CN117172505A - Crop planting state monitoring method and system based on Internet of things - Google Patents
Crop planting state monitoring method and system based on Internet of things Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 35
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- 238000012549 training Methods 0.000 claims abstract description 8
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- 241000607479 Yersinia pestis Species 0.000 claims description 21
- 238000005259 measurement Methods 0.000 claims description 15
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- ZLMJMSJWJFRBEC-UHFFFAOYSA-N Potassium Chemical compound [K] ZLMJMSJWJFRBEC-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention relates to the technical field of crop management, and discloses a crop planting state monitoring method and system based on the Internet of things, wherein the method comprises the following steps: dividing a target crop area of the current planting round into a test area, a control area and a planting area; acquiring and recording growth condition information and crop growth information of a target crop area in real time; unifying the growth condition information of the test area, the control area and the planting area; after adjusting the growth condition information of the test area according to a preset strategy, evaluating the crop growth information of the test area through a pre-training model; executing a corresponding management strategy according to the evaluation result, and adjusting the growth condition information of crops in the next round of the target crop area; factors capable of improving the quality or the yield of the planted plants are accurately and rapidly found in each round of planting round period and then applied to the planting in the planting area of the next round, so that the crop yield and quality can be effectively improved and the agricultural resource utilization can be optimized.
Description
Technical Field
The invention relates to the technical field of crop management, in particular to a crop planting state monitoring method and system based on the Internet of things.
Background
The internet of things is an important component of a new generation of information technology and is also an important development stage of an 'informatization' age. As the name implies, the internet of things is the internet to which things are connected. This has two layers of meaning: firstly, the core and the foundation of the Internet of things are still the Internet, and the Internet is an extended and expanded network based on the Internet; secondly, the user side extends and expands to any article to article, and information exchange and communication are carried out, namely, the article information is carried out. The internet of things is widely applied to the fusion of networks through communication sensing technologies such as intelligent sensing, recognition technologies, pervasive computing and the like, and is also called as the third wave of development of world information industry after a computer and the Internet.
Along with the continuous development of technology, the internet of things technology is increasingly widely applied to agricultural production. For example, the Internet of things monitoring technology applied to the greenhouse can monitor and early warn the temperature and the humidity of air in the greenhouse in real time, and ensure the normal growth of plants planted in the greenhouse; however, the conventional field crop planting method still has the problems of lack of intelligence, low efficiency, incapability of accurately grasping the growth state of crops and the like because of various planting surface light and varieties. Therefore, developing a crop planting state monitoring system and method based on the Internet of things has important significance in improving crop yield and quality, optimizing agricultural resource utilization and the like.
Disclosure of Invention
The invention aims to provide a crop planting state monitoring method and system based on the Internet of things, which solve the following technical problems:
how to provide a crop planting state monitoring method and system capable of improving crop yield and quality and optimizing agricultural resource utilization.
The aim of the invention can be achieved by the following technical scheme:
a crop planting state monitoring method based on the Internet of things comprises the following steps:
dividing a target crop area of the current planting round into a test area and a control area and a planting area;
acquiring and recording growth condition information and crop growth information of the target crop area in real time;
unifying the growth condition information of the test area, the control area and the planting area;
after adjusting the growth condition information of the test area according to a preset strategy, evaluating the crop growth information of the test area through a pre-training model;
executing a corresponding management strategy according to the evaluation result, and adjusting the growth condition information of crops in the next round of the target crop area;
wherein the growth condition information includes soil information and environmental information; the soil information comprises soil humidity and soil nutrient data, and the environment information comprises air temperature data, rainfall data and illumination data; the crop growth information includes planting density, height information, trait information, and pest information.
Through the technical scheme, the target crop area is divided, the growth condition information and the crop growth information of the crops in the control group and the experimental group are acquired and analyzed, factors which can improve the quality or the yield of the plant can be accurately and rapidly found in each round of planting round period, and then the factors are applied to the planting in the planting area of the next round, so that the crop yield and the crop quality can be effectively improved and the agricultural resource utilization can be optimized repeatedly.
