Water and fertilizer online accurate detection and irrigation control system

Abstract

The invention provides a water and fertilizer on-line accurate detection and irrigation control system which is used for irrigation of hardened water channels in low-latitude plateau areas. Comprising a cloud box, a data acquisition module, an image recognition module, a system PLC controller and a remote control and system alarm. The cloud box establishes a real-time and historical database based on the data acquired by the data acquisition module, further comprises an irrigation volume model and a crop growth period moisture management model, and makes and outputs an irrigation decision based on the irrigation volume and moisture management model; the data acquisition module is used for acquiring soil or matrix data and field water level; the image recognition module collects crop growth images to enter different period schemes; and the PLC receives an irrigation decision or controls a lifting telescopic rod or a water gate decision. According to the system, large-scale intelligent planting of crops is achieved, a large amount of manpower is saved, the problem that the low-latitude plateau green efficient water-saving irrigation technology lacks is solved, and the crop production amount is greatly increased.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of agricultural intelligent irrigation and information management, and more particularly relates to a water and fertilizer online precision detection and irrigation control system. BACKGROUND

[0002] The application relates to the technical field of agricultural intelligent irrigation, and is particularly suitable for the precision management of water and fertilizer in a seasonal water-deficient irrigation area in a low-latitude plateau region. In a traditional rice irrigation area, due to uneven distribution of water resources, variable climate conditions and backward irrigation technology, problems such as low water use efficiency, uneven crop growth and insufficient fertilizer utilization rate often occur. Especially under the condition of hardening canal irrigation, the spatial distribution of soil moisture and nutrients is complex, and it is difficult for management personnel to timely and accurately obtain key soil and water parameters, so that it is difficult to realize precision irrigation and scientific fertilizer application.

[0003] In addition, due to the lack of efficient information collection and data processing means, current irrigation decisions mostly rely on manual experience, which is easy to cause delay or failure in judgment, leading to over-irrigation, insufficient irrigation or waste of water and fertilizer, and further causing adverse effects on crop yield and ecological environment. At the same time, with the extreme weather and the development of agricultural scale, the intelligentization and automation of field water and fertilizer dynamic monitoring are increasingly urgent, and how to organically combine real-time monitoring technology, artificial intelligence algorithm and water and fertilizer regulation has become a key technical problem to promote water-saving and yield-increasing in irrigation areas and green and efficient production. Therefore, a highly integrated water and fertilizer online precision detection and irrigation control system is urgently needed to realize the omnibearing automatic monitoring and fusion analysis of environmental information during the growth of crops in the irrigation area, and based on an intelligent decision algorithm, to automatically complete the precision regulation of irrigation, drainage and water and fertilizer at different growth stages, effectively cope with the current situation of complex plateau climate and water shortage in the irrigation area, and thus improve the intelligentization and sustainable development level of agriculture. SUMMARY

[0004] In view of the technical bottlenecks such as untimely water and fertilizer monitoring, low precision of irrigation water and fertilization, dependence on manual judgment, insufficient regulation efficiency and serious resource waste in the process of planting crops such as rice in the existing low-latitude plateau irrigation area due to the lack of efficient information collection and intelligent decision means, the application aims to solve the problems of how to realize the real-time online monitoring of multi-source environmental parameters in the field, how to intelligently fuse and analyze the plateau weather, multi-dimensional soil data and crop growth information, and how to realize the dynamic precision discrimination of crop water and fertilizer demand and fully automatic optimization of irrigation control based on a large model algorithm, so as to improve the water and fertilizer utilization efficiency and intelligent management level of the water-deficient irrigation area.

[0005] In order to solve the above problems, the application is realized by adopting the following technical scheme: the system comprises a control box, and the control box comprises a monitor, a cloud box, a PLC controller, an extension rod, a soil 7-in-1 sensor and a liquid level sensor.

[0006] The monitor is installed on the top of the control box, the PLC controller and the cloud box are installed inside the control box, the bottom of the control box is provided with an opening, the telescopic rod is installed in the opening, and the front end of the telescopic rod is provided with a soil 7-in-1 sensor, and the other opening in the bottom of the control box is provided with a liquid level sensor. The PLC controller is powered by a solar panel installed above the water gate.

[0007] The output ends of the soil 7-in-1 sensor and the liquid level sensor are connected with the input end of the cloud box, the output end of the PLC controller is connected with the control port of the telescopic rod and the irrigation system, and the input end of the PLC is connected with the output end of the cloud box.

