CN117251004A - PH value control system for plant growth - Google Patents
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Abstract
The invention relates to the field of PH value control, in particular to a PH value control system for plant growth, which comprises a data collection module, a PH value prediction module, a prediction result processing module and a visualization tool, wherein the data collection module collects real-time plant growth data by using a sensor, the real-time plant growth data comprises different data characteristics, the public data set collects different historical plant growth data, the historical plant growth data comprises the same data characteristics as the real-time plant growth data, the specified PH value is used as a tag column of the historical plant growth data, the modeling module carries out model training by using a decision tree algorithm according to the relation between the different data characteristics and the tag column in the different historical plant growth data, the PH value prediction module predicts and records the data affecting the specified PH value by using the trained decision tree model, and the prediction result processing module carries out visual display of different charts by using the predicted specified PH value and the data affecting the specified PH value.
Description
Technical Field
The invention relates to the field of PH value control, in particular to a PH value control system for plant growth.
Background
Conventional plant growth PH control systems typically use basic control principles and techniques to regulate and maintain PH in a plant growth environment, which operate based on fixed rules and thresholds, and thus, conventional plant growth PH control systems tend to suffer from several drawbacks.
On the one hand, the traditional PH value control system for plant growth often decides whether to need to be regulated according to the difference between the set target PH value and the actual measured value, and cannot automatically adapt to different conditions generated during plant growth;
on the other hand, the conventional PH control system for plant growth often requires a large number of professionals to manually analyze the relationship between plant growth data and PH, which not only results in misjudgment due to subjective factors, but also requires a large amount of human resources.
Disclosure of Invention
The present invention is directed to a PH control system for plant growth, which solves the above-mentioned problems of the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions: the PH value control system for plant growth comprises a data collection module, a modeling module, a PH value prediction module and a prediction result processing module, wherein:
the data collection module includes a sensor to collect real-time plant growth data including different data characteristics and a public data set to collect different historical plant growth data including the same data characteristics as the real-time plant growth data and a tag column of the historical plant growth data with a specified PH value.
The modeling module performs model training according to the relations between different data features and tag columns in different historical plant growth data, the PH value prediction module performs specified PH value prediction on the real-time plant growth data by using a trained decision tree model, records data affecting the specified PH value according to characteristic records of the decision tree algorithm and analysis on different data in the real-time plant growth data, and the prediction result processing module performs visual display of different charts by using a visual tool.
As a further improvement of the technical scheme, the data collection module comprises a sensor unit, and the sensor unit collects real-time plant growth data by using a sensor, wherein the sensor comprises a PH value sensor, a biological height sensor, an illumination sensor, a temperature and humidity sensor, a blade color sensor and an image sensor, and sends the collected real-time plant growth data and the current PH value in the data to the PH value prediction module and the prediction result processing module.
As a further improvement of the technical scheme, the data collection module comprises a historical data collection unit, wherein the historical data collection unit collects different historical plant growth data by using a research institution, an agricultural monitoring system and a gardening database, extracts data characteristics in the historical plant growth data, takes a PH value specified during plant growth therein as a tag column of the historical plant growth data, and sends the collected historical plant growth data to the modeling module.
As a further improvement of the technical scheme, the modeling module comprises a historical data receiving unit and a model training unit, wherein the historical data receiving unit receives historical plant growth data sent by the historical data collecting unit, is used for carrying out standardized processing on the data and sends the processed data to the model training unit; the model training unit performs model training according to historical plant growth data by utilizing a decision tree algorithm, and sends the trained model to the PH value prediction module.
As a further improvement of the technical scheme, the PH value prediction module comprises a sensor data receiving unit and a model application unit, wherein the sensor data receiving unit receives the real-time plant growth data sent by the sensor unit, performs characteristic processing on image data in the data, and sends the processed data to the model application unit; the model application unit predicts the PH value and the data affecting the PH value of the real-time plant growth data sent by the sensor data receiving unit by using the model trained by the model training unit, and sends the predicted result to the predicted result processing module.
