CN112098469B - Soil conductivity detection system and method - Google Patents
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- G01N27/00—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means
- G01N27/02—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance
- G01N27/04—Investigating or analysing materials by the use of electric, electrochemical, or magnetic means by investigating impedance by investigating resistance
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- G01R27/00—Arrangements for measuring resistance, reactance, impedance, or electric characteristics derived therefrom
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Abstract
The invention discloses a system and a method for detecting soil conductivity, wherein the system comprises a data acquisition module, a data processing module and a soil conductivity prediction model, wherein the data acquisition module acquires soil sample data, the data processing module carries out data processing on all acquired soil sample data, and the data after the data processing is transmitted to the soil conductivity prediction model. The invention relates to the technical field of soil conductivity detection, in particular to a soil conductivity detection system and a method for realizing real-time prediction of soil conductivity by using multi-frequency-domain reflection frequency.
Description
Technical Field
The invention relates to the technical field of soil conductivity detection, in particular to a system and a method for detecting soil conductivity.
Background
Plants need to absorb nutrients such as various inorganic salts from the soil, and the amount of salt in the soil can directly affect the growth of the plants and can directly affect the yield of crops for agriculture. In practice, higher or lower salt content of the soil is detrimental to crop growth. Soil is low in salt content, and plants cannot fully absorb enough nutrition for growth; too high salt content in the soil can cause the problem of excessive salinization of the soil, thereby causing soil pollution and the like. Therefore, the soil salinity is monitored and the precise fertilization management is carried out by a scientific method, the fertilizer application efficiency is improved, the waste and the overuse are reduced, the economic cost can be saved, the crop yield is improved, and the pollution to the environment can be reduced.
At present, the conductivity of soil is commonly used in agriculture to evaluate the actual salt content of the soil. In a certain concentration range, the salt content of the soil solution is positively correlated with the conductivity, and the higher the salt content of the soil is, the higher the conductivity value is. In the prior art, the conductivity measurement can obtain a relatively accurate result under laboratory conditions, but the measurement process is relatively complex, time-consuming and relatively high in cost, and the requirement of large-area real-time measurement cannot be met. The precision of the soil conductivity measurement equipment applied to farmland sites is relatively low, so that the method which is more convenient, efficient and accurate in development and can monitor the soil conductivity in situ in real time is developed, so that the difference of space-time distribution of various field conductivities is determined, and the method has great significance in popularizing and popularizing modern fine agriculture.
Disclosure of Invention
Aiming at the situation, in order to overcome the defects of the prior art, the invention provides a soil conductivity detection system and a method for realizing real-time prediction of soil conductivity by using multi-frequency-domain reflection frequency.
The technical scheme adopted by the invention is as follows: the invention discloses a soil conductivity detection system, which comprises a data acquisition module, a data processing module and a soil conductivity prediction model, wherein the data acquisition module acquires soil sample data, the data processing module performs data processing on all acquired soil sample data, and the data after data processing is transmitted to the soil conductivity prediction model.
Further, the soil sample data includes frequency domain reflection frequencies of the soil under different water content and salt content conditions and corresponding soil conductivity data thereof.
Further, the soil conductivity prediction model adopts a reverse neural network structure, and the reverse neural network structure comprises an hidden layer, three hidden nodes and an output layer.
