CN116758420A - Crop seedling condition grading remote sensing monitoring system - Google Patents
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Abstract
The invention discloses a crop seedling condition grading remote sensing monitoring system, and relates to the technical field of remote sensing monitoring; collecting environmental data and image data of farmlands through a monitoring module; and transmitting the environmental data and the image data to a data processing module; the data processing module receives environment data and image data; acquiring an image index according to the image data and the image detection model; calculating and acquiring an environmental index according to the environmental data; calculating and obtaining a grade index according to the image index and the environment index, and sending the grade index to a grading module; the grading module receives the grade index and acquires a seedling condition grade interval; judging a seedling condition grade interval to which the grade index belongs, and acquiring a corresponding seedling condition grade; the seedling condition of the crops is obtained in different areas and under different conditions, the seedling condition classification result has higher precision, strong universality and stability, and a large amount of manpower and material resources are not required to be consumed; has important significance for improving the production and management efficiency of crops and maintaining the grain and ecological safety.
Description
Technical Field
The invention belongs to the field of crops, relates to a remote sensing monitoring technology, and particularly relates to a crop seedling condition grading remote sensing monitoring system.
Background
The wheat is a main grain crop in China, the high yield and stable yield of the wheat are the key for guaranteeing the national grain safety, and the timely and accurate mastering of the winter wheat growth monitoring and growth vigor quantitative evaluation technology becomes an important scientific decision basis for agricultural condition scheduling in the agricultural sector and vast farmers, and is also important in promoting scientific management of agricultural production and ensuring yield increase and harvest of crops. According to the seedling condition and growth situation of winter wheat, the winter wheat is classified into different grades, namely one type of seedling, two types of seedling, three types of seedling and vigorous growth seedling during agronomic investigation. 1. The higher the proportion of the second class seedlings is, the more favorable the wheat to be high in yield and harvest.
At present, in actual production, agricultural personnel are required to select representative sampling points for mastering the condition classification of winter wheat seedlings, at least 3 sampling points are required in 0.1h, indexes such as sowing time, basic seedlings, leaf age, growth process, mu stem tillering number, individual plant stem tillering number, soil, climate and the like are investigated in the field, and the result is given by comprehensive consideration. If the county level, the city level, the provincial level and even the national conditions are to be mastered, the required manpower and material resources are not measurable. The results given by the method are greatly influenced by the subjective influence of the investigator, and different investigators in the same area exist, so that the phenomenon of inconsistent results is given. Moreover, the sampling mode of the set point often has deviation in the point area. Therefore, how to develop simple, efficient and accurate agriculture condition hierarchical monitoring, timely and accurately acquire the growth situation of winter wheat in a large area, and has important significance for the agricultural departments to master the growth situation information of wheat in all regions of the country and timely make scheduling and decision.
The current winter wheat seedling condition monitoring is mainly based on visual inspection, most of the winter wheat seedlings are reported by sampling at set points, the manpower and material resources required by the method are multiplied along with the increase of the monitoring area, and the deviation is easy to generate by taking the points and the surfaces. Moreover, the judgment indexes of the seedling condition are complicated, the related investigation factors are more, the judgment results are greatly subjectively influenced by investigation staff, different investigation staff exist, and the phenomenon of inconsistent results is given.
Therefore, a crop seedling condition grading remote sensing monitoring system is provided.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art. Therefore, the invention provides a crop seedling classification remote sensing monitoring system, which solves the problems that the current winter wheat seedling classification is mainly based on visual inspection and is mainly reported by sampling at points, the manpower and material resources required by the method are multiplied along with the increase of the monitoring area, and the deviation is easy to generate by the point area; moreover, the judgment indexes of the seedling condition are complicated, the related investigation factors are more, the judgment results are greatly subjectively influenced by investigation staff, and different investigation staff exist, so that the problem of inconsistent results is solved.
To achieve the above object, an embodiment according to a first aspect of the present invention provides a crop seedling condition hierarchical remote sensing monitoring system, which includes a monitoring module, a data processing module, and a hierarchical module;
the monitoring module is used for collecting environmental data and image data of farmlands; wherein the environmental data includes a temperature value and a humidity value;
and transmitting the environmental data and the image data to the data processing module;
the data processing module is used for receiving the environment data and the image data;
acquiring an image index according to the image data and the image detection model; wherein the image detection model is built based on an artificial intelligence model;
calculating and acquiring an environment index according to the environment data;
calculating an acquisition grade index according to the image index and the environment index, and sending the grade index to the grading module;
the grading module is used for receiving the grade index and acquiring a seedling condition grade interval; the seedling condition grade interval comprises a vigorous growth seedling interval, a first-class seedling interval, a second-class seedling interval and three-class seedling intervals;
judging a seedling condition grade interval to which the grade index belongs, and obtaining a corresponding seedling condition grade.