As a further improvement of the technical scheme, the predicted result processing module comprises a predicted result receiving unit, a PH value adjusting unit and a predicted result sorting unit, wherein the predicted result receiving unit receives the current PH value and the predicted result which are respectively transmitted by the sensor unit and the model application unit, transmits the predicted PH value in the current PH value and the predicted result to the PH value adjusting unit, and transmits the predicted result to the predicted result sorting unit; the PH value adjusting unit is used for adjusting the PH value of the current plant according to the comparison of the current PH value and the predicted PH value; the predicted result sorting unit performs visual display of different charts on the data of the sum of the PH values in the predicted result and the data affecting the PH values by using a visual tool.
As a further improvement of the present technical solution, the sensor unit collects real-time plant growth data by means of the sensor, the real-time plant growth data comprising different data characteristics, specifically comprising:
PH value sensor: the collected data is the current plant growth PH value, which represents the PH value of the substance;
biological height sensor: the data collected are numerical values of plant height, representing the vertical growth of plants per unit time;
illumination sensor: the collected data is a numerical value of illumination intensity, representing the intensity level of visible light in the environment;
temperature and humidity sensor: the data collected are the temperature and humidity values of the environment;
blade color sensor: the data collected are values of leaf color for assessing the health and nutritional status of the plant;
an image sensor: the data collected are images of plants, which are used to analyze and measure the morphology, quantity and color of the plants
As a further improvement of the technical scheme, the model training unit trains the model by utilizing a decision tree algorithm through data division, model training, model evaluation and model tuning.
As a further improvement of the technical scheme, the model application unit predicts the PH value of the real-time plant growth data by using a trained model, and specifically comprises the following steps:
sequentially comparing the characteristic value of the sample with the value of the partition characteristic corresponding to the node from the root node, moving the sample along the corresponding child node according to different values, and recording the moved node data;
continuing to repeat the previous step, and moving the sample to the next child node according to the dividing characteristics and the value of the current node until the leaf node is reached, wherein the leaf node corresponds to the value of the prescribed PH value;
and finally, outputting the predicted PH value and recording the mobile node data, wherein the node data is the data affecting the PH value.
As a further improvement of the technical scheme, the prediction result arranging unit utilizes a data visualization library matplotlib in Python to conduct various chart displays of a line graph, a thermodynamic diagram and a scatter diagram on data.
Compared with the prior art, the invention has the beneficial effects that:
1. the PH value control system for plant growth firstly utilizes different sensors to collect real-time plant growth data, the real-time plant growth data comprise different data characteristics, utilizes a research institution, an agricultural monitoring system and a gardening database to collect different historical plant growth data, the historical plant growth data comprise the same data characteristics as the real-time plant growth data, and takes a specified PH value as a tag column of the historical plant growth data, and utilizes a decision tree algorithm to carry out model training according to the different historical plant growth data, and utilizes a trained decision tree model to carry out prediction of the specified PH value on the real-time plant growth data, so that plant growth can be effectively regulated in different and complex environments, and the vitality of plant growth is increased;
2. the PH value control system for plant growth predicts the PH value according to the real-time plant growth data by using a trained decision tree algorithm model, predicts the data influencing the PH value, and performs various chart displays of a line graph, a thermodynamic diagram and a scatter diagram on the data by using a data visualization library matplotlib in Python, thereby reducing errors caused by manual data arrangement, saving human resources and improving the working efficiency.
Drawings
FIG. 1 is a schematic diagram of the overall module of the present invention;
FIG. 2 is a schematic diagram of a data collection module unit according to the present invention;
FIG. 3 is a schematic diagram of a modeling module unit of the present invention;
FIG. 4 is a schematic diagram of a pH prediction module unit according to the present invention;
FIG. 5 is a schematic diagram of a prediction result processing module unit according to the present invention;
in the figure: 100. a data collection module; 101. a sensor unit; 102. a history data collection unit; 200. a modeling module; 201. a history data receiving unit; 202. a model training unit; 300. a PH value prediction module; 301. a sensor data receiving unit; 302. a model application unit; 400. a prediction result processing module; 401. a prediction result receiving unit; 402. a pH value adjusting unit; 403. and a prediction result sorting unit.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. 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.
Referring to fig. 1-5, the present invention provides a technical solution: a PH value control system for plant growth comprises a data collection module 100, a modeling module 200, a PH value prediction module 300 and a prediction result processing module 400.