As a further scheme of the invention: the preset strategy comprises the following steps:
selecting an unadjusted parameter in the soil information as a parameter to be adjusted;
and adjusting the parameter to be adjusted to a target value.
As a further scheme of the invention: the method for acquiring the planting density and the height information comprises the following steps:
selecting a rotation center point in the target crop area, and putting a measurer in the rotation center point;
when the laser ranging unit of the measurer is at a first height, the laser ranging unit rotates and projects ranging laser by taking the rotation center point as the center to acquire a circle of ranging curve corresponding to the first height;
lifting the first height to a second height, and rotating and projecting ranging laser at the second height by a laser ranging unit of the measurer by taking the rotation center point as the center to obtain a circle of ranging curve corresponding to the second height;
repeating the steps until the one-circle ranging curve is judged to be abnormal;
merging the normal one-circle ranging curves into the same coordinate system to obtain a comprehensive measuring and calculating picture; wherein the horizontal axis of the coordinate system is a rotation angle, and the vertical axis is a height;
inputting the comprehensive measurement pictures into a trained measurement model to obtain the corresponding planting density and height information.
As a further scheme of the invention: the method for judging the abnormality of the one-circle ranging curve comprises the following steps:
counting the duty ratio A exceeding a preset ranging value in the one-circle ranging curve;
if A is greater than or equal to A sth Judging that the device is abnormal; wherein A is sth Is a preset percentage threshold.
As a further scheme of the invention: the method for acquiring the preset ranging value comprises the following steps:
obtaining initial calculated planting density according to the comprehensive measurement pictures combined by the first N one-week ranging curves;
determining the average distance C between adjacent plants according to the planting density;
c/2 is taken as the preset ranging value.
As a further scheme of the invention: the method for acquiring the character information and the insect pest information comprises the following steps:
driving the unmanned aerial vehicle to move at a height position higher than the height information specified amplitude, and acquiring a nodding sampling diagram of a specified area;
performing character stage identification and pest identification according to the nodding sampling graph;
and carrying out pest evaluation and early warning according to the pest information obtained by recognition.
As a further scheme of the invention: and carrying out key pest monitoring on the areas which are identified in the character stage and do not meet the standard requirements.
As a further scheme of the invention: the crop planting state monitoring system based on the Internet of things divides a target crop area of a current planting round into a test area, a control area and a planting area;
the sampling module is used for acquiring and recording the growth condition information and the crop growth information of the target crop area in real time;
the initial processing unit is used for unifying the growth condition information of the test area, the control area and the planting area;
the adjusting module is used for evaluating the crop growth information of the test area through a pre-training model after adjusting the growth condition information of the test area according to a preset strategy;
the management module is used for executing a corresponding management strategy according to the evaluation result and adjusting the growth condition information of crops in the next round of the target crop area;
wherein the growth condition information includes soil information and environmental information; the soil information comprises soil humidity and soil nutrient data, and the environment information comprises air temperature data, rainfall data and illumination data; the crop growth information includes planting density, height information, trait information, and pest information.
The invention has the beneficial effects that: according to the invention, the target crop area is divided, the growth condition information and the crop growth information of the control group and the experimental group are acquired and analyzed, factors capable of improving the quality or the yield of the plant can be accurately and rapidly found in each round of planting round period, and then the factors are applied to the next round of planting in the planting area, so that the crop yield and the crop quality can be effectively improved and the agricultural resource utilization can be optimized.
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The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a basic flow diagram of a crop planting state monitoring method based on the internet of things in the invention.
