CN116818002A - Intelligent agricultural planting monitoring system based on big data - Google Patents
Intelligent agricultural planting monitoring system based on big data Download PDFInfo
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- CN116818002A CN116818002A CN202310154883.0A CN202310154883A CN116818002A CN 116818002 A CN116818002 A CN 116818002A CN 202310154883 A CN202310154883 A CN 202310154883A CN 116818002 A CN116818002 A CN 116818002A
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 47
- 239000002689 soil Substances 0.000 claims abstract description 65
- 238000012545 processing Methods 0.000 claims abstract description 62
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims abstract description 26
- 238000000034 method Methods 0.000 claims abstract description 16
- 229910002092 carbon dioxide Inorganic materials 0.000 claims abstract description 13
- 239000001569 carbon dioxide Substances 0.000 claims abstract description 13
- 230000008569 process Effects 0.000 claims abstract description 13
- 230000000694 effects Effects 0.000 claims abstract description 8
- 239000013598 vector Substances 0.000 claims description 12
- 235000011389 fruit/vegetable juice Nutrition 0.000 claims description 9
- 238000007689 inspection Methods 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000004891 communication Methods 0.000 claims description 6
- 238000009826 distribution Methods 0.000 claims description 6
- 230000009466 transformation Effects 0.000 claims description 6
- 238000005516 engineering process Methods 0.000 claims description 5
- 238000013507 mapping Methods 0.000 claims description 3
- 238000005457 optimization Methods 0.000 claims description 3
- 230000007170 pathology Effects 0.000 claims description 3
- 238000005086 pumping Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 201000010099 disease Diseases 0.000 abstract description 5
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 abstract description 5
- 241000607479 Yersinia pestis Species 0.000 abstract description 4
- 241000238631 Hexapoda Species 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 230000002411 adverse Effects 0.000 abstract description 2
- 230000006870 function Effects 0.000 description 6
- 238000012271 agricultural production Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 3
- 238000007405 data analysis Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000007418 data mining Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
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Abstract
The application discloses a big data-based intelligent agricultural planting monitoring system, which relates to the technical field of agricultural planting monitoring and provides a scheme that a gas information acquisition unit, a soil information acquisition unit, a crop information acquisition unit and a big data monitoring unit are adopted; the gas information acquisition unit comprises a carbon dioxide acquisition module, an air temperature acquisition module and an air humidity acquisition module. According to the application, the intrinsic and extrinsic growth influencing factors of the plants are collected from three aspects of gas information collection, soil information collection and crop information collection, the collected factor data are transmitted to the central data processing module to be matched with big data for processing, and the picture image data and the text data are compared with the data in the big database, so that the effect of monitoring beneficial and adverse factors in the growth process of the crops is realized, and the monitoring effect on the crops can be used for timely acquiring whether the crops are damaged by diseases and insect pests and the extent of the diseases.
Description
Technical Field
The application relates to the technical field of agricultural planting monitoring, in particular to an intelligent agricultural planting monitoring system based on big data.
Background
The agricultural planting monitoring system is generally understood to be that by deploying equipment such as a camera, a sensor, a collector and the like on an agricultural production site, constructing an Internet of things monitoring network, and utilizing a modern information technology and relying on a computer or mobile phone terminal, the real-time monitoring of the climate environment, soil condition, crop growth and pest situation on the agricultural production site is realized; and according to preset rules, remote automatic control is carried out on various agricultural facility equipment on site, so that mass data acquisition and accurate control execution of agricultural production links are realized.
In the new-era agricultural development, the precise agriculture featuring high efficiency and concentration has become an important production form of modern agriculture in developed countries, and the artificial intelligent technology innovation such as machine learning, deep learning and the like based on big data and priori knowledge continuously improves the data analysis processing capacity, the data mining capacity and the auxiliary decision making capacity, and the core is that the trial-and-error cost in the modern agriculture development process is reduced through the application of the big data, the decision making accuracy and timeliness are improved, the precise agriculture not only brings high yield, but also improves the production efficiency. On the premise of the same yield, the investment is reduced; on the premise of reducing investment, the yield is improved. Only inputs and outputs are proportional to one another to achieve the benefits.
