A method for diagnosing potassium status of rice and guiding potassium fertilizer management based on LAQUA value of rice leaf

Abstract

The present application belongs to the technical field of potassium fertilizer management, and discloses a method for diagnosing potassium status of rice and guiding potassium fertilizer management based on LAQUA value of rice leaves, which takes rice leaves as a detection object, rapidly determines potassium ion content in leaf juice through a portable LAQUA instrument, introduces leaf thickness (or SLW) as a correction parameter, establishes a quantitative relationship between LAQUA index and leaf potassium concentration, and builds a potassium nutrition threshold on the basis, so as to realize rapid determination and accurate management of whether rice potassium fertilizer needs to be applied. The diagnostic accuracy is significantly improved: the present application finds that the correlation between LAQUA value and leaf potassium content is limited alone, and after combining LAQUA value with leaf thickness (LAQUA value / SLW 2 ), a high linear relationship is presented between LAQUA value and leaf potassium content, which significantly improves the accuracy and stability of potassium diagnosis. The method is rapid, non-destructive and suitable for field application.

Description

Technical Field

[0001] This invention belongs to the field of potassium fertilizer management technology, and in particular relates to a method for diagnosing the potassium status of rice and guiding potassium fertilizer management based on the LAQUA value of rice leaves. Background Technology

[0002] Potassium is one of the essential mineral nutrients for rice growth and development, widely involved in key physiological processes such as photosynthesis, enzyme activation, osmotic regulation, and assimilate transport. Sufficient potassium supply promotes rice root development, enhances lodging resistance, strengthens stem mechanical strength, and improves water regulation and stress resistance. In production practice, potassium fertilizer application plays an irreplaceable role in increasing rice yield and improving rice quality. Meanwhile, crop straw contains a large amount of potassium, which can be reintroduced into the soil-crop system through mineralization under straw return-to-field conditions. However, in actual production, farmers generally overlook the potassium input contributed by straw return-to-field when making potassium fertilizer application decisions, easily leading to repeated or improper application of potassium fertilizer.

[0003] From a macro perspective, my country's potash fertilizer resources are highly dependent on imports, with an import dependency rate exceeding 60% for a long time, making it one of the world's largest potash fertilizer consumers. Against this backdrop, improving potash fertilizer utilization efficiency, reducing unnecessary inputs, and achieving precise potash fertilizer management are not only of significant agricultural production importance but also of vital resource security and economic importance.

[0004] Currently, potassium management in rice mainly relies on the following technical approaches:

[0005] Soil chemical analysis

[0006] Fertilization can be guided by collecting soil samples to determine the content of available potassium, but this method is difficult to reflect the real-time absorption status of crops during the growth period and is greatly affected by soil type and potassium slow-release characteristics.

[0007] Plant histochemical analysis

[0008] Determining potassium nutrition status by measuring leaf potassium concentration in the laboratory is highly accurate, but it has problems such as long sampling, digestion and analysis cycles, high cost, and difficulty in field application.

[0009] Empirical fertilization or quantitative formula fertilization

[0010] Based on regional experience or long-term average fertilizer requirements, these methods often ignore interannual differences, variety differences, and factors such as straw return to the field, making it difficult to achieve true on-demand application.

[0011] In recent years, soil and crop analysis instruments (such as SPAD analyzers) have been used for rapid detection of crop nutrients and have been applied in nitrogen diagnosis and nitrogen fertilizer management. However, existing research shows that LAQUA values ​​have not yet been used to directly diagnose the potassium status of rice, the relationship between LAQUA values ​​and actual potassium concentration in leaves is still unclear, the diagnostic accuracy is uncertain, and it is difficult to use them as a reliable basis for potassium fertilizer management.

[0012] Therefore, how to establish a rapid diagnostic method that can quickly, non-destructively, and accurately reflect the potassium status of rice and can be directly used to guide potassium fertilizer application remains a technical problem that urgently needs to be solved in this field.

[0013] Based on the above analysis, the problems and shortcomings of the existing technology are as follows:

[0014] Existing research indicates that LAQUA values ​​have not yet been used directly to diagnose potassium status in rice. The relationship between LAQUA values ​​and actual potassium concentration in leaves is unclear, the diagnostic accuracy is uncertain, and it is difficult to use them as a reliable basis for potassium fertilizer management. Summary of the Invention

[0015] To address the problems existing in the prior art, this invention provides a method for diagnosing the potassium status of rice and guiding potassium fertilizer management based on the LAQUA value of rice leaves.