[0008] The cloud box can process the collected sensor data through a large model algorithm, and then output data for controlling the PLC, and the output end of the PLC is used for controlling the movement of the telescopic rod and the start of the irrigation system, realizing the functions of irrigation and drainage during the growth period of crops in the irrigation area, growth process data monitoring, image recognition of the growth period of crops and the like.

[0009] In one scheme, the soil 7-in-1 sensor can realize real-time monitoring of the water, temperature, pH value, conductivity, nitrogen, phosphorus and potassium parameters of the target irrigation area soil.

[0010] The liquid level sensor continuously monitors the change of water level in the channel or field, and transmits all the monitored data to the cloud box through wired or wireless mode, the cloud box uniformly collects, preliminarily analyzes and data aggregates all the sensor data, and provides accurate and reliable data support for the deep processing of the large model algorithm and the generation of the irrigation control technical scheme.

[0011] In one scheme, the cloud box is provided with a data processing and neural network algorithm module, based on the real-time collected soil, water level and weather data, combined with multi-source data such as crop growth process data monitoring and image recognition of the growth period of crops, the dynamic correction of parameters for low-latitude plateau areas is realized, specifically including local calibration of plateau radiation, water evaporation and Kc value, and finally outputting optimal control instructions for irrigation and drainage, irrigation warning and the like, improving the precision and intelligent degree of the whole system.

[0012] In one scheme, the PLC controller and the cloud box realize bidirectional communication of data and commands, can automatically regulate the actuating depth and frequency of the telescopic rod according to the parameters output by the cloud box, so that the soil 7-in-1 sensor can be deepened or withdrawn to multiple soil deep layers or shallow layers as needed, dynamically and real-timely obtaining the required environmental parameters of the soil layer, while the cloud box monitors data anomalies and feeds back interface visualization, improving the operation reliability of the system.

[0013] In one scheme, the control port of the irrigation system is uniformly controlled by a PLC controller, which automatically and accurately determines the irrigation water volume and irrigation time based on different stages of crop growth, soil moisture and nutrient status in the target area, meteorological observation data, and calculation results of the cloud box based on the prediction model. This can achieve continuous soaking and softening of the soil during the soaking period, and can also start the drainage and drying process during the drying period in a timely manner to ensure the optimal growth environment for crops.

[0014] In one embodiment, the system possesses predictive control capabilities during the field drying period. It can automatically integrate real-time soil moisture monitoring values, reference plateau meteorological data collected from meteorological stations, and future rainfall forecasts to predict future soil moisture trends. When forecast data indicates that future soil moisture will drop below 65% and no significant rainfall is expected, the system will automatically activate irrigation devices and simultaneously issue an alarm warning to management personnel, achieving effective prevention and control of crop growth stress.

[0015] In one embodiment, the solar panel provides continuous and stable clean energy to all electrical equipment in the control box, including the PLC controller, cloud box, monitor, telescopic pole, soil 7-in-1 sensor, and liquid level sensor. This is suitable for application scenarios such as seasonally water-scarce irrigation areas in low-latitude plateaus where external power supply is difficult or grid coverage is insufficient, ensuring the equipment's independent operation capability in the field and the system's energy-saving and environmentally friendly green irrigation characteristics.

[0016] In one embodiment, the system is equipped with an intelligent predictive control algorithm that combines plateau meteorological data and soil moisture data. It integrates meteorological observation and reanalysis data from the plateau region in real time, incorporates influencing factors such as plateau radiation correction and diurnal temperature range when calculating soil moisture change trends, and dynamically predicts whether soil moisture will drop to a threshold based on future rainfall during the forecast period. This guides the setting of warning thresholds and the early triggering of automatic irrigation, enabling intelligent decision-making and optimized control of water management throughout the entire process of paddy field flooding and drying periods.

[0017] Beneficial effects of this invention:

[0018] This invention integrates real-time monitoring from multiple sensors, crop growth models, and intelligent decision-making algorithms to achieve dynamic and accurate perception of field water, nutrients, and crop growth status in seasonally water-scarce irrigation areas on low-latitude plateaus. It can efficiently collect and integrate various environmental and growth data, significantly improving the comprehensiveness and accuracy of monitoring.