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, the invention provides a crop planting state monitoring method based on the internet of things, which comprises the following steps:
dividing a target crop area of the current planting round into a test area and a control area and a planting area;
acquiring and recording growth condition information and crop growth information of the target crop area in real time;
unifying the growth condition information of the test area, the control area and the planting area;
after adjusting the growth condition information of the test area according to a preset strategy, evaluating the crop growth information of the test area through a pre-training model;
executing a corresponding management strategy according to the evaluation result, and adjusting the growth condition information of crops in the next round of the target crop area;
wherein the growth condition information includes soil information and environmental information; the soil information comprises soil humidity and soil nutrient data, and the environment information comprises air temperature data, rainfall data and illumination data; the crop growth information includes planting density, height information, trait information, and pest information.
Through the technical scheme, the target crop area is divided, the growth condition information and the crop growth information of the crops in the control group and the experimental group are acquired and analyzed, factors which can improve the quality or the yield of the plant can be accurately and rapidly found in each round of planting round period, and then the factors are applied to the planting in the planting area of the next round, so that the crop yield and the crop quality can be effectively improved and the agricultural resource utilization can be optimized repeatedly.
In the embodiment of the invention, a system used by the method can adopt a distributed architecture, and sensor nodes can be arranged at different positions of a farmland so as to comprehensively monitor the growth condition of crops; the wireless communication module can adopt the internet of things communication technologies such as Zigbee, NB-IoT, 4G/5G and the like to realize stable data transmission; the cloud computing platform can be adopted for data processing, so that the cloud computing platform has strong data processing capability, and real-time processing, analysis and storage of data can be realized. The user interface can display data in Web, APP and other modes, so that the user can conveniently inquire and adjust the planting strategy.
In the embodiment of the invention, the soil humidity can be measured by adopting a resistance method, and the soil humidity is measured by utilizing a resistance soil humidity measuring device according to the relation between the conductivity of the soil solution and the soil moisture content. Soil nutrient data may then be detected using nutrient sensors, which typically include an electronic component and a receiver, which are typically used to measure the elemental content of the soil. Once the sensor is installed, the soil tester may be run for testing; the soil detector converts the electrode signals in the sensor into digital signals and sends the digital signals to a computer for analysis.
Generally speaking, the total nitrogen, total phosphorus and total potassium content in the soil need to be obtained to determine the soil nutrients, and in the embodiment of the invention, the preset strategy comprises:
selecting an unadjusted parameter in the soil information as a parameter to be adjusted;
and adjusting the parameter to be adjusted to a target value. For example, the total phosphorus of the soil in the test zone can be adjusted to be improved by 10 percent, and the subsequent distinguishing change of crops in the test zone and the control zone can be observed.
As a further scheme of the invention: the method for acquiring the planting density and the height information comprises the following steps:
selecting a rotation center point in the target crop area, and putting a measurer in the rotation center point;
when the laser ranging unit of the measurer is at a first height, the laser ranging unit rotates and projects ranging laser by taking the rotation center point as the center to acquire a circle of ranging curve corresponding to the first height;
lifting the first height to a second height, and rotating and projecting ranging laser at the second height by a laser ranging unit of the measurer by taking the rotation center point as the center to obtain a circle of ranging curve corresponding to the second height;
repeating the steps until the one-circle ranging curve is judged to be abnormal;
merging the normal one-circle ranging curves into the same coordinate system to obtain a comprehensive measuring and calculating picture; wherein the horizontal axis of the coordinate system is a rotation angle, and the vertical axis is a height;
inputting the comprehensive measurement pictures into a trained measurement model to obtain the corresponding planting density and height information.
In the above method, the measurer used may be of various forms, and in the present embodiment, the measurer includes a supporting rotation rod and a laser ranging unit and a driving unit; the driving unit is used for driving the laser ranging unit to move or rotate along the supporting rotary rod, controlling laser projection of the laser ranging unit and receiving or sending of data, and the supporting rotary rod is vertically inserted into the center point of rotation.