The invention also discloses a soil conductivity detection method, which comprises the following steps:
step one, collecting soil sample data, including frequency domain reflection frequencies of soil under different water content and salt content conditions and corresponding soil conductivity data, and storing the data in a data acquisition module;
collecting surface farmland soil samples at different geographic positions of the whole country, controlling the volume water content of the soil samples to be less than 5%, grinding the soil samples, and sieving the ground soil samples with a 2mm sieve for later use;
weighing 22kg of soil samples of 3 equal parts of each soil, gradually adding 300g of distilled water, 300g of 1% sodium chloride solution and 300g of 2% sodium chloride solution into the soil samples of 3 equal parts, uniformly mixing, measuring the frequency domain reflection values of the soil at each frequency by using a fixed tool and two frequency acquisition devices with different frequencies, recording the soil frequency after the data are stable, and storing the soil frequency to a data acquisition module; then, measuring the real volume water content of each group of soil in the 3 equal parts of soil samples by using a differential weight method; each group of 3 aliquots of soil samples weighed 20.00g of dried soil sample at 1m: adding distilled water in a proportion of 5V, oscillating for 30 minutes, filtering and extracting filtrate, measuring the actual conductivity value of the filtrate by adopting a conductivity meter, recording stable conductivity reading, and storing the stable conductivity reading into a data acquisition module;
step two, carrying out normalization data processing on all obtained soil frequencies by utilizing a data processing module based on frequencies in air and in sodium chloride solution with the concentration of 1%;
based on the frequencies measured by the frequency acquisition equipment in the air and the sodium chloride solution with the concentration of 1%, the frequencies obtained by all experiments are subjected to normalized data processing through a data processing module, and the processing formula is as follows:
SF is the normalized frequency value; f (F) a A frequency measured in air for the frequency acquisition device; f (F) s Soil frequency measured for the frequency acquisition device; f (F) sw For frequency acquisition devicesFrequency measured in 1% sodium chloride brine;
step three, taking normalized data as an input independent variable, taking measured soil conductivity as an output dependent variable, and establishing a soil conductivity prediction model by adopting a back propagation neural network algorithm;
based on the frequency domain reflection values of the soil at each frequency, which are acquired by two frequency acquisition devices with different frequencies, normalization is carried out by the method, normalized frequency data is taken as an input independent variable, the measured soil conductivity is taken as an output dependent variable, a back propagation neural network structure shown in figure 2 is adopted for training, a prediction model of the soil conductivity is established, a tan sig activation function is used as a transfer function for an implicit layer, an output layer function is a linear function, a Levenberg-Marquardt training algorithm is adopted for training the model, the learning rate is 0.1, and the iteration times are 1000 times;
according to the obtained soil data, a K-weight cross validation method is adopted to conduct soil conductivity prediction model training, the training result is shown in fig. 3 and 4, the fitting goodness R2 of a training set is 0.86, the root mean square error is 0.223S/m, the R2 of a validation set is 0.93, the RMSE is 0.170S/m, and the soil conductivity prediction result with higher precision is achieved.
Further, the two frequency acquisition devices with different frequencies have 3 groups of frequencies, one of the two frequency acquisition devices with different frequencies is 147MHz and 20MHz, and the other of the two frequency acquisition devices with different frequencies is 14MHz.
The beneficial effects obtained by the invention by adopting the structure are as follows: according to the scheme, the analysis and prediction model is established by adopting a plurality of frequency cooperations, and due to the fact that farmland environments are complex and differences exist among soil types, measurement deviation of single frequency possibly caused by environmental factors can be reduced by adopting a plurality of frequency point detection, and the prediction precision of soil conductivity is improved; according to the scheme, a neural network algorithm is adopted to establish a soil conductivity prediction model, so that high-precision prediction of soil conductivity is realized; the scheme can be directly carried on the existing equipment, 3 frequencies are integrated on one piece of equipment, and real-time, in-situ and sustainable soil conductivity monitoring is realized.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a soil conductivity detection system of the present invention;
FIG. 2 is a diagram of a reverse neural network for a soil conductivity detection method according to the present invention;
FIG. 3 is a graph of training set results fitted with a back propagation neural network algorithm for a soil conductivity detection method of the present invention;
FIG. 4 is a graph of results of a validation set fitted by a back propagation neural network algorithm for a soil conductivity detection method of the present invention.
The system comprises a data acquisition module, a data processing module, a soil conductivity prediction model and a data processing module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," "outer," and the like indicate or are based on the orientation or positional relationship shown in the drawings, merely to facilitate description of the present invention and to simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
As shown in fig. 1-4, the soil conductivity detection system of the invention comprises a data acquisition module 1, a data processing module 2 and a soil conductivity prediction model 3, wherein the data acquisition module 1 acquires soil sample data, the data processing module 2 performs data processing on all acquired soil sample data, and transmits the data after data processing to the soil conductivity prediction model 3.
Further, the soil sample data includes frequency domain reflection frequencies of the soil under different water content and salt content conditions and corresponding soil conductivity data thereof.
Further, the soil conductivity prediction model 3 adopts a reverse neural network structure, and the reverse neural network structure comprises an hidden layer, three hidden nodes and an output layer.