Preferably, the monitoring module comprises a remote sensing information acquisition device;
the remote sensing information acquisition device is used for acquiring environment data and image data.
Preferably, the monitoring module acquires environmental data and image data in a periodic acquisition mode;
the acquisition period of the environment data and the image data is marked as T, and the unit is h; wherein T is an integer greater than 0;
and marking the number of the acquisition period as N, wherein the value of N is 1,2,3 and … … N, and N is the total acquisition times of the monitoring module.
Preferably, the image index is obtained according to the image data and the image detection model, and the method comprises the following steps:
the data processing module receives the image data and marks the image data as Pn; the data processing module sets the moment of receiving the image data as a reference moment and acquires M pieces of image data before the reference moment;
sequencing the M pieces of image data according to the acquisition time to obtain an original image set;
and acquiring an image detection model from the data processing module, inputting the original image set into the image detection model, acquiring an image index, and marking the image index as TXn.
Preferably, M is an integer of 10 or more.
Preferably, the image detection model is built based on an artificial intelligence model, and comprises the following steps:
standard training data are acquired from a data processing module;
training the artificial intelligent model through standard training data, and marking the trained artificial intelligent model as an image detection model;
the standard training data comprises a plurality of groups of input image sets and corresponding image indexes, and the content attributes of the input image sets and the original image sets are consistent.
Preferably, calculating and acquiring an environmental index according to the environmental data comprises the following steps:
the data processing module receives the environmental data and marks the temperature value and the humidity value as Wn and Sn respectively;
the data processing module calculates and acquires an environment index according to the temperature value and the humidity value, and marks the environment index as HJn;
the calculation formula of the environment index is as follows:
Hn n ＝ln(αW n +βS n )
wherein, alpha and beta are correction coefficients of temperature value and humidity value respectively, and alpha and beta are real numbers larger than 0.
Preferably, calculating an acquisition grade index according to the image index and the environment index includes the steps of:
marking the ranking index as DJn;
the calculation formula of the grade index is as follows:
DJn＝a×TXn+b×HJn
wherein a and b are weighting coefficients of the image index and the environment index, respectively, and a+b=1, and a and b are both greater than 0.
Compared with the prior art, the invention has the beneficial effects that:
the invention collects the environmental data and image data of farmland through the monitoring module; and transmitting the environmental data and the image data to a data processing module; the data processing module receives environment data and image data; acquiring an image index according to the image data and the image detection model; calculating and acquiring an environmental index according to the environmental data; calculating and obtaining a grade index according to the image index and the environment index, and sending the grade index to a grading module; the grading module receives the grade index and acquires a seedling condition grade interval; judging a seedling condition grade interval to which the grade index belongs, and acquiring a corresponding seedling condition grade; the seedling condition of the crops is obtained in different areas and under different conditions, the seedling condition classification result has higher precision, strong universality and stability, and a large amount of manpower and material resources are not required to be consumed; has important significance for improving the production and management efficiency of crops and maintaining the grain and ecological safety.
Drawings
FIG. 1 is a schematic diagram of the present invention;
fig. 2 is a flow chart of the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments 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.
1-2, a crop seedling condition grading remote sensing monitoring system comprises a monitoring module, a data processing module and a grading module; the modules perform information interaction based on digital signals;
the monitoring module is used for collecting environmental data and image data of farmlands; wherein the environmental data includes a temperature value and a humidity value;
and transmitting the environmental data and the image data to the data processing module;
in this embodiment, the monitoring module includes a remote sensing information acquisition device;
the remote sensing information acquisition device is used for acquiring environment data and image data; it should be further noted that, the environmental data and the image data are collected synchronously;
specifically, the monitoring module acquires environmental data and image data in a periodic acquisition mode;
the acquisition period of the environment data and the image data is marked as T, and the unit is h; wherein T is an integer greater than 0.