[0016] This invention is implemented as follows: A method for diagnosing the potassium status of rice and guiding potassium fertilizer management based on the LAQUA value of rice leaves includes:

[0017] Step 1: Setting up a potassium fertilizer gradient treatment;

[0018] During the rice growing season, three different potassium fertilizer application treatments were set up under the same ecological zone, the same variety, and the same cultivation conditions. The potassium fertilizer application rates were 0, 60, and 90 kg K ha. -1 This step is used to establish different potassium nutrient levels in rice; it is used to construct the differences in leaf potassium levels in rice under different potassium supply conditions, providing basic data for subsequent model building.

[0019] Step 2: Identification and sampling of representative rice leaves;

[0020] At different growth stages of rice (including panicle differentiation, heading, and maturity), rice plants with uniform growth and free from pests and diseases were selected from each treatment plot as sampling objects. The last fully expanded leaf of each rice plant was selected as the test leaf. The leaf must meet the following conditions: the leaf is fully expanded, and the ligule and leaf sheath are clearly separated; the leaf has no mechanical damage or obvious lesions; the leaf is in the functional leaf stage and can reflect the current potassium status of the plant.

[0021] Step 3: Measure the LAQUA value of the leaves;

[0022] After the last fully unfolded leaf is cut off, it is immediately tested using a portable LAQUA TwinK-11 potassium ion analyzer. The specific operation includes: (1) cutting or pressing the leaf to obtain leaf juice; (2) dripping the juice onto the potassium ion selective electrode detection end of the LAQUA TwinK-11 instrument; (3) reading and recording the corresponding LAQUA value.

[0023] This step is used to obtain a rapid detection index reflecting the soluble potassium content in the leaves;

[0024] Step 4: Measure the leaf specific weight (SLW);

[0025] After completing the LAQUA measurement, the uncrushed leaf samples were brought back to the laboratory to determine the leaf structure parameters, including: measuring the leaf area; drying the leaves at 80°C to constant weight; calculating the leaf dry weight per unit leaf area to obtain the leaf specific weight, which is used to characterize the leaf thickness.

[0026] This step is used to reflect the dilution or enrichment effect of differences in leaf tissue structure on the concentration of ions in leaf sap;

[0027] Step 5: Measure the potassium concentration in the leaves;

[0028] Chemical analysis of the same leaf sample to determine the potassium concentration in the leaf includes: pulverizing the dried leaf sample; processing the sample using the H2O2-H2SO4 digestion method; and determining the potassium content of the leaf using a flame photometer.

[0029] This step serves as a standard reference indicator for potassium nutrition diagnosis and is used in model establishment;

[0030] Step 6: Construct the LAQUA correction index;

[0031] Based on the obtained data, an LAQUA correction index is constructed, and the correction index is defined as follows:

[0032] LAQUA correction value = LAQUA / SLW 2

[0033] By introducing leaf structure parameters, the deviation of LAQUA measurement values ​​caused by differences in leaf thickness is eliminated, making this correction index more accurately reflect the potassium nutrition level of leaves.

[0034] Step 7: Establish a diagnostic model;

[0035] Step 8: Determine the potassium nutrient threshold for rice;

[0036] Step 9: Back-calculate and determine the LAQUA threshold;

[0037] Step 10: Determining potash fertilizer application based on the LAQUA threshold;

[0038] Step 11: Apply potassium fertilizer.

[0039] Furthermore, the diagnostic model is established as follows:

[0040] Leaf potassium content was used as the independent variable, and the LAQUA correction value (LAQUA / SLW) was used as the correction value. 2 Using ) as the dependent variable, a quantitative fitting model between the two was established, and the model with the highest determination coefficient and the best stability was selected as the rice potassium diagnostic model;

[0041] This model is used to convert the LAQUA index, which is rapidly measured in the field, into quantitative information that can characterize the potassium nutrition status of rice.

[0042] Furthermore, the determination of the potassium nutrient threshold for rice is as follows:

[0043] The relationship between leaf potassium concentration and rice yield was analyzed at different growth stages to determine the minimum leaf potassium concentration level required to achieve high rice yield, which was used as the potassium nutrition threshold for that stage.

[0044] This threshold reflects the actual potassium requirement of rice during key growth stages.

[0045] Furthermore, the inverse calculation determines the LAQUA threshold:

[0046] The leaf potassium concentration threshold is substituted into the established diagnostic model to calculate the corresponding LAQUA value threshold, which is then used as the basis for decision-making on potassium fertilizer application in rice.

[0047] Furthermore, the determination of potassium fertilizer application based on the LAQUA threshold:

[0048] In rice production, the last fully expanded leaf at the target growth stage is selected for LAQUA measurement, and the LAQUA correction value is calculated:

[0049] When the LAQUA value is greater than or equal to the threshold for the corresponding period, it is determined that the potassium supply of rice is sufficient and no potassium fertilizer is applied.

[0050] When the LAQUA value is less than the threshold for the corresponding period, it is determined that the potassium supply of rice is insufficient and potassium fertilizer needs to be applied.