From the aspect of agricultural development, the combination of big data and the agricultural planting monitoring system is a necessary trend, but the existing agricultural planting monitoring system can only realize the internet of things, namely, the crop information obtained by the monitoring system can only be collected on the internet of things terminal and cannot be connected with a big cloud database, so that the collected crop information cannot be subjected to data analysis through the cloud, for example, when the crop possibly has the conditions of plant diseases and insect pests, loss of soil nutrients, unsuitable growth environment and the like, the effect of crop growth can be effectively avoided through remote analysis of the cloud, and therefore, a smart agricultural planting monitoring system based on the big data is provided by a person skilled in the art.
Disclosure of Invention
Aiming at the defects, the technical problem to be solved by the application is to provide an intelligent agricultural planting monitoring system based on big data, which comprises the following components: the system comprises a gas information acquisition unit, a soil information acquisition unit, a crop information acquisition unit and a big data monitoring unit;
the gas information acquisition unit comprises a carbon dioxide acquisition module, an air temperature acquisition module and an air humidity acquisition module;
the soil information acquisition unit comprises a soil temperature acquisition module, a soil humidity acquisition module, a soil acid-base number acquisition module and a soil conductivity acquisition module;
the crop information acquisition unit comprises an image acquisition module, an odor acquisition module and a crop juice inspection acquisition module;
the big data monitoring unit comprises a big plant database, a communication module, a central data processing module and a data display module.
In the above technical solution of the intelligent agricultural planting monitoring system based on big data, preferably, each module of the gas information collecting unit is specifically:
the carbon dioxide acquisition module is connected with the central data processing module, acquires by using carbon dioxide capture equipment and is used for periodically and quantitatively collecting the carbon dioxide content in the crop growth environment;
the air temperature acquisition module is connected with the central data processing module and acquires the growth air temperature of crops by utilizing an air temperature sensor;
and the air humidity acquisition module is connected with the central data processing module and acquires the air humidity of the crop growth through an air humidity sensor.
In the above technical solution of the intelligent agricultural planting monitoring system based on big data, preferably, each module of the soil information collecting unit specifically includes:
the soil temperature acquisition module is connected with the central data processing module and acquires the temperature of the crop growth soil through the soil temperature sensor;
the soil humidity acquisition module is connected with the central data processing module and acquires humidity data of crop growth soil through a soil humidity sensor;
the soil pH value acquisition module is connected with the central data processing module and acquires the PH value data of the crop growth soil through a soil pH value adding and measuring technology;
and the soil conductivity acquisition module is connected with the central data processing module and acquires conductivity data of crop growth soil through a soil salinity rapid detector.
In the above technical solution of the intelligent agricultural planting monitoring system based on big data, preferably, each module of the crop information collecting unit specifically includes:
the image acquisition module is connected with the central data processing module and is used for shooting a plurality of groups of crop picture data through the industrial camera;
the odor acquisition module is connected with the central data processing module and is used for detecting harmful gas components released by crops by pumping the gas released by the crops into the reaction air chamber;
the crop juice inspection and collection module is connected with the central data processing module, and intrinsic pathology data of crops are obtained by squeezing juice inspection of sample crops.
In the above technical solution of the intelligent agricultural planting monitoring system based on big data, preferably, each module in the big data monitoring unit specifically includes:
the plant big database is connected with the cloud big database and used for providing comparison data of the central data processing module;
the communication module is connected with the central data processing module through a network and is used for communicating the large database with the central data processing module;
the central data processing module is connected with the gas information acquisition unit, the soil information acquisition unit and the crop information acquisition unit through the Internet of things and the local area network and used for controlling each unit to complete monitoring work on crops;
the data display module is connected with the central data control module and used for displaying the processed crop monitoring information.