In order to collect real-time plant growth data, the sensor unit 101 in the data collection module 100 collects real-time plant growth data using sensors including PH sensors, biological height sensors, illumination sensors, temperature and humidity sensors, leaf color sensors, image sensors, and the like, including:
PH value sensor: the collected data is a digital value (current plant growth PH value) which indicates the PH value of the substance, for example, a PH value sensor collects liquid sample data with the PH value of 6.8 which indicates that the liquid is slightly acidic;
biological height sensor: the data collected is a numerical value of plant height representing the vertical growth of the plant per unit time, e.g., a biological height sensor records that the plant has grown 2 cm in 24 hours;
illumination sensor: the collected data is a value of illumination intensity, which indicates the intensity level of visible light in the environment, for example, the illumination intensity recorded by the illumination sensor into the environment is 10000 lux, which indicates that the environment receives stronger natural light;
temperature and humidity sensor: the collected data are environmental temperature and humidity values, for example, a temperature and humidity sensor records the data that the environmental temperature is 25 ℃ and the relative humidity is 60%;
blade color sensor: the data collected is a leaf color value for assessing the health and nutritional status of the plant, e.g., leaf color sensors record a leaf color index of 42, indicating that the plant leaf exhibits good green;
an image sensor: the data collected are images of the plant which are used to analyze and measure the morphology, number and color of the plant, for example, the image sensor captures an image of a plant showing that the plant has 3 leaves which are dark green in color.
The sensor unit 101 sends the collected real-time plant growth data to the sensor data receiving unit 301 in the PH prediction module 300, and in order for the data to satisfy the input type of the model, it is necessary to perform feature processing on the image data in the real-time pavement data, which specifically includes:
image sensor data: firstly, preprocessing a plant image, including denoising, adjusting contrast and brightness, extracting the outline and boundary of the plant through morphological operation and an edge detection algorithm, then, applying an image segmentation algorithm to segment different areas in the plant image so as to measure the area and perimeter of the plant, simultaneously, using a feature extraction algorithm to obtain texture, color and shape features in the image for distinguishing different species and measuring color deviation, finally, applying a color recognition algorithm to convert the color information of the image into digital features for measuring the color and color distribution of plant leaves, and using the algorithms and methods to realize intelligent processing and analysis of the plant image to obtain data about the shape, the number and the color, and taking the data as a feature array of real-time plant growth data.
The sensor data receiving unit 301 sends the processed real-time plant growth data to the model application unit 302 for predicting the PH value, before the prediction, the model needs to be trained, and the training model needs to be trained by using the historical plant growth data as a basis of model training, so that the historical data collecting unit 102 in the data collecting module 100 collects different historical plant growth data by using public data sets such as a research institution, an agricultural monitoring system and a gardening database, and extracts data features in the historical plant growth data, the extracted data features are real-time plant growth data features, so as to ensure uniformity of model input, and the PH value (the PH value specified during plant growth) is used as a tag column of the historical plant growth data, so that the model performs supervised model training, and sends the collected historical plant growth data to the historical data receiving unit 201 in the modeling module 200, wherein the supervised model training indicates that a function or model can be learned according to a relation between input features in the training data set and output tags, and the function or model can map given input to predicted output, and the training target is that parameters or weights are adjusted to make the model accurately represent the model or the model does not have the best performance of the model in the new sample.
In order to reduce the influence of abnormal values and noise on the model, improve the robustness of the model to the abnormal values, reduce the interference of model training and prediction, thereby improving the performance and stability of the model, the historical data receiving unit 201 calculates the mean value and standard deviation of the feature columns in the historical plant growth data, subtracts the mean value from the original value, divides the standard deviation, converts the data into a distribution with the mean value of 0 and the standard deviation of 1, keeps the feature weights balanced, and sends the normalized historical plant growth data to the model training unit 202, and the model training unit 202 performs model training according to the historical plant growth data by using a decision tree algorithm in a machine learning algorithm, and specifically comprises:
dividing data: dividing the data set into two parts, wherein 80 percent is a training set, 20 percent is a testing set, the training set is used for model training, and the testing set is used for model evaluation;
model training: selecting a feature with the smallest coefficient of the data as a root node, the calculation formula of the coefficient of Kerning is: gini=1- Σ (pi)/(2), where Gini is a coefficient of kunning, used to measure the purity and uncertainty of a feature; pi is the probability of a certain class, representing the probability that the sample belongs to that class; sigma is a sum symbol representing the sum of probabilities for all classes; (pi)/(2) represents the square of the probability of a certain class, i.e. the probability of that class multiplied by itself;
then, continuing dividing each sub-node under the root node according to the size of the coefficient of the radix, and continuously iterating until the node reaches a pure state, namely, stopping growing when samples in the node belong to the same category;
model evaluation: calculating the number of accurately predicted samples between the label array predicted by the model and the label array of the test set, dividing the number of accurately predicted samples by the number of samples of the test set, multiplying the number of samples by the percentage to obtain the accuracy, and if the accuracy is more than or equal to 90 percent, successfully training the model, otherwise, performing model tuning;
and (3) model tuning: for each leaf node, calculating the prediction performance difference, namely the change of accuracy, of the test set before and after pruning;
if the performance of the pruned tree is not reduced, pruning the leaf node into a father node of the leaf node, predicting the node as the category with highest frequency on the test set, otherwise, judging the performance of the next leaf node after pruning;
pruning is gradually performed upwards until pruning is no longer possible, i.e. the performance after pruning of all leaf nodes results in an accuracy rate of < 90 percent.