[0051] Furthermore, the potassium fertilizer application:

[0052] When it is determined that potassium fertilizer needs to be applied, the method of basal application or top dressing should be selected according to production needs. The type of potassium fertilizer applied can be potassium chloride or equivalent potassium fertilizer, and the application rate should be determined according to the regional recommended application rate or production standards.

[0053] This step ensures that potassium fertilizer is effectively replenished during the critical period when rice requires potassium.

[0054] Another object of the present invention is to provide a system for diagnosing the potassium status of rice and guiding potassium fertilizer management based on the LAQUA value of rice leaves, comprising:

[0055] Gradient processing module, used for setting up potash fertilizer gradient processing;

[0056] The sampling module is used to identify and sample representative leaves of rice.

[0057] The measurement module is used to measure leaf LAQUA value; measure leaf SLW; and measure leaf potassium concentration.

[0058] The calibration index construction module is used to construct the LAQUA calibration index;

[0059] The diagnostic model building module is used to build diagnostic models;

[0060] The threshold determination module is used to determine the potassium nutrient threshold of rice; and to inversely calculate and determine the LAQUA threshold.

[0061] The determination module is used to determine potash fertilizer application based on the LAQUA threshold.

[0062] Potassium fertilizer application module, used for applying potassium fertilizer.

[0063] Another object of the present invention is to provide a computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, causes the processor to perform the steps of the method for diagnosing the potassium status of rice based on the LAQUA value of rice leaves and guiding potassium fertilizer management.

[0064] Another object of the present invention is to provide a computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method for diagnosing the potassium status of rice based on the LAQUA value of rice leaves and guiding potassium fertilizer management.

[0065] Another objective of this invention is to provide an information data processing terminal for implementing the system for diagnosing the potassium status of rice based on the LAQUA value of rice leaves and guiding potassium fertilizer management.

[0066] Based on the above technical solutions and the technical problems solved, please analyze the advantages and positive effects of the technical solution to be protected by this invention from the following aspects:

[0067] First, addressing the technical problems existing in the prior art and the difficulty in solving them, this paper closely analyzes, in conjunction with the technical solution to be protected by this invention and the results and data obtained during the research and development process, how the technical solution of this invention solves the technical problems, and the inventive technical effects brought about by solving these problems. The specific description is as follows:

[0068] This invention aims to provide a method for diagnosing the potassium nutrient status of rice based on the LAQUA value of rice leaves and combined with leaf structural characteristics, thereby guiding potassium fertilizer application. The method uses rice leaves as the detection object, rapidly measuring the potassium ion content in leaf sap using a portable LAQUATwinK-11 instrument. Leaf thickness (or SLW) is introduced as a correction parameter to establish a quantitative relationship between the LAQUA index and leaf potassium concentration. Based on this, a potassium nutrient threshold is constructed to achieve rapid determination and precise management of whether potassium fertilizer application is needed in rice.

[0069] This invention aims to provide a method for diagnosing potassium nutrition based on the LAQUA value of rice leaves and its application in potassium fertilizer management. By introducing a SLW correction mechanism, the accuracy of LAQUA value in characterizing the potassium status of rice is significantly improved, enabling rapid, non-destructive, and precise management of rice potassium fertilizer. The advantages over existing technologies are mainly as follows:

[0070] (1) Significantly improved diagnostic accuracy: This invention found that the correlation between LAQUA value and leaf potassium concentration is limited when used alone, while the correlation between LAQUA value and leaf thickness (LAQUA / SLW) is significantly improved. 2 After that, a highly linear relationship was found between potassium concentration and leaf potassium concentration, which significantly improved the accuracy and stability of potassium diagnosis.

[0071] (2) The method is fast, non-destructive, and suitable for field application: This invention does not require complex laboratory analysis, but only requires the collection of representative leaves for on-site testing, which is suitable for rapid decision-making in the production line.

[0072] (3) Determining thresholds based on yield response is scientifically sound: By analyzing the relationship between leaf potassium status and final yield at different growth stages, the potassium nutrition threshold at key stages is determined, so that potassium fertilizer management directly serves the goal of high yield.

[0073] (4) To make a clear judgment on whether potassium fertilizer needs to be applied: Based on the LAQUA threshold of the last fully unfolded leaf, it is possible to make a clear judgment on whether potassium fertilizer needs to be applied, thus avoiding blind application.

[0074] (5) Improve the efficiency of potassium fertilizer utilization and reduce resource waste: This method can effectively reduce the problems of excessive and insufficient application of potassium fertilizer, improve the efficiency of potassium fertilizer utilization while ensuring yield, and has significant economic and resource security significance.