[0019] Based on large-scale intelligent judgment and fully automated irrigation control technology, the system can automatically optimize and schedule irrigation and fertilization programs according to different growth stages of crops and their actual water and fertilizer requirements, effectively overcoming the problems of lagging decision-making and uneven water and fertilizer application in traditional manual management. Through scientific regulation of irrigation and drainage and water and fertilizer supply, it significantly improves water and fertilizer utilization efficiency, reduces resource waste, and ensures balanced crop growth and increased yield. At the same time, the comprehensive application of this invention reduces water pressure in irrigation areas, promotes ecological environmental protection, and provides strong support for the intelligent, green, and sustainable development of agriculture in water-scarce plateau regions. Attached Figure Description

[0020] Figure 1 This is a system block diagram of the present invention;

[0021] Figure 2 This is a structural diagram of the control box of the present invention. Detailed Implementation

[0022] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0023] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0024] like Figure 1 and Figure 2 This system is a new technology for irrigation of crops in seasonally water-scarce low-latitude plateau regions using hardened irrigation canals. It addresses the lack of green, efficient, and water-saving irrigation technologies in these areas. It can be used for irrigation and drainage of crops, monitoring growth process data, and image recognition of crop growth stages.

[0025] The control system includes a control box, which includes a monitor, a cloud box, a PLC controller, a telescopic rod, a soil 7-in-1 sensor, and a liquid level sensor.

[0026] The monitor is installed on the top of the control box, and the PLC controller and cloud box are installed inside the control box. The bottom of the control box has an opening, in which a telescopic rod is installed. A soil 7-in-1 sensor is installed at the front end of the telescopic rod, and a liquid level sensor is installed in another opening at the bottom of the control box.

[0027] The PLC controller is powered by a solar panel mounted above the sluice gate. The outputs of the soil 7-in-1 sensor and the liquid level sensor are connected to the input of the cloud box. The PLC controller output is connected to the control ports of the telescopic boom and the irrigation system. It is used to control the movement of the telescopic boom and the start of the irrigation system. The PLC input is connected to the cloud box output. The cloud box processes the collected sensor data using a large-scale model algorithm and then outputs the data to control the PLC.

[0028] Example:

[0029] Take the rice growing season, specifically the paddy field soaking period and the dry field drying period, as an example.

[0030] The paddy field soaking period refers to the process of flooding the paddy field with water for a period of time before rice transplanting, allowing the soil to fully absorb water and soften, facilitating subsequent land preparation and transplanting operations. Its main functions include:

[0031] 1. Soften the soil: Soaking the soil with water makes it loose and easier for mechanical or manual land preparation.

[0032] 2. Eliminate weeds and pests: During the paddy field flooding process, the water layer can submerge weeds and some pests, reducing their harm to rice.

[0033] 3. To provide better nutritional conditions for rice growth.

[0034] 4. The soaking period generally lasts 3-7 days, depending on the soil texture and the condition of the previous crop.

[0035] The field drying period refers to the process of naturally drying paddy field soil by draining water during the late tillering stage to early jointing stage of rice, in order to improve soil aeration and control ineffective tillering. Its main functions include:

[0036] 1. Controlling ineffective tillers: By drying the field, the growth of high-level tillers is reduced, allowing nutrients to be concentrated on effective tillers and increasing the ear formation rate.

[0037] 2. Improve the soil environment: Sun-drying the field can increase the oxygen content in the soil, promote the activity of aerobic microorganisms, improve soil structure, and enhance root vitality.

[0038] 3. Enhanced lodging resistance: After the field is dried, the rice stems become thicker and the basal internodes shorten, thus enhancing lodging resistance.

[0039] 4. Reduce the incidence of pests and diseases: Drying the field can reduce humidity and decrease the occurrence and spread of pests and diseases. The drying period is generally 5-10 days, but the specific time should be adjusted according to soil type, rice growth, and weather conditions.

[0040] The paddy field soaking period and the paddy field drying period are important technical aspects of rice cultivation. Proper management of these two stages can effectively improve the yield and quality of rice.

[0041] Within the cloud-based monitoring system, rice growth monitors capture images of the rice paddy environment and, based on image recognition technology, enter different operating modes (flooding or drying). One monitor is installed in each paddy field. The flooding and drying periods for each rice season are set within the cloud-based monitoring system. Images of the rice paddy surface are collected every six hours during and around these periods, forming initial training and testing datasets. These datasets are input into the cloud-based monitoring system, labeled to identify the rice's growth stage, and then subjected to online deep learning training to generate flooding and drying models. In the actual paddy field environment, the monitors collect images of the rice every six hours, inputting them into the flooding and drying models. The system outputs the rice's current stage and transmits the results via the cloud-based monitoring system to the PLC controller, which then operates in the corresponding mode (e.g., flooding or drying mode). Even after the intelligent irrigation system is in operation, images of the paddy field soaking and drying periods of rice are continuously transmitted into the cloud box for each season. Deep learning is then used to train the rice growth model online with both new and old datasets each season, updating the model to make it increasingly accurate.

[0042] The rice surface monitor autonomously identifies the growth stage of rice and sends the signal through the cloud box to input the result to the system PLC controller for the corresponding control part of the growth stage.

[0043] This function is universal; it uses image recognition to determine the growth stage of crops at different growth stages, and then issues corresponding control commands for that growth stage.

[0044] If the paddy field enters the soaking period:

[0045] Current control: When the water level sensor detects a water level below 5mm (during the flooding period), the sluice gate needs to be opened to release water. At this time, the control telescopic rod will raise the seven-in-one sensor (the seven-in-one sensor cannot be submerged in water for a long time, considering cost and service life). When the water level is above 5mm, the sluice gate will be closed.

[0046] Decision-making logic:

[0047] The system starts N days before transplanting. Based on the initial soil moisture content, it calculates and issues a large irrigation command M in one go (or in several stages). puddling This is to ensure that there is a water layer of a specified depth in the field when transplanting rice seedlings.

[0048] 1. Phase One: Flooding Period Model - The core is "rapidly establishing a water layer".

[0049] The goal of the soaking period is to saturate the soil and establish a thin water layer before transplanting rice seedlings. The core of this period is to calculate the initial soil water shortage rate.

[0050] Model formula:

[0051] Parameter definition: Total irrigation water required during the paddy field soaking period (mm). Soil saturated volumetric water content (cm³ / cm³). This can be obtained by referring to a table based on soil type (e.g., clay, loam). : Initial soil moisture content collected by sensors before paddy fields are flooded. γ: Planned soaking depth of soil (cm). Usually 20-30cm. γ: Soil bulk density (g / cm³). : The required depth of the thin water layer (mm). Typically 30-50mm. E pre : Average daily water surface evaporation during paddy field soaking (mm / day). T: Planned number of paddy field soaking days (days). For example, 3-5 days. P eff : Forecast effective rainfall during the flooding period (mm).

[0052] To make the model more regionally adaptable, in calculating ET c When calculating evaporation E, the characteristics of strong evaporation and radiation at high altitudes can be incorporated. The model is modified as follows: Due to high altitude, thin air, and strong solar radiation, evaporation and evapotranspiration in high-altitude areas are generally higher than in plains areas. Therefore, directly using the standard calculation formula may lead to underestimation. Water surface evaporation is highly correlated with the reference evapotranspiration ET0. The modified Priestley-Taylor equation is used to calculate ET0, which is then directly used as E. pre The estimated value (because the water layer during the flooding period is similar to the free water surface).

[0053]

[0054] In the formula: For reference crop water requirements, This represents the net radiation level on the crop surface. Soil heat flux density, The slope of the saturated water vapor temperature curve. This is the hygrometer constant.

[0055] Solar radiation Solar radiation is strong in high-altitude areas, so local radiation data or satellite remote sensing products (such as the high-altitude radiation enhancement coefficient) should be used. For example, the radiation value can be multiplied by a high-altitude correction factor (such as 1.1-1.3, which needs to be calibrated according to altitude and latitude).

[0056] Atmospheric pressure related parameters: Due to high altitude, atmospheric pressure decreases, affecting parameters such as Δ and γ. Altitude correction formulas need to be used (e.g., γ decreases with increasing altitude).

[0057] In the model input, E pre Instead of directly using data from ordinary weather stations, ET0 is calculated using the aforementioned high-altitude acclimatization method and used as E. pre The value of .