The invention also discloses a soil conductivity detection method, which comprises the following steps:
step one, collecting soil sample data, including frequency domain reflection frequencies of soil under different water content and salt content conditions and corresponding soil conductivity data;
collecting surface farmland soil samples at different geographic positions throughout the country, removing impurities such as stones and tree roots, air-drying in natural environment, controlling the volume water content of the soil samples to be within 5%, grinding the soil samples, and sieving with a 2mm sieve for later use;
weighing 22kg of soil samples of 3 equal parts for each soil, wherein two frequency acquisition devices with different frequencies adopted in the experiment are INSENTER, the model is intelligent soil moisture ETY-40, the soil sample has 3 groups of frequencies, one frequency is 147MHz and 20MHz, and the other frequency is 14MHz;
adding 300g of distilled water into a first soil sample step by step and uniformly mixing, measuring the frequency domain reflection value of each frequency of soil by using a fixed tool and equipment after adding distilled water each time, recording the soil frequency after the data are stable, taking 2 parts of 100ml of soil by using a ring cutter after the frequency data are collected, putting the soil into an aluminum box, then drying the soil in an oven at 105 ℃ for 12 hours, measuring the real volume water content of each group of soil according to a differential weight method, weighing 20.00g of dried soil samples, and weighing the soil samples according to a weight ratio of 1m: adding distilled water in a proportion of 5V, oscillating for 30 minutes, filtering and extracting filtrate, measuring the actual conductivity value of the filtrate by using a HANNA 8733 conductivity meter, and recording stable conductivity reading;
sequentially adding 300g of sodium chloride solution with the concentration of 1% into a second soil sample, uniformly mixing, measuring the frequency domain reflection value of soil by using the same tool and equipment after adding the sodium chloride solution each time, recording the soil frequency after stabilizing the data, taking 2 parts of 100ml of soil by using a ring cutter after collecting the frequency data, putting the soil into an aluminum box, drying the aluminum box in an oven at 105 ℃ for 12 hours, measuring the real volume water content of each group of soil according to a differential weight method, and measuring the soil conductivity value of each group by adopting the same method;
and sequentially adding 300g of sodium chloride solution with the concentration of 2% into a third soil sample, uniformly mixing, measuring the frequency domain reflection value of soil by using a tool and equipment fixed by the same light after adding the sodium chloride solution each time, and recording the soil frequency after the data are stable. After the frequency data are collected, 2 parts of 100ml soil are taken by a ring cutter and put into an aluminum box, then the aluminum box is dried in an oven at 105 ℃ for 12 hours, the real volume water content of each group of soil is measured according to a differential weight method, and the soil conductivity value of each group is measured by adopting the same method;
before experimental measurement is carried out every day, two frequency acquisition devices with different frequencies are respectively placed in a salt solution containing sodium chloride with the concentration of 1% and air, corresponding frequency domain reflection data are measured and acquired, after the data are stabilized, the data are recorded, the test environment is required to be carried out under an unmanned condition during the data acquisition, the generation of human interference is avoided, and at least 10 minutes of stable measurement data are ensured;
step two, carrying out normalization data processing on all obtained soil frequencies by utilizing a data processing module based on frequencies in air and in sodium chloride solution with the concentration of 1%;
based on the frequencies measured by the frequency acquisition equipment in the air and the sodium chloride solution with the concentration of 1%, the frequencies obtained by all experiments are subjected to normalized data processing through a data processing module, and the processing formula is as follows:
SF is the normalized frequency value; f (F) a A frequency measured in air for the frequency acquisition device; f (F) s Soil frequency measured for the frequency acquisition device; f (F) sw A frequency measured for the frequency acquisition device in 1% sodium chloride brine;
step three, taking normalized data as an input independent variable, taking measured soil conductivity as an output dependent variable, and establishing a soil conductivity prediction model by adopting a back propagation neural network algorithm;
based on the frequency domain reflection values of the soil at each frequency, which are acquired by two frequency acquisition devices with different frequencies, normalization is carried out by the method, normalized frequency data is taken as an input independent variable, the measured soil conductivity is taken as an output dependent variable, a back propagation neural network structure shown in figure 2 is adopted for training, a prediction model of the soil conductivity is established, a tan sig activation function is used as a transfer function for an implicit layer, an output layer function is a linear function, a Levenberg-Marquardt training algorithm is adopted for training the model, the learning rate is 0.1, and the iteration times are 1000 times;