In the technical scheme of the intelligent agricultural planting monitoring system based on big data, preferably, the information collected in the gas information collecting unit, the soil information collecting unit and the crop information collecting unit is divided into picture image data and text data;
the process of processing the picture image data by the central data processing module is specifically as follows:
s1: randomly taking N images from the image data of the picture to form a data set, constructing a positive example for any image in the data set, forming two image enhancement views, and carrying out nonlinear transformation on the enhancement images through upper and lower branches respectively;
s2: through a feature encoder, extracting a feature representation h corresponding to the enhanced image through CNN conversion i While the model structure in the image is represented by f θ (x) Representing and representing another enhanced image non-linearly transformed structural feature as g θ (. Cndot.) means that the feature is further denoted as h i Mapping to a vector Z in another space i After the image is subjected to nonlinear transformation twice, for one image x in the dataset, the two extracted image enhancement views are respectively expressed as x i And x j At this time, a suitable loss function is defined, and a metric function is required to determine the distance between two vectors in the projection space, and the similarity between vectors of a certain image x:
s3: on the basis of step S2, the InfoNCE loss of a certain image x is expressed as:
wherein Z is i ,The representation vectors representing the two samples can be used for training the model through InfoNCE loss function guidance in the optimization process, so that the effect of comparing the sample picture with the big data picture data can be achieved.
In the above technical solution of the intelligent agricultural planting monitoring system based on big data, preferably, the process of processing the text data by the central data processing module is specifically as follows:
s1: calculating normal distribution of the text data, setting the text data as x, wherein mu is an average value of x, sigma is a standard deviation of x, and the normal distribution of x is calculated according to the formula:
where the greater σ, the greater the degree to which x deviates from the mean μ, and the variance is the square of the standard deviation. The standard deviation and variance are interchangeable to a discrete extent.
Compared with the prior art, the intelligent agricultural planting monitoring system based on big data has the following beneficial effects:
according to the application, the intrinsic and extrinsic growth influencing factors of the plants are collected from three aspects of gas information collection, soil information collection and crop information collection, the collected factor data are transmitted to the central data processing module to be matched with big data for processing, and the picture image data and the text data are compared with the data in the big database, so that the effect of monitoring beneficial and adverse factors in the growth process of the crops is realized, and the monitoring effect on the crops can be used for timely acquiring whether the crops are damaged by diseases and insect pests and the extent of the diseases.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description will make brief description and illustrations of the drawings used in the description of the embodiments of the present application or the prior art. It is obvious that the drawings in the following description are only some embodiments of the present application and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of an intelligent agricultural planting monitoring system based on big data in the application.
Detailed Description
The technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the embodiments described below are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order to make the explanation and the description of the technical solution and the implementation of the present application clearer, several preferred embodiments for implementing the technical solution of the present application are described below.
It will be understood that when an element is referred to as being "fixed" or "disposed" on another element, it can be directly on the other element or be indirectly on the other element; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element.
In addition, the terms herein: the orientation or positional relationship indicated by "inner, outer", "front, rear", "left, right", "vertical, horizontal", "top, bottom", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of description and to simplify the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as limiting the application.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature.
Example 1
The gas information acquisition unit comprises a carbon dioxide acquisition module, an air temperature acquisition module and an air humidity acquisition module.
The carbon dioxide acquisition module is connected with the central data processing module, acquires by using carbon dioxide capture equipment and is used for periodically and quantitatively collecting the carbon dioxide content in the crop growth environment; the air temperature acquisition module is connected with the central data processing module and acquires the growth air temperature of crops by utilizing an air temperature sensor; and the air humidity acquisition module is connected with the central data processing module and acquires the air humidity of the crop growth through an air humidity sensor.
The soil information acquisition unit comprises a soil temperature acquisition module, a soil humidity acquisition module, a soil pH value acquisition module and a soil conductivity acquisition module.
The soil temperature acquisition module is connected with the central data processing module and acquires the temperature of the crop growth soil through the soil temperature sensor; the soil humidity acquisition module is connected with the central data processing module and acquires humidity data of crop growth soil through a soil humidity sensor; the soil pH value acquisition module is connected with the central data processing module and acquires the PH value data of the crop growth soil through a soil pH value adding and measuring technology; and the soil conductivity acquisition module is connected with the central data processing module and acquires conductivity data of crop growth soil through a soil salinity rapid detector.
