CN115204689A - Intelligent agricultural management system based on image processing - Google Patents
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Abstract
The invention discloses an intelligent agricultural management system based on image processing, which comprises: the system comprises an image acquisition module for acquiring crop growth images, a real-time monitoring module for monitoring the growth environment change of crops in real time, an image analysis module for analyzing the crop growth images, a storage database for storing images, an environment prediction module for predicting the growth environment change of the crops, an early warning prompt module for early warning of plant withering, insect pest occurrence and growth environment great change and a comprehensive processing module for carrying out corresponding processing according to different conditions. According to the invention, the effects of intelligently identifying the growth stage of crops and intelligently judging the growth state of the crops and the occurrence of farmland pests are realized by carrying out identification marking and feature extraction comparison on the acquired images, early warning prompts with different frequencies can be carried out, and meanwhile, the accurate prediction on the growth environment change of the crops is realized by constructing a model.
Description
Technical Field
The invention belongs to the field of intelligent agriculture, and particularly relates to an intelligent agricultural management system based on image processing.
Background
Smart agriculture refers to combining modern scientific technology and agricultural planting to achieve unmanned, automatic and intelligent management, for example, combining an integrated application computer with a network technology, an internet of things technology, an audio and video technology, a 3S technology, a wireless communication technology and expert wisdom with crop planting to achieve intelligent management means such as agricultural visual remote diagnosis, remote control and catastrophe early warning.
With the development of intelligent agriculture, it is possible to monitor growth factors required by crops, such as temperature, light, air composition, soil conditions, and pest and disease conditions, in real time, and to obtain these data therein without human intervention. For greenhouse environments, these data are more sensitive, directly related to the growth of the crop and ultimately the profitability. In addition, the yield of the previous greenhouse crops is obtained according to the planting experience of the season before, and the experience generally generates great difference along with the change of weather conditions or field management, so that the final crop yield is difficult to accurately forecast.
Disclosure of Invention
The invention aims to provide an intelligent agricultural management system based on image processing to solve the problems in the prior art.
In order to achieve the above object, the present invention provides an intelligent agricultural management system based on image processing, which comprises: the system 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;
the image acquisition module is used for acquiring a crop growth image, preprocessing the crop growth image and transmitting the crop growth image to the image analysis module;
the real-time monitoring module is used for monitoring the growth environment change of crops in real time, acquiring a growth environment image and transmitting the growth environment image 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 a crop growth environment image in the storage database and performing environment prediction based on the growth environment image;
the early warning prompting module is used for generating an early warning prompting signal based on the analysis result and transmitting the early warning prompting signal to the comprehensive processing module;
and the comprehensive processing module is used for processing based on the early warning prompt signal and the environment prediction result respectively.
Optionally, the pre-processing comprises: and 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 further configured to store the conventional crop development data based on an internet big data technology;
the general development data of the crops comprise plant characteristics of different growth stages of the crops and the period of each growth stage.
Optionally, the image analysis module extracts the conventional crop development data in the storage database, performs feature extraction on the crop growth image, compares the extracted features with the conventional crop development data in the storage data module, and determines the growth stage of the crop;
calculating the withered area based on the withered part of crops, setting a threshold value according to the withered area, generating a withered early warning signal when the withered area reaches 30% of the area of a crop plant, transmitting the withered early warning signal to the early warning prompting module, acquiring a withered processing scheme based on the growth stage of the crops, and transmitting the withered processing scheme to the comprehensive processing module.
Optionally, the image analysis module calculates the pest area based on the part of the crop where the pest occurs, and when the pest area reaches 10% of the area of the crop plant, a pest early warning signal is generated and transmitted to the early warning prompt module.
Optionally, the environment prediction module extracts features of all crop growth environment images stored in the storage database within 30 days, 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 the environment change based on the growth environment change curve, generates an environment 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 prompting module performs audio early warning with different frequencies according to different early warning signals, and performs early warning with the frequency 5 times per second when the wilting early warning signal is acquired; when insect pest early warning signals are obtained, early warning is carried out for 3 times per second; when the environment change early warning signal is obtained, early warning of the frequency is carried out at 2 times per second.
