CN112800877A - Pest control method based on agricultural Internet of things - Google Patents

Pest control method based on agricultural Internet of things Download PDF

Info

Publication number
CN112800877A
CN112800877A CN202110051665.5A CN202110051665A CN112800877A CN 112800877 A CN112800877 A CN 112800877A CN 202110051665 A CN202110051665 A CN 202110051665A CN 112800877 A CN112800877 A CN 112800877A
Authority
CN
China
Prior art keywords
data
pest
things
svm
layer
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.)
Pending
Application number
CN202110051665.5A
Other languages
Chinese (zh)
Inventor
江煜
杨忠
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jinling Institute of Technology
Original Assignee
Jinling Institute of Technology
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Jinling Institute of Technology filed Critical Jinling Institute of Technology
Priority to CN202110051665.5A priority Critical patent/CN112800877A/en
Publication of CN112800877A publication Critical patent/CN112800877A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
    • A01G13/00—Protecting plants
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16Y—INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE INTERNET OF THINGS [IoT]
    • G16Y10/00—Economic sectors
    • G16Y10/05—Agriculture
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Business, Economics & Management (AREA)
  • Health & Medical Sciences (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Health & Medical Sciences (AREA)
  • Agronomy & Crop Science (AREA)
  • Computing Systems (AREA)
  • General Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Economics (AREA)
  • General Engineering & Computer Science (AREA)
  • Primary Health Care (AREA)
  • Animal Husbandry (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Mining & Mineral Resources (AREA)
  • Evolutionary Computation (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Human Resources & Organizations (AREA)
  • Marketing (AREA)
  • Multimedia (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Toxicology (AREA)
  • Environmental Sciences (AREA)
  • Accounting & Taxation (AREA)
  • Development Economics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Medical Informatics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A pest control method based on an agricultural Internet of things comprises the following steps: step 1, building an STM 32-based pest control Internet of things system; step 2: determining a network layer communication protocol; step 3, acquiring simulated noise environment data; step 4, training an SVM pest classification model; and 5, pest control based on the agricultural Internet of things is realized. The invention simulates the data collected by a sensor in a noise environment, provides a pest control method based on the agricultural Internet of things, improves the stability and robustness of the Internet of things platform layer for classifying pests, and meanwhile, the Internet of things platform layer combines various data information collected by a sensing layer to pertinently control the pests.

