CN111046777A - Insect density recognition system based on LoRa node - Google Patents
Insect density recognition system based on LoRa node Download PDFInfo
- Publication number
- CN111046777A CN111046777A CN201911241985.6A CN201911241985A CN111046777A CN 111046777 A CN111046777 A CN 111046777A CN 201911241985 A CN201911241985 A CN 201911241985A CN 111046777 A CN111046777 A CN 111046777A
- Authority
- CN
- China
- Prior art keywords
- lora
- picture
- insect
- gray
- insect trap
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Medical Informatics (AREA)
- Multimedia (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Catching Or Destruction (AREA)
Abstract
The invention provides an insect density identification system based on LoRa nodes, which comprises a LoRa gateway, an insect trap and LoRa nodes arranged on the insect trap; a camera is arranged on the LoRa node and used for shooting a bottom picture of the insect trap, and the picture shot by the camera is an RGB picture; the LoRa node converts a bottom surface picture of the insect trap into a binary picture, and the density of the insects is calculated according to the proportion of pixel points occupied by the insects on the binary picture; the density size of the insect that the loRa node will calculate is sent to the loRa gateway to carry out remote monitoring to the loRa node. The invention has the beneficial effects that: utilize loRa node to carry out calculation processing to insect trap's bottom surface picture, obtain the density size of insect for loRa node resource obtains effective utilization, need not use extra server, can effectively reduce system's cost.
Description
Technical Field
The invention relates to an insect density identification system, in particular to an insect density identification system based on LoRa nodes.
Background
In a traditional internet of things system based on artificial intelligence, a machine learning algorithm is generally deployed in a central server in a centralized manner, data are collected through a node gateway, and finally the obtained data are processed in the central server in a centralized manner.
Aiming at the traditional machine learning Internet of things system framework, the computing resources of the central server are very precious, and the situations of the shortage of computing resources and the redundancy of the computing resources of the node gateway equipment exist at the same time, so that the improvement of the existing insect density identification system is needed.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a low-complexity insect density recognition system based on a LoRa node is provided.
In order to solve the technical problems, the invention adopts the technical scheme that: an insect density identification system based on LoRa nodes comprises a LoRa gateway, an insect trap and LoRa nodes arranged on the insect trap;
a camera is arranged on the LoRa node and used for shooting a bottom picture of the insect trap, and the picture shot by the camera is an RGB picture;
the LoRa node converts a bottom surface picture of the insect trap into a binary picture, and the density of the insects is calculated according to the proportion of pixel points occupied by the insects on the binary picture;
the density size of the insect that the loRa node will calculate is sent to the loRa gateway to carry out remote monitoring to the loRa node.
Further, the LoRa node converts a bottom surface picture of the insect trap into a binary picture, calculates the density of the insects according to the proportion of pixel points occupied by the insects on the binary picture,
converting the RGB picture into a gray picture according to a gray conversion formula;
calculating the gray average value of pixel points of the gray picture;
setting the gray value of the pixel point with the gray value larger than or equal to the average gray value as 256, and setting the gray value of the pixel point with the gray value smaller than the average gray value as 0;
and calculating the proportion of the pixel points with the gray value of 256 to all the pixel points to obtain the density of the insects.
Further, the gray scale conversion formula is as follows: gray ═ R0.299 + G0.587 + B0.114; wherein Gray is a Gray value, R is a red brightness value, G is a green brightness value, and B is a blue brightness value.
Further, the loRa node consists of an STM32 main control chip, a peripheral circuit and a loRa radio frequency module, and is connected with the camera through a serial port; the steerable camera of STM32 main control chip is shot, and then obtains the bottom surface picture of insect trap to and control loRa radio frequency module and send and receive information, communicate with the loRa gateway.
Furthermore, the camera is arranged at the top of the insect trap, and the lens of the camera faces downwards, so that the bottom surface of the insect trap is within the shooting range of the camera.
Further, the bottom surface of the insect trap is a white background bottom surface.
The invention has the beneficial effects that: the system consists of a LoRa gateway, an insect trap and a LoRa node arranged on the insect trap; gather the bottom surface picture of insect trap through the camera that establishes on the loRa node, utilize the loRa node to calculate the bottom surface picture of insect trap and handle, obtain the density size of insect for loRa node resource obtains effective utilization, need not use extra server, can effectively reduce system's cost, sends the density size of the insect that obtains of calculating to the loRa gateway, realizes the remote monitoring to the loRa node.
Drawings
The following detailed description of the invention refers to the accompanying drawings.