[0075] Secondly, as supplementary evidence of the inventive step of the claims of this invention, it is also reflected in the following important aspects:

[0076] (1) The expected benefits and commercial value of the technical solution of this invention after transformation are as follows:

[0077] This invention constructs a rapid potassium fertilizer diagnosis and fertilization decision-making method based on the LAQUA value of rice leaves combined with leaf SLW correction, realizing the transformation of potassium fertilizer management from experience-based application to real-time precise judgment. This method can effectively reduce unnecessary potassium fertilizer input, avoid repeated application, improve potassium fertilizer utilization efficiency while ensuring yield, and reduce production costs. Given my country's heavy reliance on imported potassium fertilizer resources, this invention has significant economic and strategic importance in conserving resources and improving fertilizer utilization efficiency. Furthermore, this invention can be integrated with portable testing equipment, agricultural technical service systems, and digital agriculture platforms, possessing good industrialization prospects and commercial promotion value.

[0078] (2) The technical solution of this invention fills a technical gap in the industry both domestically and internationally:

[0079] Existing rapid nutrient diagnostic technologies mainly focus on nitrogen, while a mature method for rapid potassium diagnosis that can be directly applied to field management decisions is lacking. Although portable potassium ion detectors exist, a stable quantitative relationship between them and potassium concentration in rice leaves has not yet been established, nor has a threshold system to guide fertilization decisions been developed. This invention proposes for the first time to combine LAQUA values ​​with leaf structure parameters, by constructing an LAQUA / SLW... 2 The calibration index significantly improves diagnostic accuracy and establishes a potassium nutrient threshold system based on yield response, forming a complete technical path for precision potassium fertilizer management, filling the technical gap in this field both domestically and internationally.

[0080] (3) Whether the technical solution of the present invention solves the technical problem that people have long wanted to solve but have never been able to solve successfully:

[0081] For a long time, rice production has lacked a rapid, non-destructive method for diagnosing potassium levels that can be directly used for fertilization decisions. Soil testing is insufficient to reflect the real-time absorption status of plants, and histochemical analysis is costly and time-consuming, making it unsuitable for field decision-making. Although portable potassium ion detection devices have been applied to solution testing, the interference of leaf structure differences on the measured values ​​has not been resolved, hindering their application to precision fertilization management. This invention introduces a leaf specific weight correction mechanism to eliminate the measurement bias caused by structural differences, successfully establishing a stable and reliable diagnostic model. This enables the effective conversion of rapid detection results into fertilization decisions, breaking through a long-standing technical bottleneck in this field.

[0082] (4) Does the technical solution of the present invention overcome technical bias? Attached Figure Description

[0083] Figure 1 This is a flowchart of a method for diagnosing the potassium status of rice and guiding potassium fertilizer management based on the LAQUA value of rice leaves, provided in an embodiment of the present invention.

[0084] Figure 2 This is a system structure diagram of an embodiment of the present invention for diagnosing the potassium status of rice based on the LAQUA value of rice leaves and guiding potassium fertilizer management.

[0085] Figure 3 This invention presents a correlation analysis between the LAQUA Twin-K-11 and flame spectrophotometry methods for determining leaf potassium content. A: Correlation analysis of leaf potassium concentration and content determined by the two different methods; B: Correlation analysis of leaf potassium concentration and content after removing leaf thickness. FP6420 and SLW represent flame spectrophotometry and specific leaf weight, respectively. ** indicates P ≤ 0.01 significance.

[0086] Figure 4 This invention illustrates the impact of different potassium fertilizer management practices on yield, as provided in the embodiments of the invention. M0, M60, and M90 represent potassium fertilizer application rates of 0, 60, and 90 kg K ha, respectively. -1 Different lowercase letters indicate that P ≤ 0.05 is significant.

[0087] Figure 5 This refers to the dynamic changes in potassium levels in rice leaves provided in this embodiment of the invention; A and B represent two different measurement methods, A is LAQUA and B is FP6420 (flame spectrophotometer method), and * indicates significance at P ≤ 0.05.

[0088] Figure 6 This is the dynamic change of potassium ion concentration in the field water layer provided in the embodiments of the present invention; A and B represent two different measurement methods, A is LAQUA, B is FP6420 (flame spectrophotometer method), * indicates significance at P ≤ 0.05, and the red inverted triangle indicates the fertilization date.

[0089] Figure 7 The LAQUA / SLW provided in this embodiment of the invention 2 Correlation analysis with yield; AC plots represent the panicle differentiation stage, heading stage, and maturity stage, respectively; M0, M60, and M90 represent potassium fertilizer application rates of 0, 60, and 90 kg K ha. -1 , * and ** represent significance for P ≤ 0.05 and P ≤ 0.01, respectively.

[0090] Figure 8 This is a flowchart provided in an embodiment of the present invention.

