LU502599B1 - Intelligent agricultural management system based on image processing - Google Patents
Intelligent agricultural management system based on image processing Download PDFInfo
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- LU502599B1 LU502599B1 LU502599A LU502599A LU502599B1 LU 502599 B1 LU502599 B1 LU 502599B1 LU 502599 A LU502599 A LU 502599A LU 502599 A LU502599 A LU 502599A LU 502599 B1 LU502599 B1 LU 502599B1
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- 238000012545 processing Methods 0.000 title claims abstract description 34
- 241000607479 Yersinia pestis Species 0.000 claims abstract description 27
- 238000010191 image analysis Methods 0.000 claims abstract description 19
- 230000007613 environmental effect Effects 0.000 claims abstract description 13
- 238000012544 monitoring process Methods 0.000 claims abstract description 11
- 238000007726 management method Methods 0.000 claims description 18
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- 238000013527 convolutional neural network Methods 0.000 claims description 9
- 238000005516 engineering process Methods 0.000 claims description 9
- 238000004458 analytical method Methods 0.000 claims description 7
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- 239000000575 pesticide Substances 0.000 claims description 3
- 238000013138 pruning Methods 0.000 claims description 3
- 241000894007 species Species 0.000 claims description 3
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Abstract
The application discloses an intelligent agricultural management system based on image processing, and comprises: An image acquisition module for acquiring the growth images of crops, a real-time monitoring module for monitoring the growth environment changes of crops in real time, an image analysis module for analyzing crop growth images, a storage database for storing images, an environmental prediction module for predicting the change of crop growth environment, and an early warning prompt module for early warning of plant wilt, pest occurrence and extreme change of growth environment, and a comprehensive processing module for corresponding processing according to different situations. According to the application, the effects of intelligently identifying the growth stage of crops, intelligently judging the growth state of crops and the occurrence of farm pests are realized by identifying and marking the collected images and comparing the features, and early warning prompts with different frequencies can be carried out.
Description
DESCRIPTION LU502599
INTELLIGENT AGRICULTURAL MANAGEMENT SYSTEM BASED ON
IMAGE PROCESSING
The application belongs to the field of intelligent agriculture, and in particular to an intelligent agricultural management system based on image processing.
Smart agriculture refers to the combination of modern science and technology with agricultural planting, so as to realize unmanned, automatic and intelligent management. For example, the integrated application of computer and network technology, Internet of Things technology, audio and video technology, 3S technology, wireless communication technology and expert wisdom are combined with crop planting to realize agricultural visual remote diagnosis, remote control, disaster early warning and other intelligent management means.
With the development of smart agriculture, it is possible to monitor the growth factors of crops in real time, such as temperature, light, air composition, soil conditions and diseases and insect pests, and these data can be obtained in the computer without human participation. For the greenhouse environment, these data are more sensitive, directly related to the growth of crops and the final income. Moreover, previous greenhouse crop yields have been derived from previous cropping experience, which generally varies greatly with weather conditions or changes in field management, making it difficult to accurately predict the final crop yield.
This application aims to provide an intelligent agricultural management system based on image processing to solve the problems existing in the prior art.
To achieve the above purpose, the present application provides an intelligent agricultural management system based on image processing, which comprises: an image acquisition module, a real-time monitoring module, an image analysis module, a storage database, an environment prediction module, an early warning prompt module and a comprehensive processing module which are connected in sequence; the image acquisition module is used for acquiring crop growth images, preprocessing the crop growth images and transmitting the images to the image analysis module;
the real-time monitoring module is used for monitoring the growth environment changes Pfi502599 crops in real time, acquiring growth environment images, and transmitting the growth environment images to the image analysis module; the image analysis module is used for analyzing the crop growth image, outputting an analysis result to the early warning prompt module, and simultaneously transmitting the crop growth image to the storage database; the storage database is used for storing the crop growth image and the crop growth environment image; the environment prediction module is used for extracting the growth environment images of crops in the storage database and performing environment prediction based on the growth environment images; the early warning prompt module is used for generating an early warning prompt signal based on the analysis result and transmitting the early warning prompt signal to the comprehensive processing module; the comprehensive processing module is used for processing based on the early warning prompt signal and the environmental prediction result respectively.
Optionally, the preprocessing comprises identifying and marking the development characteristics of the crops, the withered parts of the crops and the parts with insect pests in the crop growth image.
Optionally, the data storage module is also used to store conventional crop development data based on Internet big data technology; the conventional crop development data comprises plant characteristics of crops in different growth stages and periods of each growth stage.
Optionally, the image analysis module extracts the conventional development data of crops in the storage database, and performs feature extraction on the crop growth image, compares the extracted features with the conventional development data of crops in the storage data module, and determines the growth stage of crops; calculating a withering area based on a withering part of a crop, setting a threshold value according to the withering area, generating a withering early warning signal when the withering area reaches 30% of the plant area of the crop, transmitting the withering early warning signal to the early warning prompt module, and acquiring a withering treatment scheme based on the growth stage of the crop, and transmitting the withering treatment scheme to the comprehensiye 502599 treatment module.
Optionally, the image analysis module calculates the pest area based on the site where the pest occurs on the crops, and when the pest area reaches 10% of the plant area of the crops, generates a pest early warning signal and transmits the pest early warning signal to the early warning prompt module.
Optionally, the environment prediction module performs feature extraction on all crop growth environment images stored in the storage database within 30 days, simultaneously constructs a convolutional neural network feature comparison model, inputs the extracted features into the convolutional neural network feature comparison model for comparison, outputs optimal features, inputs the optimal features into the convolutional neural network feature comparison model, obtains a crop growth environment change curve within 30 days, and predicts the growth environment change of crops based on the growth environment change curve.
Optionally, the environment prediction module calculates an average fluctuation interval of environmental changes based on the growth environment change curve, generates an environmental change early warning signal to the early warning prompt module when the average fluctuation interval is less than or equal to 2 days, and transmits the corresponding growth environment change curve to the comprehensive processing module.
Optionally, the early warning prompt module carries out audio early warning with different frequencies according to different early warning signals, and carries out early warning with a frequency of five times per second when the withering early warning signal 1s obtained; When the pest early warning signal is obtained, carrying out early warning with a frequency of three times per second; when the environmental change early warning signal is obtained, an early warning with a frequency of two times per second is carried out.
Optionally, the comprehensive treatment module carries out watering, fertilizing, pruning and illumination treatment on crops based on the wilting treatment scheme; spraying pesticides and controlling natural enemies based on pest warning signals; control crop species, planting area and total farmland area based on growth environment change curve.
The application has the following technical effects:
According to the application, the effects of intelligently identifying the growth stage of crops, intelligently judging the growth state of crops and the occurrence of farm pests are realized by identifying and marking the collected images and comparing the features, and early 502599 warning prompts with different frequencies can be carried out; meanwhile, the accurate prediction of the growth environment change of crops is realized by constructing a model, thus ensuring the green and safe production of crops.
The accompanying figures, which form a part hereof, and in which is shown by way of illustration a further understanding of the application, and in which is shown by way of illustration the illustrative embodiments and the description thereof, serve to explain the application and are not to be construed as unduly limiting the same.
FIG. 1 is a structural diagram of an intelligent agriculture management system based on image processing in an embodiment of the present application.
It should be noted that the embodiments in the present application and the features in the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in conjunction with embodiments.
It should be noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system, such as a set of computer-executable instructions, and that although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different from that herein.
Embodiment 1
As shown in FIG. 1, the embodiment provides an intelligent agricultural management system based on image processing, which comprises: an image acquisition module, a real-time monitoring module, an image analysis module, a storage database, an environment prediction module, an early warning prompt module and a comprehensive processing module which are connected in sequence; the image acquisition module is used for acquiring crop growth images, preprocessing the crop growth images and transmitting the images to the image analysis module; specifically, the preprocessing comprises identifying and marking the development characteristics of the crops, the withered parts of the crops and the parts with insect pests in the crop growth image.
The real-time monitoring module is used for monitoring the growth environment changes of crops in real time, acquiring growth environment images, and transmitting the growth environment images to the image analysis module; LU502599 the storage database is used for storing crop growth images and crop growth environment images; the storage data module is also used for storing conventional crop development data based on the internet big data technology.
The image analysis module is used for analyzing the crop growth image, outputting an analysis result to the early warning prompt module, and simultaneously transmitting the crop growth image to the storage database; specifically, the analysis process of the image analysis module includes: the image analysis module extracts the conventional development data of crops in the storage database (conventional crop development data include plant characteristics and cycles of different growth stages of crops), and performs feature extraction on the crop growth image, compares the extracted features with the conventional development data of crops in the storage data module, and determines the growth stage of crops; calculating a withering area based on a withering part of a crop, setting a threshold value according to the withering area, generating a withering early warning signal when the withering area reaches 30% of the plant area of the crop, transmitting the withering early warning signal to the early warning prompt module, and acquiring a withering treatment scheme based on the growth stage of the crop, and transmitting the withering treatment scheme to the comprehensive treatment module.
The pest area 1s calculated based on the part where the crop pests occur. When the pest area reaches 10% of the crop plant area, the pest warning signal 1s generated and transmitted to the warning prompt module.
The environment prediction module is used for extracting the growth environment images of crops in the storage database and performing environment prediction based on the growth environment images; the specific prediction process includes: the environment prediction module performs feature extraction on all crop growth environment images stored in the storage database within 30 days, simultaneously constructs a convolutional neural network feature comparison model, inputs the extracted features into the convolutional neural network feature comparison model for comparison, outputs optimal features, inputs the optimal features into the convolutional neural network feature comparison model, obtains a crop growth environment change curve within 30 days, and predicts the growth environment change of crops based on the growth environment change curve. LU502599
Calculating an average fluctuation interval of environmental changes based on the growth environment change curve, generating an environmental change early warning signal to the early warning prompt module when the average fluctuation interval is less than or equal to 2 days, and transmitting the corresponding growth environment change curve to the comprehensive processing module.
The early warning prompt module is used for generating an early warning prompt signal based on the analysis result and transmitting the early warning prompt signal to the comprehensive processing module; in somes embodiments, the early warning prompt module carries out audio early warning with different frequencies according to different early warning signals, and carries out early warning with a frequency of five times per second when the withering early warning signal is obtained; When the pest early warning signal 1s obtained, carrying out early warning with a frequency of three times per second; when the environmental change early warning signal is obtained, an early warning with a frequency of two times per second is carried out. the comprehensive processing module is used for processing based on the early warning prompt signal and the environmental prediction result respectively.
In somes embodiments, the comprehensive treatment module carries out watering, fertilizing, pruning and illumination treatment on crops based on the wilting treatment scheme;
Spraying pesticides and controlling natural enemies based on pest warning signals; control crop species, planting area and total farmland area based on growth environment change curve.
The above are only the preferred embodiments of this application, but the scope of protection of this application is not limited to this. Any changes or substitutions that can be easily thought of by those skilled in the technical field within the technical scope disclosed in this application should be covered by the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.
Claims (9)
1. An intelligent agricultural management system based on image processing, characterized by comprising: an image acquisition module, a real-time monitoring module, an image analysis module, a storage database, an environment prediction module, an early warning prompt module and a comprehensive processing module which are connected in sequence; the image acquisition module is used for acquiring crop growth images, preprocessing the crop growth images and transmitting the images to the image analysis module; the real-time monitoring module is used for monitoring the growth environment changes of crops in real time, acquiring growth environment images, and transmitting the growth environment images to the image analysis module; the image analysis module is used for analyzing the crop growth image, outputting an analysis result to the early warning prompt module, and simultaneously transmitting the crop growth image to the storage database; the storage database is used for storing the crop growth image and the crop growth environment image; the environment prediction module is used for extracting the growth environment images of crops in the storage database and performing environment prediction based on the growth environment images; the early warning prompt module is used for generating an early warning prompt signal based on the analysis result and transmitting the early warning prompt signal to the comprehensive processing module; the comprehensive processing module is used for processing based on the early warning prompt signal and the environmental prediction result respectively.
2. The intelligent agricultural management system based on image processing according to claim 1, characterized in that the preprocessing comprises identifying and marking the development characteristics of the crops, the withered parts of the crops and the parts with insect pests in the crop growth image.
3. The intelligent agricultural management system based on image processing according to claim 1, characterized in that the data storage module is also used to store conventional crop development data based on Internet big data technology; the conventional crop development data comprises plant characteristics of crops in different growth stages and periods of each growth stage. LU502599
4. The intelligent agricultural management system based on image processing according to claim 1, characterized in that the image analysis module extracts the conventional development data of crops in the storage database, and performs feature extraction on the crop growth image, compares the extracted features with the conventional development data of crops in the storage data module, and determines the growth stage of crops; calculating a withering area based on a withering part of a crop, setting a threshold value according to the withering area, generating a withering early warning signal when the withering area reaches 30% of the plant area of the crop, transmitting the withering early warning signal to the early warning prompt module, and acquiring a withering treatment scheme based on the growth stage of the crop, and transmitting the withering treatment scheme to the comprehensive treatment module.
5. The intelligent agricultural management system based on image processing according to claim 4, characterized in that the image analysis module calculates the pest area based on the site where the pest occurs on the crops, and when the pest area reaches 10% of the plant area of the crops, generates a pest early warning signal and transmits the pest early warning signal to the early warning prompt module.
6. The intelligent agricultural management system based on image processing according to claim 1, characterized in that the environment prediction module performs feature extraction on all crop growth environment images stored in the storage database within 30 days, simultaneously constructs a convolutional neural network feature comparison model, inputs the extracted features into the convolutional neural network feature comparison model for comparison, outputs optimal features, inputs the optimal features into the convolutional neural network feature comparison model, obtains a crop growth environment change curve within 30 days, and predicts the growth environment change of crops based on the growth environment change curve.
7. The intelligent agricultural management system based on image processing according to claim 6, characterized in that the environment prediction module calculates an average fluctuation interval of environmental changes based on the growth environment change curve, generates an environmental change early warning signal to the early warning prompt module when the average fluctuation interval is less than or equal to 2 days, and transmits the corresponding growth environment change curve to the comprehensive processing module. LU502599
8. The intelligent agricultural management system based on image processing according to claim 1, characterized in that the early warning prompt module carries out audio early warning with different frequencies according to different early warning signals, and carries out early warning with a frequency of five times per second when the withering early warning signal is obtained; when the pest early warning signal is obtained, carrying out early warning with a frequency of three times per second; when the environmental change early warning signal is obtained, carrying out an early warning with a frequency of two times per second.
9. The intelligent agricultural management system based on image processing according to claim 1, characterized in that the comprehensive treatment module carries out watering, fertilizing, pruning and illumination treatment on crops based on the wilting treatment scheme; spraying pesticides and controlling natural enemies based on pest warning signals; controlling crop species, planting area and total farmland area based on growth environment change curve.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| LU502599A LU502599B1 (en) | 2022-07-28 | 2022-07-28 | Intelligent agricultural management system based on image processing |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| LU502599A LU502599B1 (en) | 2022-07-28 | 2022-07-28 | Intelligent agricultural management system based on image processing |
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| LU502599B1 true LU502599B1 (en) | 2023-01-30 |
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2022
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