[0058] For example, in the system code, E pre The calculation module has been replaced with a plateau-specific ET0 calculation function. Therefore, the revised result of the original formula is:

[0059] Predictive Control: Predict water level changes over the next few days, combining weather forecasts (especially rainfall forecasts) and ETC (Electronic Toll Collection) predictions to calculate the likelihood and extent of future water shortages. Decision Making: If the water level is predicted to drop below 5mm within the next 24 hours, and no effective rainfall is expected, open the sluice gates for irrigation in advance to prevent excessively low water levels. Simultaneously, considering potential delays in canal water delivery, request water releases in advance and set alarm thresholds.

[0060] Entering the field drying season:

[0061] Current control:

[0062] (1) When the seven-in-one sensor detects that the soil moisture content is below 65% (drying period), the sluice gate needs to be opened to release water;

[0063] (2) When the water level sensor detects that the water level exceeds 1 mm, raise the seven-in-one sensor away from the water surface and close the sluice gate.

[0064] (3) As the soil surface moisture evaporates, when the water level is below 1 mm, put down the seven-in-one sensor.

[0065] 2. Phase Two: Field Drying Period Model - The core is "precise water control and drying out".

[0066] The goal of the dry season (usually at the end of tillering) is to reduce soil moisture content to a suitable range by draining water and stopping irrigation, in order to suppress ineffective tillering. The core of this is to predict the soil moisture content decline curve and determine whether "re-watering" is necessary.

[0067] Model formula (for predicting future moisture content):

[0068]

[0069] Parameter definition: θ predict (t+Δt): The predicted soil moisture content after a future time Δt.

[0070] θ current (t): Current soil moisture content. ET c (t): Daily evapotranspiration (mm / day) during the rice field drying period. During this stage, rice physiological activity is vigorous, but water surface evaporation becomes soil evaporation; therefore, a soil evaporation model or empirical values ​​must be used. D deep : Daily seepage (mm / day). Drainage during the field drying period can lead to significant seepage, which is an important pathway for water loss. Δt: Prediction step (days), e.g., 1 day.

[0071] During the field drying period, ETc(t) is the daily crop evapotranspiration (unit: mm / day), which is usually calculated from the reference evapotranspiration ET0 and the crop coefficient Kc: ETc(t) = Kc × ET0. Strong radiation at high altitudes will significantly increase ET0, thereby increasing ETc.

[0072] Key corrections to the radiation term: The net radiation Rn in the ET0 formula should be based on plateau radiation data. For example, introduce a plateau radiation enhancement factor (e.g., multiply by 1.2-1.4), or use a plateau-specific radiation model (e.g., considering sky clarity).

[0073] Considering the diurnal temperature range: The large diurnal temperature range in plateau areas may affect evapotranspiration. It is recommended to use the average daily temperature in ET0 calculations and verify its applicability.

[0074] Adjustment of crop coefficient Kc: During the field drying period, rice is in the late tillering stage, and the Kc value may vary slightly due to the strong sunlight at high altitudes (for example, enhanced photosynthesis may increase transpiration). However, Kc mainly depends on crop type and growth stage, and the variation is relatively small. It is recommended to calibrate the Kc value with reference to local high-altitude experimental data (e.g., fine-tuning through field trials).

[0075] Integrating plateau meteorological data: During model operation, ET0 calculations should be performed by real-time access to plateau meteorological station data (especially radiation and wind speed), or by using plateau reanalysis data products.

[0076] The calculation of ETc(t) is changed to: first calculate ET0 using the high-altitude adaptation method, then multiply by the crop coefficient Kc (Kc can temporarily use a standard value, to be calibrated locally later). Therefore, the revised model formula is:

[0077]

[0078] Predictive Control: Predict soil moisture changes over the next few days, and calculate the time it will take for soil moisture to drop to a threshold based on ETC (Earnings Tolerance) and rainfall forecasts. Decision Making: If soil moisture is predicted to fall below 65% and there is no expected rainfall, irrigation should be carried out in advance. Simultaneously, soil moisture needs to be controlled during the field drying period to prevent it from falling too low. Prediction can prevent low soil moisture from causing stress to crops, and alarm thresholds can be set.