The model training unit 202 sends the trained model to the model application unit 302 in the PH prediction module 300 for model application, and the model application unit 302 predicts the PH of the real-time plant growth data by using the trained model, which specifically includes:
sequentially comparing the characteristic value of the sample with the value of the partition characteristic corresponding to the node from the root node, moving the sample along the corresponding child node according to different values, and recording the moved node data;
continuing to repeat the previous step, and moving the sample to the next child node according to the dividing characteristics and the value of the current node until the leaf node is reached, wherein the leaf node corresponds to the value of the PH value;
and finally, outputting the predicted PH value and recording the mobile node data, wherein the node data is the data affecting the PH value.
The model application unit 302 sends the predicted PH value result and the recorded node data to the predicted result receiving unit 401 in the predicted result processing module 400, and the predicted result receiving unit 401 receives the real-time PH value collected by the PH value sensor in the sensor unit 101, and sends the PH value in the predicted result and the real-time PH value to the PH value adjusting unit 402, where the PH value adjusting unit 402 adjusts the current PH value according to the comparison size of the predicted PH value and the real-time PH value, and specifically includes:
if the predicted pH is less than the real-time pH, indicating that the medium is too alkaline, the pH is lowered by adding an acidic solution, and vice versa, if the predicted pH is greater than the real-time pH, an alkaline solution is added to raise the pH.
In order to save the time for a inspector to manually sort the relationship between the data features and the predicted results, and improve the working efficiency, the predicted result receiving unit 401 sends the predicted PH value result and the recorded node data to the predicted result sorting unit 403, and the predicted result sorting unit 403 performs visual presentation of the data by using a visualization tool (for example, a data visualization library matplotlib in Python), which specifically includes:
line graph: taking time as a horizontal axis and a predicted result (PH value) as a vertical axis, drawing a line graph, displaying the PH value condition changing along with time, and knowing the change trend of the PH value by a detector through the trend and the change amplitude of the line graph;
thermodynamic diagrams: displaying the recorded mobile node data and the corresponding predicted result in a thermodynamic diagram form, and representing the intensity and degree of the recorded node data and the predicted result through the color change, so that a detector can intuitively see the influence of important data characteristics on the PH value;
scatter plot: and different dimensions of the data are displayed in a scatter diagram form, and detection results are represented by points with different colors or sizes, so that detection personnel can find out the association relation between the data characteristics and the prediction results.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (10)
1. A PH control system for plant growth, characterized by: the system comprises a data collection module (100), a modeling module (200), a PH value prediction module (300) and a prediction result processing module (400), wherein:
the data collection module (100) includes a sensor to collect real-time plant growth data including different data characteristics and a public data set to collect different historical plant growth data including the same data characteristics as the real-time plant growth data and having a specified PH as a tag column for the historical plant growth data;
the modeling module (200) performs model training according to the relations between different data features and tag columns in different historical plant growth data, the PH value prediction module (300) performs prediction of a specified PH value on real-time plant growth data by using a trained decision tree model, and records data affecting the specified PH value according to characteristic records of the decision tree algorithm and analysis of different data in the real-time plant growth data, and the prediction result processing module (400) performs visual display of different charts on the predicted specified PH value and the data affecting the specified PH value by using a visual tool.