The crop information acquisition unit comprises an image acquisition module, an odor acquisition module and a crop juice inspection acquisition module.
The image acquisition module is connected with the central data processing module and is used for shooting a plurality of groups of crop picture data through the industrial camera; the odor acquisition module is connected with the central data processing module and is used for detecting harmful gas components released by crops by pumping the gas released by the crops into the reaction air chamber; the crop juice inspection and collection module is connected with the central data processing module, and intrinsic pathology data of crops are obtained by squeezing juice inspection of sample crops.
The big data monitoring unit comprises a big plant database, a communication module, a central data processing module and a data display module.
The plant big database is connected with the cloud big database and used for providing comparison data of the central data processing module; the communication module is connected with the central data processing module through a network and is used for communicating the large database with the central data processing module; the central data processing module is connected with the gas information acquisition unit, the soil information acquisition unit and the crop information acquisition unit through the Internet of things and the local area network and used for controlling each unit to complete monitoring work on crops; the data display module is connected with the central data control module and used for displaying the processed crop monitoring information.
Example 2
The process of processing the picture image data by the central data processing module is specifically as follows:
s1: randomly taking N images from the image data of the picture to form a data set, constructing a positive example for any image in the data set, forming two image enhancement views, and carrying out nonlinear transformation on the enhancement images through upper and lower branches respectively;
s2: through a feature encoder, extracting a feature representation h corresponding to the enhanced image through CNN conversion i While the model structure in the image is represented by f θ (x) Representing and representing another enhanced image non-linearly transformed structural feature as g θ (. Cndot.) means that the feature is further denoted as h i Mapping to a vector Z in another space i After the image is subjected to nonlinear transformation twice, the image x is used for one image in the data setThe two extracted image enhancement views are denoted as x i And x j At this time, a suitable loss function is defined, and a metric function is required to determine the distance between two vectors in the projection space, and the similarity between vectors of a certain image x:
s3: on the basis of step S2, the InfoNCE loss of a certain image x is expressed as:
wherein Z is i ,The representation vectors representing the two samples can be used for training the model through InfoNCE loss function guidance in the optimization process, so that the effect of comparing the sample picture with the big data picture data can be achieved.
The text data processing flow by the central data processing module is specifically as follows:
s1: calculating normal distribution of the text data, setting the text data as x, wherein mu is an average value of x, sigma is a standard deviation of x, and the normal distribution of x is calculated according to the formula:
where the greater σ, the greater the degree to which x deviates from the mean μ, and the variance is the square of the standard deviation. The standard deviation and variance are interchangeable to a discrete extent.
Finally, it should be further noted that the structures, proportions, sizes, etc. shown in the drawings are merely for the purpose of understanding and reading the disclosure, and are not intended to limit the applicable scope of the present application, so that any structural modifications, proportional changes, or adjustments of sizes may not be technically significant, and all fall within the scope of the disclosure without affecting the efficacy and achievement of the present application.
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. Without further limitation, an element defined by the phrase "comprising one …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The present application is not limited to the above-mentioned preferred embodiments, and any person who can learn the structural changes made under the teaching of the present application can fall within the scope of the present application if the present application has the same or similar technical solutions.
Claims (7)
1. The intelligent agricultural planting monitoring system based on the big data is characterized by comprising a gas information acquisition unit, a soil information acquisition unit, a crop information acquisition unit and a big data monitoring unit;
the gas information acquisition unit comprises a carbon dioxide acquisition module, an air temperature acquisition module and an air humidity acquisition module;
the soil information acquisition unit comprises a soil temperature acquisition module, a soil humidity acquisition module, a soil acid-base number acquisition module and a soil conductivity acquisition module;
the crop information acquisition unit comprises an image acquisition module, an odor acquisition module and a crop juice inspection acquisition module;
the big data monitoring unit comprises a big plant database, a communication module, a central data processing module and a data display module.