Fig. 1 is a diagram of an insect density recognition system based on LoRa nodes according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As shown in fig. 1, the specific embodiment of the present invention is: an insect density identification system based on LoRa nodes, the system is composed of a LoRa gateway 30, an insect trap 10 and a LoRa node 20 arranged on the insect trap 10;
a camera 21 is arranged on the LoRa node 20, the camera 21 is used for shooting a bottom picture of the insect trap 10, and the picture shot by the camera 21 is an RGB picture;
the LoRa node 20 converts the bottom surface picture of the insect trap 10 into a binary picture, and calculates the density of the insects according to the proportion of pixel points occupied by the insects on the binary picture;
the LoRa node 20 sends the calculated density of the insects to the LoRa gateway 30 to remotely monitor the LoRa node 20.
In this embodiment, insects are trapped by insect trap 10, and the density of insects in insect trap 10 or the density increase of insects in a certain period of time can be calculated, so that LoRa node 20 monitors the density of insects, and remote monitoring of LoRa node 20 can be realized through communication between LoRa node 20 and LoRa gateway 30.
In one embodiment, the LoRa node 20 converts the bottom surface image of the insect trap 10 into a binary image, and calculates the density of the insects according to the ratio of the pixels occupied by the insects on the binary image,
converting the RGB picture into a gray picture according to a gray conversion formula;
calculating the gray average value of pixel points of the gray picture;
setting the gray value of the pixel point with the gray value larger than or equal to the average gray value as 256, and setting the gray value of the pixel point with the gray value smaller than the average gray value as 0;
and calculating the proportion of the pixel points with the gray value of 256 to all the pixel points to obtain the density of the insects.
In this embodiment, the LoRa node 20 may obtain the binary gray scale picture by calculating, the binary gray scale picture has an expression form that the point with the pixel point of 256 is an insect, the point with the pixel point of 0 is a bottom surface, and the density of the insect may be calculated by calculating the proportion of the point with the pixel point of 256.
Further, the gray scale conversion formula is as follows: gray ═ R0.299 + G0.587 + B0.114; wherein Gray is a Gray value, R is a red brightness value, G is a green brightness value, and B is a blue brightness value.
Further, the LoRa node 20 is composed of an STM32 main control chip, a peripheral circuit and an LoRa radio frequency module, and is connected to the camera 21 through a serial port; the steerable camera 21 of STM32 main control chip is shot, and then obtains the bottom surface picture of insect trap 10 to and control the loRa radio frequency module and send and receive information, communicate with loRa gateway 30.
Further, the camera 21 is disposed on the top of the insect trap 10, and the lens of the camera 21 faces downward, so that the bottom surface of the insect trap 10 is within the shooting range of the camera 21.
Further, the bottom surface of insect trap 10 is a white background bottom surface.
In summary, the embodiments of the present invention have the following beneficial effects: the system consists of a LoRa gateway 30, an insect trap 10 and a LoRa node 20 arranged on insect trap 10; gather the bottom surface picture of insect trap 10 through camera 21 that establishes on loRa node 20, utilize loRa node 20 to calculate the bottom surface picture of insect trap 10, obtain the density size of insect for loRa node 20 resource obtains effective utilization, need not use extra server, can effectively reduce system's cost, send the density size of the insect that obtains of calculating to loRa gateway 30, realize loRa node 20's remote monitoring.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (6)
1. The utility model provides an insect density identification system based on loRa node which characterized in that: the system consists of a LoRa gateway, an insect trap and a LoRa node arranged on the insect trap;
a camera is arranged on the LoRa node and used for shooting a bottom picture of the insect trap, and the picture shot by the camera is an RGB picture;
the LoRa node converts a bottom surface picture of the insect trap into a binary picture, and the density of the insects is calculated according to the proportion of pixel points occupied by the insects on the binary picture;
the density size of the insect that the loRa node will calculate is sent to the loRa gateway to carry out remote monitoring to the loRa node.
2. An insect density identification system based on LoRa nodes as claimed in claim 1 wherein: the LoRa node converts the bottom surface picture of the insect trap into a binary picture, and calculates the density of the insects according to the proportion of pixel points occupied by the insects on the binary picture,
converting the RGB picture into a gray picture according to a gray conversion formula;
calculating the gray average value of pixel points of the gray picture;
setting the gray value of the pixel point with the gray value larger than or equal to the average gray value as 256, and setting the gray value of the pixel point with the gray value smaller than the average gray value as 0;
and calculating the proportion of the pixel points with the gray value of 256 to all the pixel points to obtain the density of the insects.