When the arrangement of the measurer is finished, the driving unit can drive the laser ranging unit to rotate at a first height, meanwhile, the projection and the reception of the ranging laser are carried out, and a circle of ranging curve can be obtained after one circle of rotation; since the distance measurement result is known, the rotation angle is known, the rotation center point is known, and the crop variety is also known, the thickness condition and the interval condition of the crops can be reflected by the change condition of the distance measurement curve from a circle of distance measurement curve. Therefore, the invention adopts AI technology to quickly identify and confirm the planting density and height information of crops, and does not need to manually go to the site for measurement and calculation every day, thereby greatly reducing the use of manpower and material resources.
In the invention, the pre-training model is designed and trained based on CNN, which refers to a convolutional neural network (Convolutional Neural Network) commonly used for processing data such as images, voice and the like. CNN operates on the input data by sliding a filter (or convolution kernel) in the input data, thereby extracting features of the data. CNNs are typically composed of multiple convolutional layers, pooled layers, and fully-connected layers. The convolution layer is used for carrying out convolution operation on input data so as to extract the characteristics of the data; the pooling layer is used for downsampling the data so as to reduce the dimension of the data; the full connection layer is used for mapping the output results of the previous layers into an output space so as to obtain a final classification or regression result.
As a further scheme of the invention: the method for judging the abnormality of the one-circle ranging curve comprises the following steps:
counting the duty ratio A exceeding a preset ranging value in the one-circle ranging curve;
if A is greater than or equal to A sth Judging that the device is abnormal; wherein A is sth Is a preset percentage threshold.
Because in the embodiment of the invention, the height information is determined by the height corresponding to the last normal one-circle ranging curve; therefore, the method is to consider the sporadic individual difference between crops, the phenomenon of uneven height may occur, so that the height information can be more accurately determined.
As a further scheme of the invention: the method for acquiring the preset ranging value comprises the following steps:
obtaining initial calculated planting density according to the comprehensive measurement pictures combined by the first N one-week ranging curves;
determining the average distance C between adjacent plants according to the planting density;
c/2 is taken as the preset ranging value. In the invention, N can be a downward integer of M/3, M is the total number of the one-week ranging curves; because, in the first several circle ranging curves, the height of the rotating surface of the laser ranging unit is closer to the ground, the probability of the laser scanning the bottom support rod of the crops is higher, the interference of plant leaves or other parts on the measuring result is reduced, and more accurate planting density information can be obtained; therefore, the influence of the planting density on the accuracy of the height information measurement and calculation can be reduced, and the accuracy of the height information measurement and calculation is ensured.
As a further scheme of the invention: the method for acquiring the character information and the insect pest information comprises the following steps:
driving the unmanned aerial vehicle to move at a height position higher than the height information specified amplitude, and acquiring a nodding sampling diagram of a specified area;
performing character stage identification and pest identification according to the nodding sampling graph;
and carrying out pest evaluation and early warning according to the pest information obtained by recognition.
As a further scheme of the invention: and carrying out key pest monitoring on the areas which are identified in the character stage and do not meet the standard requirements.
As a further scheme of the invention: the crop planting state monitoring system based on the Internet of things divides a target crop area of a current planting round into a test area, a control area and a planting area;
the sampling module is used for acquiring and recording the growth condition information and the crop growth information of the target crop area in real time;
the initial processing unit is used for unifying the growth condition information of the test area, the control area and the planting area;
the adjusting module is used for evaluating the crop growth information of the test area through a pre-training model after adjusting the growth condition information of the test area according to a preset strategy;
the management module is used for executing a corresponding management strategy according to the evaluation result and adjusting the growth condition information of crops in the next round of the target crop area;
wherein the growth condition information includes soil information and environmental information; the soil information comprises soil humidity and soil nutrient data, and the environment information comprises air temperature data, rainfall data and illumination data; the crop growth information includes planting density, height information, trait information, and pest information.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (8)
1. The crop planting state monitoring method based on the Internet of things is characterized by comprising the following steps of:
dividing a target crop area of the current planting round into a test area and a control area and a planting area;
acquiring and recording growth condition information and crop growth information of the target crop area in real time;
unifying the growth condition information of the test area, the control area and the planting area;
after adjusting the growth condition information of the test area according to a preset strategy, evaluating the crop growth information of the test area through a pre-training model;
executing a corresponding management strategy according to the evaluation result, and adjusting the growth condition information of crops in the next round of the target crop area;
wherein the growth condition information includes soil information and environmental information; the soil information comprises soil humidity and soil nutrient data, and the environment information comprises air temperature data, rainfall data and illumination data; the crop growth information includes planting density, height information, trait information, and pest information.