The data processing module is used for receiving the image data and the environment data;
acquiring an image index according to the image data, and acquiring an environment index according to the environment data;
calculating an acquisition grade index according to the image index and the environment index, and sending the grade index to the grading module;
in this embodiment, the data processing module obtains an image index according to the image data, including the following steps:
the data processing module receives the image data and marks the image data as Pn; wherein N is the number of the acquisition period of the image data, the value of N is 1,2,3 and … … N, and N is the total acquisition times of the image data;
the data processing module sets the moment of receiving the image data as a reference moment and acquires M pieces of image data before the reference moment;
sequencing the M pieces of image data according to the acquisition time to obtain an original image set;
acquiring an image detection model from the data processing module, inputting the original image set into the image detection model, acquiring an image index, and marking the image index as TXn; wherein the image detection model is built based on an artificial intelligence model;
in this embodiment, M is an integer greater than or equal to 10, and after verification, the accurate image index can be obtained only when at least 10 pieces of image data are obtained; illustrating:
the acquisition period of the image data is 2h;
the moment when the data processing module receives the image data is 22 hours 0 minutes 0 seconds, namely, 22 hours 0 minutes 0 seconds is set as a reference moment;
acquiring 10 pieces of image data before a reference time, wherein the acquisition time of the first piece of image data is 4 hours 0 minutes 0 seconds, the acquisition time of the second piece of image data is 6 hours 0 minutes 0 seconds, and the tenth piece of image data is acquired until 22 hours 0 minutes 0 seconds;
sequencing the 10 pieces of image data according to the acquisition time to obtain an original image set;
in this embodiment, the image detection model is built based on an artificial intelligence model, and includes the following steps:
standard training data are acquired from a data processing module;
and training the artificial intelligent model through standard training data, and marking the trained artificial intelligent model as an image detection model.
In this embodiment, the standard training data includes a plurality of sets of input image sets and corresponding image indexes, and content attributes of the input image sets and original image sets are consistent; it will be appreciated that the input image set and the original image set each include a selected M image data, except for the content of the image data.
In this embodiment, the artificial intelligence model includes a model with strong nonlinear fitting capability such as a deep convolutional neural network model or an RBF neural network model.
In this embodiment, the data processing module obtains an environmental index according to the environmental data, including the following steps:
the data processing module receives the environmental data and marks the temperature value and the humidity value as Wn and Sn respectively; wherein N is the number of the acquisition period of the image data, the value of N is 1,2,3 and … … N, and N is the total acquisition times of the image data;
the data processing module calculates and acquires an environment index according to the temperature value and the humidity value, and marks the environment index as HJn;
the calculation formula of the environment index is as follows:
HJ n ＝In(αW n +βS n )
wherein, alpha and beta are correction coefficients of temperature value and humidity value respectively, and alpha and beta are real numbers larger than 0.
In this embodiment, the ranking index is labeled DJn;
the calculation formula of the grade index is as follows:
DJn＝a×TXn+b×HJn
wherein a and b are weighting coefficients of the image index and the environment index, respectively, and a+b=1, and a and b are both greater than 0.
The grading module is used for receiving the grade index and grading the seedling according to the grade index;
in this embodiment, the grading module performs grading of seedling conditions according to the grading index, and includes the following steps:
the grading module sets a seedling condition grade interval; the seedling condition grade interval comprises a vigorous growth seedling interval, a first-class seedling interval, a second-class seedling interval and three-class seedling intervals; it should be further noted that the seedling condition level interval is set by a person skilled in the art;
judging a seedling condition grade interval to which the grade index belongs, and acquiring a corresponding seedling condition grade; illustrating:
the interval of vigorous growth of seedlings is 0.9,1
The seedling interval is [0.6,0.9 ]
Class Miao Oujian [0.2,0.6 ]
Three kinds of seedling interval (0,0.2)
When the grade index is 0.93,0.93, the seedlings belong to a vigorous growth seedling region, namely the corresponding seedling condition grade is a vigorous growth seedling;
when the grade index is 0.7,0.7, the seedlings belong to a seedling interval, namely the corresponding seedling condition grade is a seedling;
when the grade index is 0.35,0.35 and belongs to the second-class seedling interval, namely the corresponding seedling condition grade is second-class seedlings;
when the grade index is 0.15,0.15, the seedlings belong to three types of seedling intervals, namely, the corresponding seedling condition grade is three types of seedlings.
In this embodiment, the monitoring module is in communication and/or electrical connection with the data processing module;
the data processing module is in communication and/or electrical connection with the grading module.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas which are obtained by acquiring a large amount of data and performing software simulation to obtain the closest actual situation, and preset parameters and preset thresholds in the formulas are set by a person skilled in the art according to the actual situation or are obtained by simulating a large amount of data.
The working principle of the invention is as follows:
the monitoring module acquires environment data and image data in a periodic acquisition mode; and transmitting the environmental data and the image data to a data processing module;
the data processing module receives image data; setting the moment of receiving the image data as a reference moment, and acquiring M pieces of image data before the reference moment; sequencing M pieces of image data according to the acquisition time to obtain an original image set; acquiring an image detection model from a data processing module, inputting an original image set into the image detection model, and acquiring an image index;
the data processing module receives the environmental data and calculates and acquires an environmental index according to the temperature value and the humidity value;
calculating and obtaining a grade index according to the image index and the environment index, and sending the grade index to a grading module;
the grading module receives the grade index and sets a seedling condition grade interval; judging a seedling condition grade interval to which the grade index belongs, and obtaining a corresponding seedling condition grade.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (8)
1. The crop seedling condition grading remote sensing monitoring system is characterized by comprising a monitoring module, a data processing module and a grading module;
the monitoring module is used for collecting environmental data and image data of farmlands; wherein the environmental data includes a temperature value and a humidity value;
and transmitting the environmental data and the image data to the data processing module;
the data processing module is used for receiving the environment data and the image data;
acquiring an image index according to the image data and the image detection model; wherein the image detection model is built based on an artificial intelligence model;
calculating and acquiring an environment index according to the environment data;
calculating an acquisition grade index according to the image index and the environment index, and sending the grade index to the grading module;
the grading module is used for receiving the grade index and acquiring a seedling condition grade interval; the seedling condition grade interval comprises a vigorous growth seedling interval, a first-class seedling interval, a second-class seedling interval and three-class seedling intervals;
judging a seedling condition grade interval to which the grade index belongs, and obtaining a corresponding seedling condition grade.
2. The system of claim 1, wherein the monitoring module comprises a remote sensing information acquisition device;
the remote sensing information acquisition device is used for acquiring environment data and image data.
3. The hierarchical remote sensing monitoring system for crop seedling according to claim 1, wherein the monitoring module acquires environmental data and image data by means of periodic acquisition;
the acquisition period of the environment data and the image data is marked as T, and the unit is h; wherein T is an integer greater than 0;
and marking the number of the acquisition period as N, wherein the value of N is 1,2,3 and … … N, and N is the total acquisition times of the monitoring module.
4. A crop seedling stage remote sensing monitoring system according to claim 3, characterized in that the acquisition of an image index from said image data and image detection model comprises the steps of:
the data processing module receives the image data and marks the image data as Pn; the data processing module sets the moment of receiving the image data as a reference moment and acquires M pieces of image data before the reference moment;
sequencing the M pieces of image data according to the acquisition time to obtain an original image set;
and acquiring an image detection model from the data processing module, inputting the original image set into the image detection model, acquiring an image index, and marking the image index as TXn.
5. The hierarchical remote sensing monitoring system for crop seedling conditions according to claim 4, wherein M is an integer greater than or equal to 10.
6. The hierarchical remote sensing monitoring system for crop seedling conditions according to claim 4, wherein said image detection model is built based on an artificial intelligence model, comprising the steps of:
standard training data are acquired from a data processing module;
training the artificial intelligent model through standard training data, and marking the trained artificial intelligent model as an image detection model;
the standard training data comprises a plurality of groups of input image sets and corresponding image indexes, and the content attributes of the input image sets and the original image sets are consistent.
7. The hierarchical remote sensing monitoring system for crop seedling conditions according to claim 4, wherein the computing of the acquired environmental index from the environmental data comprises the steps of:
the data processing module receives the environmental data and marks the temperature value and the humidity value as Wn and Sn respectively;
the data processing module calculates and acquires an environment index according to the temperature value and the humidity value, and marks the environment index as HJn;
the calculation formula of the environment index is as follows:
HJ n ＝ln(αW n +βS n )
wherein, alpha and beta are correction coefficients of temperature value and humidity value respectively, and alpha and beta are real numbers larger than 0.
8. The system of claim 7, wherein calculating an acquisition grade index from the image index and the environmental index comprises the steps of:
marking the ranking index as DJn;
the calculation formula of the grade index is as follows:
DJn＝a×TXn+b×HJn
wherein a and b are weighting coefficients of the image index and the environment index, respectively, and a+b=1, and a and b are both greater than 0.
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