring device is installed inside the breeding unit to monitor the temperature, humidity, light intensity, and soil moisture content data in each breeding greenhouse. Step 2: The environmental simulation unit adjusts the temperature, humidity, light intensity, and soil moisture content data in each group of breeding greenhouses according to the drought environment database, and adjusts the ecological environment in each group of breeding greenhouses to match the average value of the corresponding drought environment data group in each area. Step 3: The breeding monitoring unit receives the data collected in Step 1 and Step 2, and generates the drought resistance level DRU of the sugarcane bred in each group of breeding greenhouses and the trend chart of the changes in the sugarcane growth environment in each group of breeding greenhouses. Step 4: The data analysis unit analyzes the drought resistance level DRU of the sugarcane in each group of breeding greenhouses and the trend chart of the changes in the sugarcane growth environment in each group of breeding greenhouses in Step 3, and analyzes the drought resistance adaptability prediction report and the optimal planting parameter suggestions for the sugarcane varieties in each breeding greenhouse.

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