[0079] The above management model is built on a cloud platform. The smart irrigation management model for rice uses meteorological data (temperature, humidity, wind speed, solar radiation, rainfall) to calculate ETO (reference crop evapotranspiration) and soil data (humidity, temperature, EC, pH, nitrogen, phosphorus, potassium) and water level as inputs during the rice paddy soaking and drying stages, and other periods. A deep learning model is constructed by dividing historical data into training and testing data, and a deep learning neural network model for rice irrigation management is obtained through training. In the actual rice growing environment, the cloud platform, relying on its powerful computing power, generates various real-time monitoring data and data change curves, and displays predicted values ​​in real time (how much time remains before the operation). Field production data is input into this model to calculate the farmland's water requirement. Based on this, this invention incorporates irrigation delay calculation to allow for advance "application," ensuring timely irrigation and feedback for the crop. It calculates the time from gate opening to changes in water level and soil moisture content. Because irrigation in hardened canals has a specific order, such as irrigating plot Ni (i=1, 2, 3...), the response of each plot is calculated. The timing of seasonal variations is processed and continuously improved to achieve greater accuracy. After the intelligent irrigation system is operational, data monitoring is conducted year-round, and data is collected and processed during and around the flooding and drying periods to continuously refine the model. Deep learning is used to train the intelligent irrigation management model online with both new and old datasets during the flooding and drying periods each quarter, updating the model to make it increasingly accurate.

[0080] This system is universally applicable to various crops, allowing for the development of different technical solutions tailored to different crops and needs. For example, it can be used for irrigation and drainage systems for custard apples and control systems for drip irrigation tapes for grapes, identifying different growth stages of grapes. This enables efficient and precise irrigation. This application provides an online precision water and fertilizer detection and irrigation control system for hardened canal irrigation in low-latitude plateau regions. It not only addresses the lack of green, efficient, and water-saving irrigation technologies in low-latitude plateau regions but also provides precise technology for the cultivation of various crops, demonstrating strong targeting and universality.

[0081] Example:

[0082] I. Implementation Environment and Equipment Configuration

[0083] This embodiment uses a low-latitude plateau irrigation area in Yunnan Province as the pilot area, with a total experimental paddy field area of ​​40 mu (approximately 2.67 hectares) at an average altitude of 2200 meters. The soil type is mainly loam suitable for rice, with a significant dry season and traditional irrigation methods resulting in significant resource waste. The experimental field was randomly divided into an experimental area (using the system of this invention, 20 mu) and a control area (using conventional manual irrigation and fertilization management, 20 mu). The system configuration of this invention includes: a wireless soil temperature and humidity / EC multi-point monitoring network, crop canopy image acquisition equipment, an automatic weather station (temperature, humidity, rainfall, light intensity, wind speed), an integrated water and fertilizer intelligent control machine, a wireless communication and cloud data analysis platform, and supporting valves and pipelines.

[0084] II. System Operation Flow

[0085] 1. Data Acquisition Phase

[0086] Soil information is collected through three monitoring nodes per acre, with each sensor measuring soil moisture content, electrical conductivity (EC), and temperature at depths of 5cm, 10cm, and 20cm. Crop canopy images are collected daily at fixed points, and plant color and growth are automatically identified. A weather station simultaneously records regional temperature, humidity, solar radiation, rainfall, and wind speed. All information is automatically uploaded to a cloud-based analysis platform every half hour.

[0087] 2. Intelligent Decision-Making and Control Stage

[0088] Based on the collected real-time data and the current growth stage of the crop (from greening, tillering, jointing and heading, grain filling to maturity for rice), the platform calls upon the large-scale crop growth model built into this invention. According to weather forecasts, soil moisture thresholds, crop growth, etc., it intelligently determines the actual water and fertilizer requirements of each zone, automatically generates corresponding irrigation / fertilization plans, and issues instructions to the integrated field facilities.

[0089] In contrast, the control area was irrigated manually based on experience, with each irrigation primarily consisting of regular flooding based on the apparent soil moisture and the farmers' judgment.

[0090] 3. Intelligent control and execution

[0091] The intelligent zone uses electric valves and a water and fertilizer unit for precise supply, automatically irrigating and fertilizing in batches and zones. Each time, it dynamically adjusts the timing and dosage of the next irrigation / fertilization based on changes in soil moisture and crop growth feedback. The entire operation is automated, requiring only periodic inspections of the system. In contrast, the manual zone is operated by farmers based on their daily experience, with water and fertilizer applied simultaneously without targeted adjustments.

[0092] 4. Data Summary and Effect Evaluation

[0093] After the entire growth period, the cumulative water consumption, fertilizer consumption, changes in soil nutrients, water and fertilizer use efficiency, main agronomic traits (tiller number, plant height, ear length, yield, etc.), yield per unit area, incidence of diseases and pests, and labor input in each region were collected and compared. The data are detailed in the table below.