[0091] Figure 9 This is the specific operation process provided in the embodiments of the present invention. Detailed Implementation

[0092] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0093] like Figure 1 As shown in the figure, the method for diagnosing the potassium status of rice and guiding potassium fertilizer management based on the LAQUA value of rice leaves provided by this invention includes the following steps:

[0094] S101: Potassium fertilizer gradient treatment settings;

[0095] During the rice growing season, three different potassium fertilizer application treatments were set up under the same ecological zone, the same variety, and the same cultivation conditions. The potassium fertilizer application rates were 0, 60, and 90 kg K ha. -1 This step is used to establish different potassium nutrient levels in rice; it is used to construct the differences in leaf potassium levels in rice under different potassium supply conditions, providing basic data for subsequent model building.

[0096] S102: Determination and sampling of representative rice leaves;

[0097] At different growth stages of rice (including panicle differentiation, heading, and maturity), rice plants with uniform growth and free from pests and diseases were selected from each treatment plot as sampling objects. The last fully expanded leaf of each rice plant was selected as the test leaf. The leaf must meet the following conditions: the leaf is fully expanded, and the ligule and leaf sheath are clearly separated; the leaf has no mechanical damage or obvious lesions; the leaf is in the functional leaf stage and can reflect the current potassium status of the plant.

[0098] S103: Determine the LAQUA value of leaves;

[0099] After the last fully unfolded leaf is cut off, it is immediately tested using a portable LAQUA TwinK-11 potassium ion analyzer. The specific operation includes: (1) cutting or pressing the leaf to obtain leaf juice; (2) dripping the juice onto the potassium ion selective electrode detection end of the LAQUA TwinK-11 instrument; (3) reading and recording the corresponding LAQUA value.

[0100] This step is used to obtain a rapid detection index reflecting the soluble potassium content in the leaves;

[0101] S104: Measure the leaf SLW;

[0102] After completing the LAQUA measurement, the uncrushed leaf samples were brought back to the laboratory to determine the leaf structural parameters, including: measuring the leaf area; drying the leaves at 80°C to constant weight; calculating the leaf dry weight per unit leaf area to obtain the leaf SLW, and characterizing the leaf thickness through SLW.

[0103] This step is used to reflect the dilution or enrichment effect of differences in leaf tissue structure on the concentration of ions in leaf sap;

[0104] S105: Determination of potassium concentration in leaves;

[0105] Chemical analysis of the same leaf sample to determine the potassium concentration in the leaf includes: pulverizing the dried leaf sample; processing the sample using the H2O2-H2SO4 digestion method; and determining the potassium content of the leaf using a flame photometer.

[0106] This step serves as a standard reference indicator for potassium nutrition diagnosis and is used in model establishment;

[0107] S106: Construct the LAQUA correction index;

[0108] Based on the obtained data, an LAQUA correction index is constructed, and the correction index is defined as follows:

[0109] LAQUA correction value = LAQUA / SLW 2

[0110] By introducing leaf structure parameters, the deviation of LAQUA measurement values ​​caused by differences in leaf thickness is eliminated, making this correction index more accurately reflect the potassium nutrition level of leaves.

[0111] S107: Establish a diagnostic model;

[0112] S108: Determine the potassium nutrient threshold for rice;

[0113] S109: Inverse calculation to determine the LAQUA threshold;

[0114] S110: Determination of potash fertilizer application based on LAQUA threshold;

[0115] S111: Potassium fertilizer application.

[0116] The diagnostic model provided in this embodiment of the invention:

[0117] Leaf potassium content was used as the independent variable, and the LAQUA correction value (LAQUA / SLW) was used as the correction value. 2 Using ) as the dependent variable, a quantitative fitting model between the two was established, and the model with the highest determination coefficient and the best stability was selected as the rice potassium diagnostic model;

[0118] This model is used to convert the LAQUA index, which is rapidly measured in the field, into quantitative information that can characterize the potassium nutrition status of rice.

[0119] The present invention provides a method for determining the potassium nutrient threshold in rice:

[0120] The relationship between leaf potassium concentration and rice yield was analyzed at different growth stages to determine the minimum leaf potassium concentration level required to achieve high rice yield, which was used as the potassium nutrition threshold for that stage.

[0121] This threshold reflects the actual potassium requirement of rice during key growth stages.

[0122] The inverse calculation method for determining the LAQUA threshold provided in this embodiment of the invention:

[0123] The leaf potassium concentration threshold is substituted into the established diagnostic model to calculate the corresponding LAQUA value threshold, which is then used as the basis for decision-making on potassium fertilizer application in rice.

[0124] The potash fertilizer application determination based on the LAQUA threshold provided in this embodiment of the invention:

[0125] In rice production, the last fully expanded leaf at the target growth stage is selected for LAQUA measurement, and the LAQUA correction value is calculated:

[0126] When the LAQUA value is greater than or equal to the threshold for the corresponding period, it is determined that the potassium supply of rice is sufficient and no potassium fertilizer is applied.