according to the obtained soil data, a K-weight cross validation method is adopted to conduct soil conductivity prediction model training, the training result is shown in fig. 3 and 4, the fitting goodness R2 of a training set is 0.86, the Root Mean Square Error (RMSE) is 0.223S/m, the R2 of the validation set is 0.93, and the RMSE is 0.170S/m, and therefore the soil conductivity prediction result with higher precision is achieved.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
While the fundamental and principal features of the invention and advantages of the invention have been shown and described, it will be apparent to those skilled in the art that the invention is not limited to the details of the foregoing exemplary embodiments, but may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (3)
1. The soil conductivity detection method is characterized by comprising the following steps of:
step one, collecting soil sample data, including frequency domain reflection frequencies of soil under different water content and salt content conditions and corresponding soil conductivity data, and storing the data in a data acquisition module;
collecting surface farmland soil samples at different geographic positions of the whole country, controlling the volume water content of the soil samples to be less than 5%, grinding the soil samples, and sieving the ground soil samples with a 2mm sieve for later use;
weighing 3 equal parts of 22kg of soil samples for each soil, gradually adding 300g of distilled water, 300g of 1% sodium chloride solution and 300g of 2% sodium chloride solution into the 3 equal parts of soil samples respectively, uniformly mixing, measuring the frequency domain reflection values of the soil at each frequency by using a fixed tool and two frequency acquisition devices with different frequencies, recording the soil frequency after the data are stable, and storing the soil frequency into a data acquisition module; then, measuring the real volume water content of each group of soil in the 3 equal parts of soil samples by using a differential weight method; each group of 3 aliquots of soil samples weighed 20.00g of dried soil sample at 1m: adding distilled water in a proportion of 5V, oscillating for 30 minutes, filtering and extracting filtrate, measuring the actual conductivity value of the filtrate by adopting a conductivity meter, recording stable conductivity reading, and storing the stable conductivity reading into a data acquisition module;
step two, carrying out normalization data processing on all obtained soil frequencies by utilizing a data processing module based on frequencies in air and in sodium chloride solution with the concentration of 1%;
based on the frequencies measured by the frequency acquisition equipment in the air and the sodium chloride solution with the concentration of 1%, the frequencies obtained by all experiments are subjected to normalized data processing through a data processing module, and the processing formula is as follows:
SF is the normalized frequency value; f (F) a A frequency measured in air for the frequency acquisition device; f (F) s Soil frequency measured for the frequency acquisition device; f (F) sw A frequency measured for the frequency acquisition device in 1% sodium chloride brine;
step three, taking normalized data as an input independent variable, taking measured soil conductivity as an output dependent variable, and establishing a soil conductivity prediction model by adopting a back propagation neural network algorithm;
based on the frequency domain reflection values of the soil at each frequency, which are acquired by two frequency acquisition devices with different frequencies, normalization is carried out by the method, normalized frequency data is taken as an input independent variable, the measured soil conductivity is taken as an output dependent variable, a reverse propagation neural network structure is adopted for training, a prediction model of the soil conductivity is established, a tan sig activation function is used as a transfer function for an implicit layer, an output layer function is a linear function, a Levenberg-Marquardt training algorithm is adopted for training the model, the learning rate is 0.1, and the iteration times are 1000 times;
according to the obtained soil data, a K-weight cross verification method is adopted to conduct soil conductivity prediction model training, the fitness R2 of a training set is 0.86, the root mean square error is 0.223S/m, the R2 of the verification set is 0.93, and the RMSE is 0.170S/m, so that a higher-precision soil conductivity prediction result is achieved;
the soil conductivity detection method uses a soil conductivity detection system, wherein the soil conductivity detection system comprises a data acquisition module, a data processing module and a soil conductivity prediction model, the data acquisition module acquires soil sample data, the data processing module carries out data processing on all acquired soil sample data and transmits the data after the data processing to the soil conductivity prediction model;
the soil sample data comprises frequency domain reflection frequencies of soil under different water content and salt content conditions and corresponding soil conductivity data.
2. The method for detecting the soil conductivity according to claim 1, wherein the soil conductivity prediction model adopts a reverse neural network structure, and the reverse neural network structure comprises an hidden layer, three hidden nodes and an output layer.
3. The method according to claim 1, wherein the two different frequency acquisition devices have 3 sets of frequencies, one of the two different frequency acquisition devices has 147MHz and 20MHz, and the other of the two different frequency acquisition devices has 14MHz.
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