[0094] III. Key Parameters and Results Table

[0095] Table 1 Comparison of the effects of intelligent water and fertilizer integration control in low-latitude plateau paddy fields

[0096] Index Experimental area (intelligent system) Control area (manual management) The number of sensors per mu 6 / mu 0 Frequency of growth period monitoring data collection per day 48 0 Average field soil moisture content (%) 72.1 61.3 Farmland EC value (μS / cm) 864 521 Average irrigation frequency (times per growth period) 13 8 Average irrigation water per time (m³ / mu) 19.7 32.2 Total irrigation water during growth period (m³ / mu) 256.1 257.6 Total fertilizer application during growth period (kg / mu) 33.2 40.1 Effective nutrient utilization rate (%) 59.3 39.5 Water and fertilizer utilization efficiency (%) 88.6 61.2 Graduation period field runoff (%) 9.1 21.6 Water saving rate (%) 18.9 0 Average manual operation time (hours / mu / season) 3.5 18.2 Rice yield per mu (kg / mu) 582 531 Tillering stage incidence rate (%) 4.1 7.8 Grain filling and setting rate (%) 92.5 85.7

[0097] IV. Implementation Effect Analysis

[0098] 1. Efficient and precise regulation significantly boosts output.

[0099] This invention achieves intelligent and precise regulation of soil water and fertilizer conditions by collecting environmental and crop data at high frequency and at multiple levels, dynamically combining this data with crop growth requirements. In the experimental area, rice yield reached 582 kg / mu, 9.6% higher than the control area, and key agronomic indicators such as seed setting rate and effective tillering were also superior to those in the control area.

[0100] 2. Improved water and fertilizer resource utilization efficiency, eco-friendly

[0101] Thanks to the intelligent decision-making model, the experimental area adopted a precise irrigation and fertilization strategy of "small amounts, multiple times," effectively avoiding over-application. Water and fertilizer use efficiency increased from 61.2% in the control group to 88.6%, saving 18.9% of water and 17.2% of fertilizer input, and reducing surface runoff pollution. Analysis showed that the available nutrients in the soil increased by 12.3%, effectively supporting crop growth.

[0102] 3. Significantly reduces labor input and management costs.

[0103] In the smart zone, over 90% of operations are automated, with manual intervention only requiring periodic system inspections and maintenance. Labor time per acre has been significantly reduced to 3.5 hours per season (compared to 18.2 hours in the manual zone). Farmers' labor intensity has been significantly reduced, freeing up time for other agricultural activities.

[0104] 4. Improved crop health and stress resistance

[0105] The experimental area showed a lower incidence of disease during the tillering stage and an improved grain filling rate, demonstrating the positive effects of precise water and fertilizer management on crop health and stress resistance. Field growth was more uniform, and the lodging rate decreased significantly in the later stages, effectively reducing risk.

[0106] 5. Intelligent management ensures water supply security and ecological balance.

[0107] By rationally regulating and dynamically adjusting irrigation schemes, the contradiction between farmland demand and water shortage during the dry season is effectively balanced, ineffective drainage is reduced, and overall water conservation and ecological environmental protection in the irrigation area are promoted. It has good demonstration and promotion value.

[0108] The intelligent water and fertilizer integrated irrigation system of this invention, applied in water-scarce irrigation areas of low-latitude plateaus, achieves deep integration of multi-source real-time monitoring, intelligent optimization decision-making, and automatic control. This significantly improves agricultural water and fertilizer utilization efficiency, crop yield, and ecological benefits, greatly promoting the intelligent and green development of facility agriculture. This system has strong prospects for widespread application in similar climatic zones.

[0109] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0110] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A water and fertilizer online precision detection and irrigation control system, characterized in that: The system comprises a control box, wherein the control box comprises a monitor, a cloud box, a PLC controller, an extension rod, a soil 7-in-1 sensor and a liquid level sensor. The monitor is installed on the top of the control box, the PLC controller and the cloud box are installed inside the control box, the bottom of the control box is provided with an opening, the extension rod is installed in the opening, the front end of the extension rod is provided with the soil 7-in-1 sensor, and the other opening in the bottom of the control box is provided with the liquid level sensor. The output ends of the soil 7-in-1 sensor and the liquid level sensor are connected with the input end of the cloud box, the output end of the PLC controller is connected with the control port of the irrigation system and the extension rod, and the input end of the PLC is connected with the output end of the cloud box. The cloud box can process the collected sensor data through a large model algorithm, and then output the data for controlling the PLC, and the output end of the PLC is used for controlling the movement of the extension rod and the start of the irrigation system, so as to realize the functions of irrigation and drainage during the growth period of crops in the irrigation area, growth process data monitoring, image recognition of the growth period of crops.