[0127] When the LAQUA value is less than the threshold for the corresponding period, it is determined that the potassium supply of rice is insufficient and potassium fertilizer needs to be applied.

[0128] Potassium fertilizer application provided in this embodiment of the invention:

[0129] When it is determined that potassium fertilizer needs to be applied, the method of basal application or top dressing should be selected according to production needs. The type of potassium fertilizer applied can be potassium chloride or equivalent potassium fertilizer, and the application rate should be determined according to the regional recommended application rate or production standards.

[0130] This step ensures that potassium fertilizer is effectively replenished during the critical period when rice requires potassium.

[0131] like Figure 2 As shown in the figure, an embodiment of the present invention provides a system for diagnosing the potassium status of rice and guiding potassium fertilizer management based on the LAQUA value of rice leaves, comprising:

[0132] Gradient processing module, used for setting up potash fertilizer gradient processing;

[0133] The sampling module is used to identify and sample representative leaves of rice.

[0134] The measurement module is used to measure leaf LAQUA value; measure leaf SLW; and measure leaf potassium concentration.

[0135] The calibration index construction module is used to construct the LAQUA calibration index;

[0136] The diagnostic model building module is used to build diagnostic models;

[0137] The threshold determination module is used to determine the potassium nutrient threshold of rice; and to inversely calculate and determine the LAQUA threshold.

[0138] The determination module is used to determine potash fertilizer application based on the LAQUA threshold.

[0139] Potassium fertilizer application module, used for applying potassium fertilizer.

[0140] The system provided in this invention, based on the LAQUA value of rice leaves, for diagnosing the potassium status of rice and guiding potassium fertilizer management, works by using rapid in-situ detection of leaf parameters as its core, combined with physiological index correction and model inversion methods, to achieve accurate determination and dynamic control of the potassium nutritional status of rice. The system first establishes different potassium fertilizer application gradients in experimental fields or demonstration areas through a gradient processing module, forming a control population covering different nutrient levels such as potassium deficiency, adequate potassium, and potassium enrichment, providing a standardized sample basis for subsequent index calibration and model construction. The sampling module identifies representative functional leaves based on the rice's growth stage and leaf position patterns and collects them in a standardized manner, ensuring sample consistency in both spatial and temporal dimensions. The measurement module uses a portable LAQUA ion detection device to rapidly measure the potassium ion activity in the leaves, simultaneously measuring SLW and leaf potassium concentration to reflect leaf structural characteristics and the true nutrient accumulation status.

[0141] Building upon this foundation, the calibration index construction module couples LAQUA measurements with parameters such as SLW and leaf potassium concentration to construct a LAQUA calibration index that eliminates the influence of differences in leaf thickness and water content. This allows the original ion activity value to more accurately characterize the potassium level per unit leaf area or unit dry matter. The diagnostic model establishment module, based on gradient-processed data and the calibration index, uses regression analysis or response curve fitting methods to establish a functional relationship model between the LAQUA calibration index and rice yield, relative yield, or plant potassium uptake, thereby achieving a quantitative expression of potassium nutritional status. The threshold determination module calculates the corresponding LAQUA calibration index threshold based on the critical potassium concentration corresponding to a preset relative yield (e.g., 90% or 95%), and further converts it into a field-applicable LAQUA reading threshold.

[0142] During the production application stage, the judgment module compares the real-time measured leaf LAQUA value with the threshold. When the detected value is below the threshold, it is determined that potassium is insufficient and potassium fertilizer needs to be applied. When the detected value is within the appropriate range, the current management measures are maintained. If it is significantly higher than the threshold, over-fertilization is avoided. The potassium fertilizer application module outputs fertilizer application suggestions based on the judgment results, realizing precise potassium supplementation and variable fertilization management. Through the collaborative work of the above modules, this system realizes a closed-loop management mechanism of "gradient calibration - index correction - model construction - threshold back calculation - on-site judgment - fertilization execution", which improves potassium fertilizer utilization efficiency, reduces resource waste and environmental risks, and achieves precise diagnosis and scientific regulation of potassium nutrition in rice.

[0143] Specific implementation of the present invention: LAQUA value: refers to the reading obtained by rapidly measuring the potassium ion concentration in plant leaf sap using a portable LAQUA series ion-selective electrode instrument, which is used to reflect the relative content of soluble potassium in the leaves;

[0144] The last fully unfolded leaf: refers to the uppermost functional leaf in rice at a certain growth stage, when the leaf has fully extended, the ligule and leaf sheath are clearly separated, and the morphology is stable. It is a representative leaf position that reflects the current nutrient supply status of the plant.