2. The plant growth PH control system according to claim 1, wherein: the data collection module (100) comprises a sensor unit (101), wherein the sensor unit (101) collects real-time plant growth data by using a sensor, the sensor comprises a PH value sensor, a biological height sensor, an illumination sensor, a temperature and humidity sensor, a blade color sensor and an image sensor, and the collected real-time plant growth data and the current PH value in the data are sent to the PH value prediction module (300) and the prediction result processing module (400).
3. The plant growth PH control system according to claim 2, wherein: the data collection module (100) comprises a historical data collection unit (102), wherein the historical data collection unit (102) collects different historical plant growth data by utilizing a research institution, an agricultural monitoring system and a gardening database, extracts data characteristics in the historical plant growth data, takes a PH value specified during plant growth therein as a tag column of the historical plant growth data, and sends the collected historical plant growth data to the modeling module (200).
4. A PH control system for plant growth according to claim 3, wherein: the modeling module (200) comprises a historical data receiving unit (201) and a model training unit (202), wherein the historical data receiving unit (201) receives historical plant growth data sent by the historical data collecting unit (102) and is used for carrying out standardized processing on the data and sending the processed data to the model training unit (202); the model training unit (202) performs model training according to historical plant growth data by utilizing a decision tree algorithm, and sends the trained model to the PH value prediction module (300).
5. The plant growing PH control system of claim 4, wherein: the PH value prediction module (300) comprises a sensor data receiving unit (301) and a model application unit (302), wherein the sensor data receiving unit (301) receives real-time plant growth data sent by the sensor unit (101), performs characteristic processing on image data in the data, and sends the processed data to the model application unit (302); the model application unit (302) predicts the PH value and the data affecting the PH value of the real-time plant growth data sent by the sensor data receiving unit (301) by using the model trained by the model training unit (202), and sends the predicted result to the predicted result processing module (400).
6. The plant growing PH control system of claim 5, wherein: the prediction result processing module (400) comprises a prediction result receiving unit (401), a PH value adjusting unit (402) and a prediction result sorting unit (403), wherein the prediction result receiving unit (401) receives the current PH value and the prediction result which are respectively transmitted by the sensor unit (101) and the model application unit (302), transmits the current PH value and the predicted PH value in the prediction result to the PH value adjusting unit (402), and transmits the prediction result to the prediction result sorting unit (403); the PH value adjusting unit (402) is used for adjusting the PH value of the current plant according to the comparison of the current PH value and the predicted PH value; the predicted result sorting unit (403) uses visualization tools to perform visual display of different charts on the data of the sum of the PH values in the predicted result, wherein the data affects the PH values.
7. The plant growth PH control system according to claim 2, wherein: the sensor unit (101) utilizes sensors to collect real-time plant growth data, including different data characteristics, including in particular:
PH value sensor: the collected data is the current plant growth PH value, which represents the PH value of the substance;
biological height sensor: the data collected are numerical values of plant height, representing the vertical growth of plants per unit time;
illumination sensor: the collected data is a numerical value of illumination intensity, representing the intensity level of visible light in the environment;
temperature and humidity sensor: the data collected are the temperature and humidity values of the environment;
blade color sensor: the data collected are values of leaf color for assessing the health and nutritional status of the plant;
an image sensor: the data collected are images of plants, which are used to analyze and measure the morphology, number and color of the plants.
8. The plant growing PH control system of claim 4, wherein: the model training unit (202) trains the model by means of a decision tree algorithm through data partitioning, model training, model evaluation and model tuning.
9. The plant growing PH control system of claim 5, wherein: the model application unit (302) predicts the PH value of the real-time plant growth data by using the trained model, and specifically comprises the following steps:
sequentially comparing the characteristic value of the sample with the value of the partition characteristic corresponding to the node from the root node, moving the sample along the corresponding child node according to different values, and recording the moved node data;
continuing to repeat the previous step, and moving the sample to the next child node according to the dividing characteristics and the value of the current node until the leaf node is reached, wherein the leaf node corresponds to the value of the prescribed PH value;
and finally, outputting the predicted PH value and recording the mobile node data, wherein the node data is the data affecting the PH value.
10. The plant growing PH control system of claim 6, wherein: the prediction result sorting unit (403) performs various chart displays of a line graph, a thermodynamic diagram and a scatter diagram on data by using a data visualization library matplotlib in Python.
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