2. The intelligent agricultural planting monitoring system based on big data according to claim 1, wherein each module of the gas information acquisition unit is specifically:
the carbon dioxide acquisition module is connected with the central data processing module, acquires by using carbon dioxide capture equipment and is used for periodically and quantitatively collecting the carbon dioxide content in the crop growth environment;
the air temperature acquisition module is connected with the central data processing module and acquires the growth air temperature of crops by utilizing an air temperature sensor;
and the air humidity acquisition module is connected with the central data processing module and acquires the air humidity of the crop growth through an air humidity sensor.
3. The intelligent agricultural planting monitoring system based on big data according to claim 1, wherein each module of the soil information acquisition unit specifically comprises:
the soil temperature acquisition module is connected with the central data processing module and acquires the temperature of the crop growth soil through the soil temperature sensor;
the soil humidity acquisition module is connected with the central data processing module and acquires humidity data of crop growth soil through a soil humidity sensor;
the soil pH value acquisition module is connected with the central data processing module and acquires the PH value data of the crop growth soil through a soil pH value adding and measuring technology;
and the soil conductivity acquisition module is connected with the central data processing module and acquires conductivity data of crop growth soil through a soil salinity rapid detector.
4. The intelligent agricultural planting monitoring system based on big data according to claim 1, wherein the modules of the crop information acquisition unit are as follows:
the image acquisition module is connected with the central data processing module and is used for shooting a plurality of groups of crop picture data through the industrial camera;
the odor acquisition module is connected with the central data processing module and is used for detecting harmful gas components released by crops by pumping the gas released by the crops into the reaction air chamber;
the crop juice inspection and collection module is connected with the central data processing module, and intrinsic pathology data of crops are obtained by squeezing juice inspection of sample crops.
5. The intelligent agricultural planting monitoring system based on big data according to claim 1, wherein each module in the big data monitoring unit specifically comprises:
the plant big database is connected with the cloud big database and used for providing comparison data of the central data processing module;
the communication module is connected with the central data processing module through a network and is used for communicating the large database with the central data processing module;
the central data processing module is connected with the gas information acquisition unit, the soil information acquisition unit and the crop information acquisition unit through the Internet of things and the local area network and used for controlling each unit to complete monitoring work on crops;
the data display module is connected with the central data control module and used for displaying the processed crop monitoring information.
6. The intelligent agricultural planting monitoring system based on big data according to claim 1, wherein the information collected in the gas information collection unit, the soil information collection unit and the crop information collection unit is divided into picture image data and text data;
the process of processing the picture image data by the central data processing module is specifically as follows:
s1: randomly taking N images from the image data of the picture to form a data set, constructing a positive example for any image in the data set, forming two image enhancement views, and carrying out nonlinear transformation on the enhancement images through upper and lower branches respectively;
s2: through a feature encoder, extracting a feature representation h corresponding to the enhanced image through CNN conversion i While the model structure in the image is represented by f θ (x) Representing and representing another enhanced image non-linearly transformed structural feature as g θ (. Cndot.) means that the feature is further denoted as h i Mapping to a vector Z in another space i After the image is subjected to nonlinear transformation twice, for one image x in the dataset, the two extracted image enhancement views are respectively expressed as x i And x j At this time, a suitable loss function is defined, and a metric function is required to determine the distance between two vectors in the projection space, and the similarity between vectors of a certain image x:
s3: on the basis of step S2, the InfoNCE loss of a certain image x is expressed as:
wherein Z is i ,The representation vectors representing the two samples can be used for training the model through InfoNCE loss function guidance in the optimization process, so that the effect of comparing the sample picture with the big data picture data can be achieved.
7. The intelligent agricultural planting monitoring system based on big data according to claim 6, wherein the flow of processing text data by the central data processing module is specifically as follows:
s1: calculating normal distribution of the text data, setting the text data as x, wherein mu is an average value of x, sigma is a standard deviation of x, and the normal distribution of x is calculated according to the formula:
where the greater σ, the greater the degree to which x deviates from the mean μ, and the variance is the square of the standard deviation. The standard deviation and variance are interchangeable to a discrete extent.
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