2. The method for monitoring crop planting conditions based on the internet of things according to claim 1, wherein the preset strategy comprises:
selecting an unadjusted parameter in the soil information as a parameter to be adjusted;
and adjusting the parameter to be adjusted to a target value.
3. The method for monitoring the planting state of crops based on the internet of things according to claim 1, wherein the method for acquiring the planting density and the height information comprises the following steps:
selecting a rotation center point in the target crop area, and putting a measurer in the rotation center point;
when the laser ranging unit of the measurer is at a first height, the laser ranging unit rotates and projects ranging laser by taking the rotation center point as the center to acquire a circle of ranging curve corresponding to the first height;
lifting the first height to a second height, and rotating and projecting ranging laser at the second height by a laser ranging unit of the measurer by taking the rotation center point as the center to obtain a circle of ranging curve corresponding to the second height;
repeating the steps until the one-circle ranging curve is judged to be abnormal;
merging the normal one-circle ranging curves into the same coordinate system to obtain a comprehensive measuring and calculating picture; wherein the horizontal axis of the coordinate system is a rotation angle, and the vertical axis is a height;
inputting the comprehensive measurement pictures into a trained measurement model to obtain the corresponding planting density and height information.
4. The method for monitoring crop planting conditions based on the internet of things according to claim 3, wherein the method for judging that the one-week ranging curve is abnormal comprises:
counting the duty ratio A exceeding a preset ranging value in the one-circle ranging curve;
if A is greater than or equal to A sth Judging that the device is abnormal; wherein A is sth Is a preset percentage threshold.
5. The method for monitoring the crop planting state based on the internet of things according to claim 4, wherein the method for acquiring the preset ranging value comprises the following steps:
obtaining initial calculated planting density according to the comprehensive measurement pictures combined by the first N one-week ranging curves;
determining the average distance C between adjacent plants according to the planting density;
c/2 is taken as the preset ranging value.
6. The method for monitoring crop planting conditions based on the internet of things according to claim 1, wherein the method for acquiring the trait information and the pest information comprises:
driving the unmanned aerial vehicle to move at a height position higher than the height information specified amplitude, and acquiring a nodding sampling diagram of a specified area;
performing character stage identification and pest identification according to the nodding sampling graph;
and carrying out pest evaluation and early warning according to the pest information obtained by recognition.
7. The method for monitoring crop planting conditions based on the internet of things according to claim 6, wherein important pest monitoring is performed on areas which are not in compliance with standard requirements in the character stage identification.
8. The crop planting state monitoring system based on the Internet of things is characterized in that a target crop area of a current planting round is divided into a test area, a control area and a planting area;
the sampling module is used for acquiring and recording the growth condition information and the crop growth information of the target crop area in real time;
the initial processing unit is used for unifying the growth condition information of the test area, the control area and the planting area;
the adjusting module is used for evaluating the crop growth information of the test area through a pre-training model after adjusting the growth condition information of the test area according to a preset strategy;
the management module is used for executing a corresponding management strategy according to the evaluation result and adjusting the growth condition information of crops in the next round of the target crop area;
wherein the growth condition information includes soil information and environmental information; the soil information comprises soil humidity and soil nutrient data, and the environment information comprises air temperature data, rainfall data and illumination data; the crop growth information includes planting density, height information, trait information, and pest information.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102102988A (en) * | 2009-12-22 | 2011-06-22 | 中国农业科学院农业环境与可持续发展研究所 | Method, system and device for measuring crop yield information in real time |
| CN103616014A (en) * | 2013-11-29 | 2014-03-05 | 浙江农林大学 | Method for measuring and computing forest stock volume parameter |
| CN104112305A (en) * | 2013-10-17 | 2014-10-22 | 北京竞业达数码科技有限公司 | Passenger flow monitoring method and apparatus based on dual laser ranging |
| CN105136107A (en) * | 2015-07-07 | 2015-12-09 | 旷天水 | Measurement method and measurement system for target position, tree height and diameter |
| CN107392104A (en) * | 2017-06-23 | 2017-11-24 | 深圳市盛路物联通讯技术有限公司 | A kind of crop growth management method and system based on Internet of Things |
| CN107589729A (en) * | 2017-09-12 | 2018-01-16 | 合肥师范学院 | A kind of wisdom agricultural management system and method based on Internet of Things and expert system |
| CN109029588A (en) * | 2018-09-11 | 2018-12-18 | 南京都宁大数据科技有限公司 | A kind of Grain Growth Situation prediction technique based on climatic effect |
| CN112639405A (en) * | 2020-05-07 | 2021-04-09 | 深圳市大疆创新科技有限公司 | State information determination method, device, system, movable platform and storage medium |
| US20210364487A1 (en) * | 2018-01-03 | 2021-11-25 | Jiangsu University | Suspension slide rail platform-based greenhouse information automatic monitoring method |
| CN114004458A (en) * | 2021-10-11 | 2022-02-01 | 青岛硕盈科技有限公司 | Polymorphic potential perception fusion plant growth management system |
| CN116602178A (en) * | 2023-06-21 | 2023-08-18 | 南昌凡叶科技有限公司 | Construction method of paddy field fertilization field district test |
-
2023
- 2023-09-27 CN CN202311263770.0A patent/CN117172505B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102102988A (en) * | 2009-12-22 | 2011-06-22 | 中国农业科学院农业环境与可持续发展研究所 | Method, system and device for measuring crop yield information in real time |
| CN104112305A (en) * | 2013-10-17 | 2014-10-22 | 北京竞业达数码科技有限公司 | Passenger flow monitoring method and apparatus based on dual laser ranging |
| CN103616014A (en) * | 2013-11-29 | 2014-03-05 | 浙江农林大学 | Method for measuring and computing forest stock volume parameter |
| CN105136107A (en) * | 2015-07-07 | 2015-12-09 | 旷天水 | Measurement method and measurement system for target position, tree height and diameter |
| CN107392104A (en) * | 2017-06-23 | 2017-11-24 | 深圳市盛路物联通讯技术有限公司 | A kind of crop growth management method and system based on Internet of Things |
| CN107589729A (en) * | 2017-09-12 | 2018-01-16 | 合肥师范学院 | A kind of wisdom agricultural management system and method based on Internet of Things and expert system |
| US20210364487A1 (en) * | 2018-01-03 | 2021-11-25 | Jiangsu University | Suspension slide rail platform-based greenhouse information automatic monitoring method |
| CN109029588A (en) * | 2018-09-11 | 2018-12-18 | 南京都宁大数据科技有限公司 | A kind of Grain Growth Situation prediction technique based on climatic effect |
| CN112639405A (en) * | 2020-05-07 | 2021-04-09 | 深圳市大疆创新科技有限公司 | State information determination method, device, system, movable platform and storage medium |
| CN114004458A (en) * | 2021-10-11 | 2022-02-01 | 青岛硕盈科技有限公司 | Polymorphic potential perception fusion plant growth management system |
| CN116602178A (en) * | 2023-06-21 | 2023-08-18 | 南昌凡叶科技有限公司 | Construction method of paddy field fertilization field district test |
Non-Patent Citations (1)
| Title |
|---|
| 王显梅;: "基于农作物全生命周期的信息系统管控研究", 农机化研究, no. 12, 18 May 2020 (2020-05-18) * |
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