[0145] Specific leaf weight: refers to the dry matter mass of a leaf per unit leaf area. It is used to characterize the structural features and tissue density of a leaf and is closely related to the internal ion distribution and sap dilution effect of the leaf.

[0146] Leaf potassium concentration: refers to the potassium content in leaves determined by laboratory H2O2-H2SO4 digestion method and flame photometry. It is a standard indicator for measuring the potassium nutrition status of crops.

[0147] Example 1: Establishment of the relationship between LAQUA index and leaf potassium concentration

[0148] Under different potassium fertilizer application rates, the LAQUA value, SLW (leaf potassium level), and leaf potassium concentration were measured in rice leaves. The results showed that the correlation coefficient (r) between LAQUA value and leaf potassium concentration was approximately 0.63; while the LAQUA / SLW ratio... 2 The ratio of potassium concentration in leaves (r) increased to 0.80. Figure 3 This significantly enhances diagnostic accuracy.

[0149] Example 2: Potash fertilizer management effect based on LAQUA threshold

[0150] In a production trial in Xiaogan, Hubei Province, the method of this invention was used to make decisions on potassium fertilizer application. Compared with M0, the yields of treatments M60 and M90 increased significantly, with increases ranging from 8.07% to 14.25%. However, the yield of M60 decreased by 5.41% compared to M90, but this was not statistically significant, and the amount of potassium fertilizer used was reduced by 33.3%. Figure 4 ).

[0151] Example 3: Dynamic changes in potassium levels in leaves

[0152] Under three different potassium fertilizer treatments, the trends of the results obtained using the two determination methods were basically consistent. Figure 5 Potassium in rice leaves decreases significantly with the growth process. The period of rapid accumulation of potassium ions in plants is 30-50 days after transplanting, after which it declines.

[0153] Example 4: Dynamic changes in potassium levels in field water layers

[0154] like Figure 6 The results showed that potassium ions in the field water layer increased significantly 1 day after fertilization, and decreased most rapidly within 7 days after fertilization, followed by a slower rate of decline. Both methods indicated that potassium exists in the field water layer for 50 days, which is the period of rapid potassium accumulation in leaves. Figure 6 This indicates that the heading stage is an important period for potassium fertilizer management.

[0155] Example 5: LAQUA / SLW 2 Establishing the relationship with output

[0156] Establish LAQUA / SLW at different growth stages under different potassium fertilizer treatments. 2 The correlation between potassium content and rice yield was investigated, and the results showed that potassium content at different growth stages had a significant positive correlation with rice yield. Figure 7 The M60 treatment exhibited the steepest slope across all three growth stages, while the M0 and M90 treatments were relatively stable. Furthermore, compared to the M0 treatment, the M60 treatment showed the highest LAQUA / SLW slope at the heading stage. 2 The yield r reached a highly significant level ( Figure 7 B) Explanation of M60 processing for LAQUA / SLW 2 The response is most sensitive to increases in yield, and the heading stage is a sensitive period for strengthening potassium fertilizer management.

[0157] Figure 8 flow chart.

[0158] Figure 9 Specific operating procedures.

[0159] Evidence related to the technical effects obtained by the embodiments of the present invention.

[0160] Example 1: Establishment of the basic gradient model. Conventional indica rice varieties were selected within the same ecological zone, and models with yields of 0, 60, and 90 kg / ha were established. -1Potassium fertilizer treatment involved collecting the last fully expanded leaf at the panicle differentiation stage, heading stage, and maturity stage. Potassium ion levels in the sap were measured in-situ, while leaf area and dry weight were measured in the laboratory. Specific leaf weight was calculated, and a correction index was obtained by dividing the potassium ion level by the square of the specific leaf weight. Simultaneously, leaf potassium concentration was determined using chemical analysis. The results showed that the correlation coefficient between sap potassium ion levels and leaf potassium concentration was 0.63, while the correlation coefficient between the correction index and leaf potassium concentration increased to 0.80, indicating that the structural correction mechanism effectively eliminated concentration deviations caused by differences in leaf thickness.

[0161] Example 2: Stability verification at different growth stages. Based on the data from Example 1, fitting models were independently established for each of the three growth stages. The results showed that the correlation coefficients of the models reached 0.71-0.86 at the heading stage, 0.86 at the panicle differentiation stage, and 0.77-0.84 at the maturity stage, maintaining a high linear correlation in all three stages.

[0162] Example 3: Variety Difference Adaptability Test. Two rice varieties with different plant types were selected and tested under the same potassium fertilizer gradient conditions. The results showed that the potassium ion values ​​in the juice of different varieties differed at the same potassium concentration level. However, after correction for specific leaf weight, the slopes of the two variety models tended to be consistent, and the correlation coefficient between the cross-variety models remained above 0.80, indicating that the structural correction mechanism has good variety adaptability.