2. The water and fertilizer online precision detection and irrigation control system according to claim 1, characterized in that: The soil 7-in-1 sensor can realize real-time monitoring of the parameters of water, temperature, pH value, conductivity, nitrogen, phosphorus and potassium of the soil in the target irrigation area. The liquid level sensor continuously monitors the change of the water level in the channel or field, and transmits all the monitored data to the cloud box through wired or wireless mode, the cloud box uniformly collects, preliminarily analyzes and data aggregates all the sensor data, and provides accurate and reliable data support for the deep processing of the large model algorithm and the generation of the irrigation control technology scheme. 3.The water and fertilizer online precision detection and irrigation control system according to claim 1, characterized in that: The cloud box is provided with a data processing and neural network algorithm module, based on the real-time collected soil, water level and weather data, combined with the multi-source data of crop growth process data monitoring and image recognition of the growth period of crops, the dynamic correction of the parameters for the characteristics of the low-latitude plateau area is realized, specifically including local calibration of the plateau radiation, water evaporation and Kc value, and finally outputting the optimal control instruction for the irrigation and drainage, irrigation early warning link, improving the precision and intelligent degree of the whole system.

4. The water and fertilizer online precision detection and irrigation control system according to claim 1, characterized in that: The PLC controller and the cloud box realize bidirectional communication of data and commands, can automatically control the actuating depth and frequency of the extension rod according to the parameters output by the cloud box, so that the soil 7-in-1 sensor can be deepened or withdrawn to multiple soil deep or shallow layers as needed, and the required environmental parameters of the soil layer are dynamically and real-timely obtained, at the same time, the PLC monitors and feeds back the abnormal data, improving the operation reliability of the system.

5. The water and fertilizer online precision detection and irrigation control system according to claim 1, characterized in that: The control port of the irrigation system is controlled by the PLC controller, which can accurately determine the irrigation water quantity and irrigation time according to the different growth stages of crops, the soil moisture and nutrient conditions of the target area, the meteorological observation data and the calculation results of the cloud box based on the prediction model, so as to realize the continuous soaking and softening of the soil in the bubble field period, and timely start the drainage and drying process in the field drying period, so as to ensure the best growth environment for crops.

6. The water and fertilizer online precision detection and irrigation control system according to claim 1, characterized in that: The system has a prediction control function in the field drying period, can automatically integrate real-time monitoring values of soil moisture, highland meteorological data collected by reference meteorological stations and future rainfall prediction results, make trend judgment on future soil moisture dynamics, and when the prediction data shows that the future soil moisture will decrease to below 65% and no significant rainfall is expected, the system will automatically start the irrigation device and send an alarm warning to the management personnel at the same time, so as to realize effective prevention and control of crop growth stress.

7. The water and fertilizer online precision detection and irrigation control system according to claim 1, characterized in that: The solar panel provides continuous and stable clean energy for all electrical equipment in the control box, such as PLC controller, cloud box, monitor, telescopic rod, soil 7-in-1 sensor and liquid level sensor, and is suitable for application scenarios where external power supply is difficult or power grid coverage is insufficient in low-latitude highland seasonal water shortage irrigation areas, thereby ensuring the independent operation ability of the equipment in the field and the green irrigation characteristics of the system in terms of energy saving and environmental protection. 8.The water and fertilizer online precision detection and irrigation control system according to claim 1, characterized in that: The system is configured with an intelligent prediction control algorithm combining highland meteorological data and soil moisture data, can integrate meteorological observation and reanalysis data information in highland areas in real time, introduce highland radiation correction and temperature daily range influence factors when calculating the trend of soil moisture change, dynamically predict whether the soil moisture will decrease to the threshold value in combination with the future rainfall in the prediction period, so as to guide the setting of the warning threshold value and the advance triggering of automatic irrigation, and realize intelligent decision and optimization control of the whole process of moisture management in the field soaking and drying periods.

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