3. An insect density identification system based on LoRa nodes as claimed in claim 2 wherein:
the gray scale conversion formula is as follows: gray ═ R0.299 + G0.587 + B0.114; wherein Gray is a Gray value, R is a red brightness value, G is a green brightness value, and B is a blue brightness value.
4. An insect density identification system based on LoRa nodes as claimed in claim 1 wherein:
the LoRa node consists of an STM32 main control chip, a peripheral circuit and a LoRa radio frequency module and is connected with the camera through a serial port; the steerable camera of STM32 main control chip is shot, and then obtains the bottom surface picture of insect trap to and control loRa radio frequency module and send and receive information, communicate with the loRa gateway.
5. An insect density identification system based on LoRa nodes as claimed in claim 1 wherein:
the camera is arranged at the top of the insect trap, and the lens of the camera faces downwards, so that the bottom surface of the insect trap is within the shooting range of the camera.
6. An insect density identification system based on LoRa nodes as claimed in claim 1 wherein:
the bottom surface of the insect trap is a white background bottom surface.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911241985.6A CN111046777B (en) | 2019-12-06 | 2019-12-06 | Insect density identification system based on LoRa node |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911241985.6A CN111046777B (en) | 2019-12-06 | 2019-12-06 | Insect density identification system based on LoRa node |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111046777A true CN111046777A (en) | 2020-04-21 |
| CN111046777B CN111046777B (en) | 2023-07-25 |
Family
ID=70235054
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911241985.6A Active CN111046777B (en) | 2019-12-06 | 2019-12-06 | Insect density identification system based on LoRa node |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111046777B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112288795A (en) * | 2020-10-29 | 2021-01-29 | 深圳大学 | Insect density calculation method and device based on fast-RCNN |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102096808A (en) * | 2011-01-19 | 2011-06-15 | 南京农业大学 | Method for automatically monitoring and reporting insect condition of rice planthopper |
| CN106332855A (en) * | 2015-07-06 | 2017-01-18 | 宁波高新区鹏博科技有限公司 | Automatic early warning system for pests and diseases |
-
2019
- 2019-12-06 CN CN201911241985.6A patent/CN111046777B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102096808A (en) * | 2011-01-19 | 2011-06-15 | 南京农业大学 | Method for automatically monitoring and reporting insect condition of rice planthopper |
| CN106332855A (en) * | 2015-07-06 | 2017-01-18 | 宁波高新区鹏博科技有限公司 | Automatic early warning system for pests and diseases |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112288795A (en) * | 2020-10-29 | 2021-01-29 | 深圳大学 | Insect density calculation method and device based on fast-RCNN |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111046777B (en) | 2023-07-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107680056B (en) | Image processing method and device | |
| CN109361910A (en) | Self-adapted white balance correction method and device | |
| CN106326939A (en) | Parameter optimization method and system of convolutional neural network | |
| CN105574896B (en) | A kind of efficient background modeling method towards high-resolution video | |
| CN104768009B (en) | A kind of image transfer method under desktop virtualization SPICE protocol | |
| CN116188808B (en) | Image feature extraction method and system, storage medium and electronic device | |
| WO2024027287A9 (en) | Image processing system and method, and computer-readable medium and electronic device | |
| KR20170101455A (en) | Training method of robot with 3d camera using artificial intelligence deep learning network based big data platform | |
| CN113436105A (en) | Model training and image optimization method and device, electronic equipment and storage medium | |
| CN101500067B (en) | Fast image processing method for high definition camera | |
| CN111046777A (en) | Insect density recognition system based on LoRa node | |
| JP2021531688A (en) | Data processing methods and devices, electronic devices and storage media | |
| CN110766636B (en) | Intelligent image processing method for AI chip | |
| WO2020135604A1 (en) | Image processing method and device, and unmanned aerial vehicle | |
| CN114998122A (en) | Low-illumination image enhancement method | |
| CN110930474B (en) | Insect density thermodynamic diagram construction method, device and system | |
| CN109035181A (en) | A kind of wide dynamic range image processing method based on mean picture brightness | |
| JP4635975B2 (en) | Image processing apparatus and image processing method | |
| US10546369B2 (en) | Exposure level control for high-dynamic-range imaging, system and method | |
| CN110047077A (en) | A kind of image processing method for ether mill common recognition mechanism | |
| CN103428522B (en) | Method for improving image quality at low illumination level of web camera | |
| JP3870796B2 (en) | Image processing apparatus and image processing method | |
| CN113283447B (en) | Food baking method and device, storage medium and electronic device | |
| CN112166598A (en) | Image processing method, system, movable platform and storage medium | |
| CN110458754A (en) | Image generating method and terminal device |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |