15 results about "Water deficit" patented technology

The application discloses a forest seedling cultivation spraying method with adjustable water supply and a matching system. The application collects multi-source data such as multi-layer soil water content and microclimate, accurately calculates real-time water deficit, irrigation efficiency loss and target water requirement of each partition based on intelligent algorithm, and generates individualized irrigation decision. The system combines constant pressure variable frequency water supply and branch pipe independent pressure regulation to ensure global stability of pipe network pressure and precise controllability of partition pressure; through cooperation of partition electromagnetic valve and flowmeter, precise water distribution of 'quantity closing' is realized. The flow and angle of the end nozzle are adjustable, and intermittent spraying and real-time monitoring are cooperated to ensure the uniformity and efficiency of irrigation. After irrigation, the system continuously optimizes the decision model through effect evaluation and historical data learning. The application realizes the change from experience irrigation to data-driven intelligent irrigation, and significantly improves the water resource utilization efficiency, seedling growth quality and system operation reliability.

The invention discloses an intelligent precise irrigation method for matrix cultivation based on environmental parameter optimization. Firstly, the theoretical evapotranspiration is calculated based on the total irrigation amount, the irrigation discharge amount and the total weight of a crop matrix; then, calculating reference evapotranspiration based on a Penman correction formula, and calculating the reference evapotranspiration in different growth stages by utilizing crop coefficients; finally, by taking the reference evapotranspiration of different growth stages as a contrast, optimizing the theoretical evapotranspiration by using the daily average temperature, the daily average photosynthetically active radiation quantity and the daily average relative humidity to obtain a difference function between the reference evapotranspiration and the theoretical evapotranspiration; compensating the theoretical evapotranspiration by using a difference value between the reference evapotranspiration and the theoretical evapotranspiration to obtain an actual evapotranspiration; when the actual evapotranspiration is larger than the allowable water deficit value of the matrix, irrigation is started, and the irrigation amount is the actual evapotranspiration. According to the method, the quantitative relation among the irrigation time, the irrigation amount and the actual evapotranspiration amount is obtained, intelligent and accurate irrigation is achieved, and the problems that the irrigation accuracy is poor, and irrigation is not timely are solved.

The invention discloses an irrigation method for increasing the yield of medicago sativa under water deficit. The irrigation method comprises the following steps of 1, medicago sativa planting, wherein strip sowing with the row spacing being 20 cm, the sowing rate being 20 kg / ha and the sowing depth being 5 cm is adopted, and 2, medicago sativa irrigation is conducted, wherein the irrigation rate is kept twice a week from sowing to the seedling stage, and the total irrigation amount is 50 mm. Water deficit irrigation is started in the alfalfa branching period, the irrigation amount is calculated according to the local actual crop evapotranspiration amount, the moderate water deficit and the severe water deficit are 2 / 3 and 1 / 3 of the actual crop evapotranspiration amount respectively, and the irrigation frequency is one time per week. And (3) irrigation system: when an underground drip irrigation system is designed, a drip irrigation belt is buried at a position 20cm underground before alfalfa is sown. The alternate subsurface drip irrigation comprises a subsurface drip irrigation system 1 and a subsurface drip irrigation system 2. One set of underground drip irrigation system is used for one-time irrigation, and the other set of underground drip irrigation system is used for next-time irrigation. The alternate underground drip irrigation method is an effective way for improving the yield of alfalfa under the condition of water deficiency.

The invention relates to a tidal seedling bed intelligent control method and system, and specifically, the method comprises the steps: calculating a comprehensive moisture evapotranspiration rate through an evapotranspiration weight matrix, and obtaining an accumulated moisture deficit value through time integration; when the value reaches an irrigation triggering threshold value, liquid supply is started, the liquid level height in the bed is monitored in real time, a first-order derivative is calculated, and if a super-matrix saturation flow velocity inflection point value is reached, liquid supply is stopped, and the duration is recorded; measuring a complex impedance phase angle of the matrix in a liquid retention period, detecting the conductivity of the backflow nutrient solution after liquid drainage, and calculating the water-holding capacity of the matrix; adjusting a crop transpiration coefficient in the evapotranspiration weight matrix based on a conductivity comparison result before liquid drainage and irrigation to accord with an actual water and fertilizer absorption ratio; updating the saturation flow velocity inflection point value to adapt to the physical property change of the matrix; the irrigation trigger threshold value of the next period is corrected by using the statistical deviation between the current liquid supply time and the historical liquid supply time, and the cumulative error of the evapotranspiration model is compensated; if the phase angle deviates from the health baseline by a preset amplitude, low-concentration nutrient solution flushing is started in the next period, and the rhizosphere stress environment is improved.