Description

Pest control method based on agricultural Internet of things
Technical Field
The invention relates to the field of agricultural Internet of things, in particular to a pest control method based on the agricultural Internet of things.
Background
Agriculture is the basic industry of national economy in China, and the pest control effect is directly related to agricultural production benefit, agricultural product quality safety, agricultural ecological environment safety and the like. In recent years, global climate is abnormally changeable, farming systems and production modes are changed, crop multiple cropping indexes are improved, and crop insect pests are frequently and massively infected. The insect pest detection method mainly depends on the experience of growers, agricultural technicians and agricultural experts are invited to guide the plants at home or remotely in some areas, and the traditional method usually misses the best prevention and control opportunity, and has low efficiency, high cost and poor timeliness.
In recent years, with the development of machine learning and deep learning, the occurrence of agricultural insect pests can be greatly reduced by recognizing agricultural insect pests based on machine vision recognition and convolutional neural network deep learning, when the characteristics of pest bodies appear, the rapid analysis and judgment of images can be realized, the insect pest types can be accurately positioned, early warning is timely sent out, and the pests are killed in germination. Meanwhile, with the wide deployment of sensors in the real world, the internet technology gradually permeates into the physical entity world, more and more physical entities are connected to the internet through the sensors to realize information sharing, and the internet of things is applied to the background. The new wave of information development of the Internet of Things (Internet of Things) has attracted great attention from the industry and academia. The thing networking has contained 4 parts: the system comprises physical entities in the real world, sensors for sensing state information of the physical entities, a transmission network and an intelligent processing system.
Disclosure of Invention
In order to solve the problems, the invention simulates the data collected by the sensor in the noise environment, provides the pest control method based on the agricultural Internet of things, improves the stability and robustness of the Internet of things platform layer for classifying the pests, and meanwhile, the Internet of things platform layer combines various data information collected by the sensing layer to pertinently control the pests. In addition, in order to enhance the separability of farmland image data, the invention adopts an SVM classifier. To achieve the purpose, the invention provides a pest control method based on an agricultural Internet of things, which comprises the following specific steps:
step 1, building a pest control Internet of things system based on STM 32: the STM32 is used as a controller of a network layer of the Internet of things, and a WiFi communication module is used as a network layer to be in wireless communication with a perception layer and a platform layer respectively;
step 2: determining a network layer communication protocol: the communication data format comprises a frame head, an address, command codes, effective data, check bits and a frame tail, different command codes are used for sending a working instruction to the STM32, and address coding is carried out on different sensing layer sensors so as to distinguish data;
step 3, acquiring simulated noise environment data: adding salt and pepper noise to the agricultural image data acquired by the platform in the step 2, and simulating interference on an acquired signal in a noise environment by adding the salt and pepper noise;
step 4, training an SVM pest classification model: dividing the agricultural image data obtained in the step 3 into training samples and testing samples, taking the characteristics of the training samples as input, taking the labels of the training samples as output, and obtaining an SVM pest classification model;
and 5, realizing pest control based on the agricultural Internet of things: and sending a corresponding command code to an STM32 controller according to a pest classification result output by the SVM pest classification model, and killing pests in a targeted manner through an external pest control system.
Further, the pest control internet of things system based on STM32 built in the step 1 can be expressed as follows:
the system is divided into: perception layer, network layer, the data of perception layer are connected with the network layer through STM 32's wiFi module, and the perception layer includes: the temperature data that temperature sensor gathered, the humidity data that humidity transducer gathered, the PH value data that PH value sensor gathered, the light intensity data that light intensity sensor gathered, the farmland image data that CCD sensor gathered, every sensor node is independent each other. The STM32 controller passes through drive circuit, filtering amplifier circuit, AD converting circuit and gathers the all kinds of data on perception layer to utilize the WIFI module to send the data transmission who gathers to the thing networking platform, the thing networking platform layer is to the processing analysis of further step of data that detect.
Further, the acquisition of the simulated noise environment data in step 3 can be represented as:
before data are processed and analyzed on an Internet of things platform layer, a pest classification model based on the agricultural Internet of things is constructed. Thing networking platform layer distinguishes the data that the perception layer was gathered through network communication protocol's command code to obtain the farmland image of real-time collection, after STM32 detected all kinds of data, can be with data packing into data format and be: frame head, address, command code, valid data, check digit, frame tail, platform layer receive farmland image data after, establish farmland image data set, add salt and pepper noise simulation environmental noise on the basis of image data set, the salt and pepper noise model that adds is:
Figure BDA0002899286420000021
wherein, ImaxAnd IminIs the maximum value and the minimum value of the image pixel points, p is the probability of noise occurrence of the image, the value range of p is 20 percent to 30 percent, ixyThe actual value of the collected image pixel point (x, y) is obtained, and the f (x, y) is the image pixel point value after the salt and pepper noise is added.
Further, the process of training the SVM pest classification model in step 4 can be expressed as:
the agricultural image dataset constitutes a sample set (x) of k datai,yi)jWherein j is 1,2, the., k, i is 1,2, the., n, n is the number of training samples, y is the pest category label, and the optimal hyperplane of k classifications obtained by learning through an interval maximization learning strategy is as follows:
ω·x+b＝0 (2)
where ω is the normal vector and b is the displacement, the general form of the corresponding linear classification function is:
f(x)＝ω·x+b (3)
while the optimal hyperplane makes all sample points satisfy:
f(x)|≥1 (4)
the support vector of the SVM is a sample point for establishing the formula 4, and after the support vector is found, the data can be divided into two types; the SVM model adopts a Gaussian radial basis kernel function, wherein the Gaussian radial basis kernel function is as follows:
Figure BDA0002899286420000031
during classification, the SVM algorithm maps sample data from a low-dimensional feature space to a high-dimensional feature space in a nonlinear way through a kernel function, an optimal hyperplane with maximized interval is found in the high-dimensional feature space, and the nonlinear classification function is as follows:
Figure BDA0002899286420000032
the SVM algorithm is a two-classification model, a plurality of two-classification models are constructed at the same time, the multi-classification problem can be solved, when n pest categories exist, a group of SVM classifiers need to design n (n-1)/2 SVM models, and finally k SVM pest classifiers are trained.
Further, the process of implementing pest control based on the agricultural internet of things in step 5 can be expressed as:
after obtaining SVM pest classifier in step 4 training, imbed the classifier in the platform layer of thing networking, the temperature data is monitored at each monitoring point to the perception layer, humidity data, PH value data, light intensity data, behind the farmland image data, utilize the WIFI module to send to the platform layer with data packing through STM32, the platform layer distinguishes various data through the command code in the data, independently carry out the analysis, show real-time temperature data on the platform, humidity data, PH value data, light intensity data, and input farmland image data to SVM pest classifier, output farmland pest type, combine all kinds of data to have pertinence ground infrastructure, adjust planting structure, reasonable fertilization, in time water, prevention and cure measures such as chemical prevention and cure send to the platform layer, remind the grower to prevent and cure.
The pest control method based on the agricultural Internet of things has the beneficial effects that: the invention has the technical effects that:
1. the agricultural data acquisition system utilizes the sensing layer to acquire various agricultural data, and independently transmits various data to the platform layer through the driving circuit, the filtering and amplifying circuit, the A/D conversion circuit, the WIFI module and the like;
2. the method effectively simulates the interference of the agricultural image in the noise environment when the data are collected, and the data simulating the noise are used for training the SVM classification model, so that the robustness and stability of the model in the noise environment are enhanced;
drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following detailed description and accompanying drawings:
the invention provides a pest control method based on an agricultural Internet of things, and aims to improve the robustness of data decoupling of a multi-dimensional force sensor in a noise environment and improve the stability and accuracy of data decoupling. FIG. 1 is a flow chart of the present invention. The steps of the present invention will be described in detail with reference to the flow chart.
Step 1, building a pest control Internet of things system based on STM 32: the STM32 is used as a controller of a network layer of the Internet of things, and a WiFi communication module is used as a network layer to be in wireless communication with a perception layer and a platform layer respectively;
the pest control internet of things system based on STM32 built in the step 1 can be expressed as follows:
the system is divided into: perception layer, network layer, the data of perception layer are connected with the network layer through STM 32's wiFi module, and the perception layer includes: the temperature data that temperature sensor gathered, the humidity data that humidity transducer gathered, the PH value data that PH value sensor gathered, the light intensity data that light intensity sensor gathered, the farmland image data that CCD sensor gathered, every sensor node is independent each other. The STM32 controller passes through drive circuit, filtering amplifier circuit, AD converting circuit and gathers the all kinds of data on perception layer to utilize the WIFI module to send the data transmission who gathers to the thing networking platform, the thing networking platform layer is to the processing analysis of further step of data that detect.
Step 2: determining a network layer communication protocol: the communication data format comprises a frame head, an address, command codes, effective data, check bits and a frame tail, different command codes are used for sending a working instruction to the STM32, and address coding is carried out on different sensing layer sensors so as to distinguish data;
step 3, acquiring simulated noise environment data: adding salt and pepper noise to the agricultural image data acquired by the platform in the step 2, and simulating interference on an acquired signal in a noise environment by adding the salt and pepper noise;
the acquisition of the simulated noise environment data in step 3 can be represented as:
before data are processed and analyzed on an Internet of things platform layer, a pest classification model based on the agricultural Internet of things is constructed. Thing networking platform layer distinguishes the data that the perception layer was gathered through network communication protocol's command code to obtain the farmland image of real-time collection, after STM32 detected all kinds of data, can be with data packing into data format and be: frame head, address, command code, valid data, check digit, frame tail, platform layer receive farmland image data after, establish farmland image data set, add salt and pepper noise simulation environmental noise on the basis of image data set, the salt and pepper noise model that adds is:
Figure BDA0002899286420000041
wherein, ImaxAnd IminIs the maximum value and the minimum value of the image pixel points, p is the probability of noise occurrence of the image, the value range of p is 20 percent to 30 percent, ixyThe actual value of the collected image pixel point (x, y) is obtained, and the f (x, y) is the image pixel point value after the salt and pepper noise is added.
Step 4, training an SVM pest classification model: dividing the agricultural image data obtained in the step 3 into training samples and testing samples, taking the characteristics of the training samples as input, taking the labels of the training samples as output, and obtaining an SVM pest classification model;
the process of training the SVM pest classification model in step 4 can be expressed as:
the agricultural image dataset constitutes a sample set (x) of k datai,yi)jWhere j is 1, 2.. times, k, i is 1, 2.. times, n, n is the number of training samples, y ∈ { -1, 1} isPest category labels, and learning by an interval maximization learning strategy to obtain the optimal hyperplane of k classifications as follows:
ω·x+b＝0 (2)
where ω is the normal vector and b is the displacement, the general form of the corresponding linear classification function is:
f(x)＝ω·x+b (3)
while the optimal hyperplane makes all sample points satisfy:
f(x)|≥1 (4)
the support vector of the SVM is a sample point for establishing the formula 4, and after the support vector is found, the data can be divided into two types; the SVM model adopts a Gaussian radial basis kernel function, wherein the Gaussian radial basis kernel function is as follows:
Figure BDA0002899286420000051
during classification, the SVM algorithm maps sample data from a low-dimensional feature space to a high-dimensional feature space in a nonlinear way through a kernel function, an optimal hyperplane with maximized interval is found in the high-dimensional feature space, and the nonlinear classification function is as follows:
Figure BDA0002899286420000052
the SVM algorithm is a two-classification model, a plurality of two-classification models are constructed at the same time, the multi-classification problem can be solved, when n pest categories exist, a group of SVM classifiers need to design n (n-1)/2 SVM models, and finally k SVM pest classifiers are trained.
And 5, realizing pest control based on the agricultural Internet of things: and sending a corresponding command code to an STM32 controller according to a pest classification result output by the SVM pest classification model, and killing pests in a targeted manner through an external pest control system.
The process of realizing pest control based on the agricultural internet of things in the step 5 can be expressed as follows:
after obtaining SVM pest classifier in step 4 training, imbed the classifier in the platform layer of thing networking, the temperature data is monitored at each monitoring point to the perception layer, humidity data, PH value data, light intensity data, behind the farmland image data, utilize the WIFI module to send to the platform layer with data packing through STM32, the platform layer distinguishes various data through the command code in the data, independently carry out the analysis, show real-time temperature data on the platform, humidity data, PH value data, light intensity data, and input farmland image data to SVM pest classifier, output farmland pest type, combine all kinds of data to have pertinence ground infrastructure, adjust planting structure, reasonable fertilization, in time water, prevention and cure measures such as chemical prevention and cure send to the platform layer, remind the grower to prevent and cure.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, but any modifications or equivalent variations made according to the technical spirit of the present invention are within the scope of the present invention as claimed.

Claims (5)

1. A pest control method based on an agricultural Internet of things comprises the following specific steps:
step 1, building a pest control Internet of things system based on STM 32: the STM32 is used as a controller of a network layer of the Internet of things, and a WiFi communication module is used as a network layer to be in wireless communication with a perception layer and a platform layer respectively;
step 2: determining a network layer communication protocol: the communication data format comprises a frame head, an address, command codes, effective data, check bits and a frame tail, different command codes are used for sending a working instruction to the STM32, and address coding is carried out on different sensing layer sensors so as to distinguish data;
step 3, acquiring simulated noise environment data: adding salt and pepper noise to the agricultural image data acquired by the platform in the step 2, and simulating interference on an acquired signal in a noise environment by adding the salt and pepper noise;
step 4, training an SVM pest classification model: dividing the agricultural image data obtained in the step 3 into training samples and testing samples, taking the characteristics of the training samples as input, taking the labels of the training samples as output, and obtaining an SVM pest classification model;
and 5, realizing pest control based on the agricultural Internet of things: and sending a corresponding command code to an STM32 controller according to a pest classification result output by the SVM pest classification model, and killing pests in a targeted manner through an external pest control system.
2. The pest control method based on the agricultural internet of things as claimed in claim 1, wherein: the pest control internet of things system based on STM32 built in the step 1 can be expressed as follows:
the system is divided into: perception layer, network layer, the data of perception layer are connected with the network layer through STM 32's wiFi module, and the perception layer includes: the system comprises a temperature sensor, a humidity sensor, a PH value sensor, a light intensity sensor and a CCD sensor, wherein each sensor node is independent from each other; the STM32 controller passes through drive circuit, filtering amplifier circuit, AD converting circuit and gathers the all kinds of data on perception layer to utilize the WIFI module to send the data transmission who gathers to the thing networking platform, the thing networking platform layer is to the processing analysis of further step of data that detect.
3. The pest control method based on the agricultural internet of things as claimed in claim 1, wherein: the acquisition of the simulated noise environment data in step 3 can be represented as:
before thing networking platform layer is handled the analysis to data, found earlier pest classification model based on agricultural thing networking, the data that perception layer was gathered are distinguished to the command code that thing networking platform layer passes through network communication agreement to obtain the farmland image of real-time collection, after STM32 detected all kinds of data, can be with data packing into the data format and be: frame head, address, command code, valid data, check digit, frame tail, platform layer receive farmland image data after, establish farmland image data set, add salt and pepper noise simulation environmental noise on the basis of image data set, the salt and pepper noise model that adds is:
Figure FDA0002899286410000011
wherein, ImaxAnd IminIs the maximum value and the minimum value of the image pixel points, p is the probability of noise occurrence of the image, the value range of p is 20 percent to 30 percent, ixyThe actual value of the collected image pixel point (x, y) is obtained, and the f (x, y) is the image pixel point value after the salt and pepper noise is added.
4. The pest control method based on the agricultural internet of things as claimed in claim 1, wherein: the process of training the SVM pest classification model in step 4 can be expressed as:
the agricultural image dataset constitutes a sample set (x) of k datai,yi)jWherein j is 1,2, the., k, i is 1,2, the., n, n is the number of training samples, y is the pest category label, and the optimal hyperplane of k classifications obtained by learning through an interval maximization learning strategy is as follows:
ω·x+b＝0 (2)
where ω is the normal vector and b is the displacement, the general form of the corresponding linear classification function is:
f(x)＝ω·x+b (3)
while the optimal hyperplane makes all sample points satisfy:
f(x)|≥1 (4)
the support vector of the SVM is a sample point for establishing the formula 4, and after the support vector is found, the data can be divided into two types; the SVM model adopts a Gaussian radial basis kernel function, wherein the Gaussian radial basis kernel function is as follows:
Figure FDA0002899286410000021
during classification, the SVM algorithm maps sample data from a low-dimensional feature space to a high-dimensional feature space in a nonlinear way through a kernel function, an optimal hyperplane with maximized interval is found in the high-dimensional feature space, and the nonlinear classification function is as follows:
Figure FDA0002899286410000022
the SVM algorithm is a two-classification model, a plurality of two-classification models are constructed at the same time, the multi-classification problem can be solved, when n pest categories exist, a group of SVM classifiers need to design n (n-1)/2 SVM models, and finally k SVM pest classifiers are trained.
5. The pest control method based on the agricultural internet of things as claimed in claim 1, wherein: the process of realizing pest control based on the agricultural internet of things in the step 5 is represented as follows:
after obtaining SVM pest classifier in step 4 training, imbed the classifier in the platform layer of thing networking, the temperature data is monitored at each monitoring point to the perception layer, humidity data, PH value data, light intensity data, behind the farmland image data, utilize the WIFI module to send to the platform layer with data packing through STM32, the platform layer distinguishes various data through the command code in the data, independently carry out the analysis, show real-time temperature data on the platform, humidity data, PH value data, light intensity data, and input farmland image data to SVM pest classifier, output farmland pest type, combine all kinds of data to have pertinence ground infrastructure, adjust planting structure, reasonable fertilization, in time water, prevention and cure measures such as chemical prevention and cure send to the platform layer, remind the grower to prevent and cure.
CN202110051665.5A 2021-01-15 2021-01-15 Pest control method based on agricultural Internet of things Pending CN112800877A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110051665.5A CN112800877A (en) 2021-01-15 2021-01-15 Pest control method based on agricultural Internet of things

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110051665.5A CN112800877A (en) 2021-01-15 2021-01-15 Pest control method based on agricultural Internet of things

Publications (1)

Publication Number Publication Date
CN112800877A true CN112800877A (en) 2021-05-14

Family

ID=75809425

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110051665.5A Pending CN112800877A (en) 2021-01-15 2021-01-15 Pest control method based on agricultural Internet of things

Country Status (1)

Country Link
CN (1) CN112800877A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113741597A (en) * 2021-09-03 2021-12-03 安徽中昆绿色防控科技有限公司 Intelligent control system for insect trapping, measuring and reporting in agriculture and forestry

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106210043A (en) * 2016-07-11 2016-12-07 太原理工大学 A kind of industrialized agriculture environmental information remote intelligent monitoring system based on Internet of Things
CN110913012A (en) * 2019-12-05 2020-03-24 金陵科技学院 High-speed parallel data processing method based on agricultural Internet of things
CN112085032A (en) * 2020-09-14 2020-12-15 衢州学院 Grapefruit disease and insect pest dynamic monitoring model based on combination of Internet of things image processing and SVM (support vector machine)

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106210043A (en) * 2016-07-11 2016-12-07 太原理工大学 A kind of industrialized agriculture environmental information remote intelligent monitoring system based on Internet of Things
CN110913012A (en) * 2019-12-05 2020-03-24 金陵科技学院 High-speed parallel data processing method based on agricultural Internet of things
CN112085032A (en) * 2020-09-14 2020-12-15 衢州学院 Grapefruit disease and insect pest dynamic monitoring model based on combination of Internet of things image processing and SVM (support vector machine)

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
江 煜 等: "基于 Ｍ２Ｍ 平台的智能农业物联网监控系统设计", 《金陵科技学院学报》, pages 52 - 54 *
江 煜 等: "物联网工程中 Ｍ２Ｍ 技术研究", 《金陵科技学院学报》, pages 17 - 21 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113741597A (en) * 2021-09-03 2021-12-03 安徽中昆绿色防控科技有限公司 Intelligent control system for insect trapping, measuring and reporting in agriculture and forestry
CN113741597B (en) * 2021-09-03 2022-04-12 安徽中昆绿色防控科技有限公司 Intelligent control system for insect trapping, measuring and reporting in agriculture and forestry

Similar Documents

Publication Publication Date Title
Abdullahi et al. Convolution neural network in precision agriculture for plant image recognition and classification
Maduranga et al. Machine learning applications in IoT based agriculture and smart farming: A review
Wani et al. An appropriate model predicting pest/diseases of crops using machine learning algorithms
CN101953287B (en) Multi-data based crop water demand detection system and method
CN110110595B (en) Farmland image and medicine hypertrophy data analysis method based on satellite remote sensing image
Yashaswini et al. Smart automated irrigation system with disease prediction
Ramana et al. Leaf disease classification in smart agriculture using deep neural network architecture and IoT
CN116681929A (en) Wheat crop disease image recognition method
CN112526909A (en) Wisdom agricultural equipment system based on thing networking
CN112800877A (en) Pest control method based on agricultural Internet of things
CN113591990A (en) Meteorological disaster early warning method based on agricultural Internet of things
CN118261488A (en) Intelligent management system based on digital farm
Abdulla et al. Agriculture based on internet of things and deep learning
Abdulla et al. Design a mobile application to detect tomato plant diseases based on deep learning
CN116560946A (en) Soil pollution data pushing system based on cloud computing
Kalaiarasi et al. Pest Detection and Identification on Plants Using CNN Algorithm: A survey
CN116818002A (en) Intelligent agricultural planting monitoring system based on big data
FAISAL A pest monitoring system for agriculture using deep learning
Huang et al. Application of data augmentation and migration learning in identification of diseases and pests in tea trees
CN110796639B (en) Pinellia ternata quality grading method based on neural network
Diya et al. IoT-based Precision Agriculture: A Review
Kaliappan et al. Plant Disease Classification and Identification Using Deep Convolutional Neural Network
Varalakshmi et al. Automatic plant escalation monitoring system using IoT
CN213659215U (en) Planting environment monitoring system
Qin et al. Tobacco top flowering period recognition and detection model based on improved YOLOv4 and Mask R-CNN network

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
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20210514