[0163] Example 4: Threshold Back-Calculation and Yield Correlation Verification. During the heading stage, the relationship between leaf potassium concentration and final yield was statistically analyzed. A critical value method was used to determine the minimum leaf potassium concentration required to achieve the target yield as a fixed value. This value was substituted into the established model to back-calculate the corresponding correction index threshold. Field measurements were conducted on unfertilized plots. If the correction index was lower than the threshold, potassium fertilizer was applied as topdressing. The results showed that the topdressing treatment increased yield by 8.5%, while in plots with correction indices higher than the threshold, there was no significant difference in yield between the unfertilized and fertilized treatments, indicating that the threshold back-calculation mechanism has practical guiding significance.

[0164] Example 5: Regional Replication Validation. The experiment was repeated in another ecoregion using the same gradient treatment. While the model parameters differed between the two locations, a significant correlation remained between the correction index and leaf potassium concentration, with correlation coefficients exceeding 0.87. Substituting local thresholds into the model for fertilization decisions, the yield differences showed a trend consistent with Example 4, indicating the regional reproducibility of this method.

[0165] Example 6: Comparison with traditional rapid detection methods. The same batch of samples was used for diagnosis using both the direct numerical method and the correction index method for potassium ions in the sap. The direct numerical method had a misjudgment rate of 25% during the maturity stage when leaf thickness varied greatly, while the misjudgment rate of the correction index method decreased to 7%. This result indicates that introducing SLW parameters to form a structural correction mechanism is significantly superior to single rapid detection methods in terms of technical performance.

[0166] The above six embodiments fully disclose the sample selection method, potassium fertilizer gradient setting, detection steps, model establishment method, threshold determination path, and fertilization verification results, enabling those skilled in the art to repeatedly implement the technical solution. Each embodiment focuses on the structure correction mechanism, model construction mechanism, and threshold back-calculation mechanism, verifying its technical feasibility, stability, and significant effect improvement, fully supporting the creativity and practicality of the technical solution.

[0167] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions, and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention, and within the spirit and principles of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for diagnosing potassium levels in rice based on the coupling and correction of potassium ion measurements in leaf sap and leaf structural parameters, characterized in that, Includes the following steps: The last fully expanded leaf of rice was selected as the test object; The potassium ion concentration in the leaf sap was measured. Measure the specific leaf weight of the leaf; The correction index is calculated by dividing the measured potassium ion value by the square of the specific leaf weight. The calibration index is used as a diagnostic parameter to characterize the potassium nutrition level of rice.

2. The method as described in claim 1, characterized in that, The specific leaf weight is the dry weight of the leaf per unit leaf area, which is obtained by drying the leaf to constant weight at 80°C.

3. The method as described in claim 1, characterized in that, The leaves are fully expanded leaves that are free from mechanical damage and disease spots and are in the functional leaf stage.

4. A method for constructing a quantitative diagnostic model for potassium in rice, characterized in that, Includes the following steps: In the same ecological zone, with the same variety and under the same cultivation conditions, potassium fertilizer gradient treatments of 0, 60, and 90 kg per hectare were set up. Leaf potassium concentration and corresponding correction index were measured at different growth stages. A quantitative fitting model between leaf potassium concentration and calibration index was established. The model with the highest coefficient of determination and the best stability was selected as the diagnostic model for potassium levels in rice.

5. The method as described in claim 4, characterized in that, The correction index is the value obtained by dividing the measured potassium ion value by the specific leaf weight.

6. The method as described in claim 4, characterized in that, The potassium concentration of the leaves was determined by flame photometry after the dried and pulverized leaf samples were digested with sulfuric acid and hydrogen peroxide.

7. A method for managing potassium fertilizer in rice based on leaf potassium threshold back calculation, characterized in that, Includes the following steps: The relationship between leaf potassium concentration and rice yield at different growth stages was analyzed to determine the minimum leaf potassium concentration required to achieve high yield as the potassium nutrition threshold for that stage. Substitute the leaf potassium concentration threshold into the established potassium diagnostic model to calculate the corresponding correction index threshold. Determine leaf correction indices at the target growth stage during rice production; When the measured correction index is not lower than the correction index threshold, the potassium supply is considered sufficient. When the measured correction index is lower than the correction index threshold, it is determined that the potassium supply is insufficient and potassium fertilizer is applied.

8. The method as described in claim 7, characterized in that, The target growth period includes the panicle differentiation period, the heading period, and the maturity period.

9. The method as described in claim 7, characterized in that, The potassium fertilizer is applied either as a base fertilizer or as a top dressing.

10. The method as described in claim 7, characterized in that, The potassium fertilizer is potassium chloride or other equivalent potassium fertilizer, and the application rate is determined according to the regional recommended application rate or production standards.

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