This invention belongs to the field of smart agriculture technology. It discloses a smart agricultural irrigation control system based on multi-source data fusion. The system includes: uniformly dividing a target farmland into multiple unit farmlands; collecting multi-source crop phenotypic data from each unit farmland and performing fusion analysis to identify the crop type and growth stage of each unit farmland; acquiring the soil moisture status of each unit farmland and, combined with the crop type and growth stage, quantitatively assessing the water deficit degree of each unit farmland; acquiring the three-dimensional distribution of crop roots in each unit farmland and, combined with the water deficit degree, dynamically calculating the irrigation hydraulic parameters of each unit farmland; and, based on the irrigation hydraulic parameters, determining the irrigation operation parameters for each unit farmland and precisely controlling the irrigation equipment. This invention enables differentiated and precise irrigation at the unit farmland level, significantly improving water resource utilization efficiency and ensuring that crops receive the most suitable water supply at different growth stages.

The present application relates to the field of intelligent garden irrigation technology, and specifically relates to an intelligent garden irrigation early warning device and method, comprising the following steps: a thin-layer substrate water dynamic monitoring unit, a microclimate parameter acquisition unit, a waterproof layer safety monitoring unit, a load-bearing safety evaluation unit, a high-frequency micro-irrigation control unit, a hierarchical early warning unit, the hierarchical early warning unit comprising a water deficit early warning module, a waterlogging risk early warning module, a waterproof layer leakage early warning module and an overweight early warning module; and a precision irrigation execution unit.

The invention discloses a potato leaf water content monitoring method based on crown temperature difference, which comprises the following steps: acquiring data by adopting an infrared thermal imager, determining a monitoring time period, and calculating to obtain the crown temperature difference of potatoes; respectively constructing a lower base line and an upper base line related to steam pressure deficit, and forming water stress indexes in different growth periods; and respectively constructing a quantitative relationship between the water stress index and the leaf and a quantitative relationship between the water stress index and the plant water content, verifying, and monitoring the water content of the potato leaf. The problems that in the prior art, a multi-growth-period dynamic monitoring and systematic threshold system is lacked, and accurate support cannot be provided for irrigation are solved, the potato leaf water content and the plant water deficit condition can be accurately indicated according to field verification CWSI, a basis is provided for precise irrigation of potatoes, and the method is suitable for large-scale popularization and application. Important technical support can be provided for sustainable production of potatoes in arid and semi-arid regions.

The invention discloses a crop efficient water irrigation opportunity prediction method based on machine learning, relates to the technical field of intelligent agriculture and precise irrigation, and aims to solve the problem that a static irrigation decision cannot respond to spatial-temporal dynamic change of soil moisture. According to the method, rapid response to weather and crop water demand disturbance is realized, multi-depth water content and meteorological and phenological data are fused, a water stress index is calculated, and time-space correlation characteristics are constructed, so that a water deficit area and a key water demand time period are accurately identified. On the basis, in combination with phenological period sensing feature scaling and Pareto optimization, an efficient strategy for optimizing irrigation water quantity, energy consumption and yield risks is synchronously generated, so that resource waste caused by decision lag and global rearrangement is avoided, and finally, irrigation equipment is linked to form a closed-loop execution process, so that the irrigation efficiency is improved. Therefore, the irrigation accuracy, the self-adaptive capability and the resource utilization efficiency are obviously improved in a changeable farmland environment.

This invention provides a water and fertilizer management method for improving the yield and quality of edible roses through deficit irrigation and organic fertilizer substitution, relating to the field of agricultural planting technology. This invention uses two-year-old dark red edible roses as the test material, and conducts irrigation and fertilization treatments during the rose's growth process, setting three irrigation levels (full irrigation CI and two deficit irrigations: DI). 80 and DI 60 The irrigation amounts for deficit irrigation were 80% and 60% of those for full irrigation, respectively, and there were four fertilization levels (F0: fertilizer alone, F...). 15 Organic fertilizer replaces chemical fertilizer by 15%, F 30 Organic fertilizer replaces 30% of chemical fertilizer and F 45 This invention investigated the effects of deficit irrigation (80% of full irrigation) and partial organic fertilizer substitution (30% of chemical fertilizer) on the yield and quality of edible roses using 12 complete combination treatments. The method of coupling deficit irrigation (80% of full irrigation) and organic fertilizer substitution significantly improved the yield and quality of edible roses, reduced fertilizer use and water waste, and provided a scientific theoretical basis for water and fertilizer management of edible roses.

The invention belongs to the technical field of intelligent agriculture and water resource management, and particularly relates to an irrigation decision-making method and device based on predictive model and multi-source data fusion. The method comprises the following steps: acquiring current key physiological parameters and earth surface parameters of crops; a coupling prediction model is constructed based on a ground surface evapotranspiration sub-model, a crop growth sub-model and a soil water balance sub-model, current key physiological parameters and ground surface parameters of crops are input into the model, and set weather forecast data of future N days are used as driving data. The probability distribution of the accumulated water deficit amount of the crop plot in the future N days under the zero irrigation scene is simulated and output day by day to calculate the probability that the soil water content of the crop plot on the ith day in the future is lower than a dynamic stress early warning threshold value, and if the probability exceeds a preset decision confidence level, an irrigation prescription corresponding to the crop plot is triggered. According to the invention, the problems of lagging irrigation decision, lack of predictability and insufficient consideration of uncertainty factors in the prior art are solved.

The application discloses a water-saving and emission-reducing potential analysis method and system for an irrigation system. The method comprises the following steps: extracting a water deficit feature, and constructing a water deficit-yield response relationship feature vector; calculating a reference crop evapotranspiration, and determining an optimal irrigation water volume in combination with the feature vector; determining an initial water-saving potential according to an actual irrigation water volume and the optimal irrigation water volume; obtaining energy structure information and a real-time carbon emission factor time sequence library, and calculating actual carbon emissions and theoretical carbon emissions based on an actual irrigation time node and an optimal irrigation time window respectively; and combining a crop yield loss upper limit value, and determining a target water-saving potential and a target emission-reducing potential. The application realizes the collaborative optimization of water saving and emission reduction under the constraint of yield loss by constructing the water deficit-yield response relationship feature vector and introducing the time sequence fluctuation of the marginal carbon emission factor, and solves the problems of isolated water-saving and emission-reducing analysis, insufficient consideration of crop water response and inaccurate carbon emission evaluation in the prior art.

The invention discloses a closed-loop adaptive precise irrigation method based on a prediction model. The closed-loop adaptive precise irrigation method comprises the following steps: obtaining predicted meteorological data through LSTM and calculating to obtain reference crop transpiration; obtaining soil / plant prediction state data through XGBoost, and calculating to obtain actual crop transpiration; and finally calculating the water demand and the water shortage risk index of the crops. Compared with'hysteresis control 'in the prior art, the closed-loop self-adaptive precise irrigation method based on the prediction model disclosed by the invention has the advantages of time sequence prediction and nonlinear interactive modeling by utilizing the LSTM-XGBoost hybrid model, realizes rolling prediction 12 hours ahead of time, and has relatively high accuracy. Predictive driving is combined with the crop water demand and the water shortage risk index, it is guaranteed that the irrigation amount is kept within a reasonable range, the situation that due to lag control, interference irrigation of rainfall after irrigation and insufficient irrigation amount, water shortage of plants is caused is avoided, waste of irrigation water can be avoided, and it can be guaranteed that the crop growth water demand is met.

The invention relates to the field of agricultural irrigation, and discloses an intelligent water management system for a watering cart, and the system comprises a crop canopy recognition module which is used for carrying out the crop canopy recognition processing of a regional image, and obtaining a crop distribution diagram; the regional water demand determination module is used for calculating the water evapotranspiration amount of crops in the crop distribution map, calculating the water deficit coefficient corresponding to the watering region and determining the regional water demand corresponding to the watering region; the sprinkling irrigation uniformity determination module is used for determining the sprinkling irrigation uniformity of the to-be-sprinkled area; the regional irrigation map generation module is used for generating a regional irrigation map of the watering region; and the regional watering treatment module is used for carrying out watering treatment on the watering region by utilizing a watering cart based on the sprinkling irrigation uniformity and the regional irrigation map to obtain a watering result. The rationality of intelligent water management of the watering cart can be improved.

The present application relates to the field of agricultural data analysis, and more particularly to a corn drought dynamic monitoring and influence estimation method, which comprises: acquiring an instantaneous water deficit time sequence and double sliding time window parameters; performing ascending and descending evolution characteristic analysis on the instantaneous water deficit time sequence to obtain day and night fluctuation damping factors; performing rehydration pulse and historical load coupling analysis on the instantaneous water deficit time sequence to obtain global historical correction factors; performing state self-feedback dynamic integral evolution on the instantaneous water deficit amount to obtain real and effective cumulative water deficit total load; and performing crop water deficiency response analysis on the real and effective cumulative water deficit total load to obtain corn yield influence dynamic estimation results, thereby solving the problems that the existing cumulative water deficit index is difficult to distinguish between photosynthetic noon rest false drought and real drought stress, and cannot represent rehydration compensation feedback to cause drought load and inaccurate yield reduction estimation results.