4 results about "Crop coefficient" patented technology

The application discloses an irrigation system for adjusting soil moisture by means of meteorological data information, and relates to the technical field of agricultural irrigation. The system comprises a historical meteorological data processing module, a crop water requirement model optimization module, a soil moisture balance model optimization module, an irrigation decision model optimization module, a multi-modal fusion model optimization module, an execution control module and an irrigation device. The system calculates the ET0 value by the Penman-Monteith formula, optimizes the soil moisture model in combination with the precipitation intensity duration correction factor, dynamically correlates the crop coefficient and meteorological elements, constructs the irrigation water function based on the meteorological forecast and real-time monitoring data, introduces the disaster early warning mechanism to adjust the irrigation timing, adopts the historical meteorological feature fusion neural network, reduces the prediction error through cross validation, and realizes the accurate matching of soil moisture and crop water requirement. The application integrates multiple source meteorological parameters, optimizes the simulation precision of water cycle, improves the irrigation efficiency, and improves the efficiency of maintenance of the sprinkler equipment by replacing the sprinkler equipment without shutdown.

This invention relates to the field of water demand prediction technology, specifically to an intelligent water demand prediction system and method for rural areas. By quantifying the crop growth process into continuous growth state parameters and constructing a dynamic crop coefficient model based on a sine function, it solves the problems of discontinuous water demand calculation and physiological distortion caused by traditional step-type models during growth stage transitions. It corrects root depth and combines measured soil moisture content with preset field capacity to dynamically constrain effective precipitation, overcoming the shortcomings of existing technologies that ignore basic soil moisture conditions and overestimate the contribution of natural precipitation. By constructing a priority score based on water depletion time and growth sensitivity, it achieves rational scheduling in scenarios with limited water resources across multiple plots, ensuring the continuity of crop physiological water demand and improving the precision of irrigation decisions and water resource utilization efficiency.

This invention discloses a method and system for generating water-saving agricultural irrigation decisions based on multi-source data fusion, relating to the field of intelligent agricultural irrigation technology. The method includes: acquiring soil moisture data, meteorological data, and multi-source remote sensing data for a target field to form a multi-dimensional data matrix; using the physical process of soil moisture transport as a constraint, fusing the soil profile water content of each field grid according to the physical semantics and spatial representativeness of each data source to obtain the fused soil state and its uncertainty; dynamically determining the crop coefficient and its uncertainty based on meteorological data, the fused soil state, and crop remote sensing data; substituting the fused soil state, crop coefficient, and meteorological elements into a crop water requirement estimation model to obtain the crop water requirement and its uncertainty for each field grid; and generating irrigation decision instructions based on the crop water requirement. This invention, through multi-source data fusion and full-link uncertainty propagation, enables refined and quantitative characterization of the reliability of agricultural irrigation decisions.

This invention relates to the field of plant canopy transpiration estimation, specifically a method for estimating canopy transpiration in shallow-rooted plants. The invention collects readily available surface environmental factors such as total radiation, vapor pressure deficit, soil moisture content at 20cm depth, canopy leaf area index, and wind speed. It then uses the envelope method to extract the corresponding standardized canopy transpiration rate values ​​and fits control functions one by one. Finally, it constructs a canopy transpiration estimation model by multiplying the output values ​​of each control function, the leaf area index, and the canopy transpiration rate per unit leaf area. This effectively avoids the shortcomings of traditional Penman-Monteith-type models, which require a large number of difficult-to-obtain parameters such as crop coefficients and canopy impedance, significantly lowering the barrier to model use. Furthermore, considering the root distribution characteristics of shallow-rooted plants, it uses soil moisture content at a depth of 20cm to improve the accuracy of canopy transpiration estimation.