Optionally, the comprehensive processing module performs watering, fertilizing, trimming and lighting treatment on crops based on the withering treatment scheme; spraying pesticides and preventing and treating natural enemies based on the insect pest early warning signal; and controlling the crop species, the planting area and the total farmland area based on the growth environment change curve.
The invention has the technical effects that:
according to the invention, the collected images are subjected to identification marking and feature extraction and are compared, so that the effects of intelligently identifying the growth stage of crops and intelligently judging the growth state of the crops and the occurrence of farmland pests are realized, early warning prompts with different frequencies can be carried out, meanwhile, the accurate prediction of the growth environment change of the crops is realized by constructing a model, and the guarantee is provided for the green and safe production of the crops.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a block diagram of an intelligent agriculture management system based on image processing according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures 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 flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
Example one
As shown in fig. 1, the present embodiment provides an intelligent agriculture management system based on image processing, which includes, connected in sequence:
image acquisition module, real-time supervision module, image analysis module, storage database, environmental prediction module, early warning suggestion module, comprehensive processing module, wherein:
the image acquisition module is used for acquiring a crop growth image, preprocessing the crop growth image and transmitting the crop growth image to the image analysis module; specifically, the pretreatment process comprises: and identifying and marking the development characteristics of 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 change of crops in real time, acquiring a growth environment image and transmitting the growth environment image to the image analysis module;
the storage database is used for storing the crop growth image and the crop growth environment image; the data storage module is also used for storing the 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 comprises the following steps:
extracting conventional crop development data (the conventional crop development data comprises plant characteristics of different growth stages of crops and periods of each growth stage) in a storage database, extracting characteristics of a crop growth image, and comparing the extracted characteristics with the conventional crop development data in a storage data module to determine the growth stage of the crops;
calculating the withered area based on the withered part of the crops, setting a threshold value according to the withered area, generating a withered early warning signal when the withered area reaches 30% of the plant area of the crops, transmitting the withered early warning signal to an early warning prompt module, acquiring a withered processing scheme based on the growth stage of the crops, and transmitting the withered processing scheme to a comprehensive processing module.
The pest area is calculated based on the part of the crop where the pest occurs, when the pest area reaches 10% of the area of the crop plant, a pest early warning signal is generated, and the pest early warning signal is transmitted to the early warning prompt module.
The environment prediction module is used for extracting a crop growth environment image in the storage database and carrying out environment prediction based on the growth environment image; the specific prediction process comprises the following steps:
the environment prediction module extracts features of 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.
And calculating the average fluctuation interval of the environmental change based on the growth environmental change curve, generating an environmental change early warning signal to an early warning prompt module when the average fluctuation interval is less than or equal to 2 days, and transmitting the corresponding growth environmental change curve to a comprehensive processing module.
The early warning prompting module is used for generating an early warning prompting signal based on the analysis result and transmitting the early warning prompting signal to the comprehensive processing module;
in some embodiments, the early warning prompting module performs audio early warning with different frequencies according to different early warning signals, and performs early warning with the frequency 5 times per second when the wilting early warning signal is acquired; when insect pest early warning signals are obtained, early warning is carried out for 3 times per second; when the environment change early warning signal is obtained, early warning of the frequency is carried out at 2 times per second.
The comprehensive processing module is used for processing based on the acquired different signals respectively;
in some embodiments, the integrated processing module waters, fertilizes, trims, illuminates crops based on a wilting treatment plan; spraying pesticides and preventing and treating natural enemies based on the insect pest early warning signal; and controlling the crop species, the planting area and the total farmland area based on the growth environment change curve.
The above description is only for the preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The utility model provides an wisdom agricultural management system based on image processing which characterized in that includes and links to each other in proper order: the system 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;
the image acquisition module is used for acquiring a crop growth image, preprocessing the crop growth image and transmitting the crop growth image to the image analysis module;
the real-time monitoring module is used for monitoring the growth environment change of crops in real time, acquiring a growth environment image and transmitting the growth environment image 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 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 a crop growth environment image in the storage database and performing environment prediction based on the growth environment image;
the early warning prompting module is used for generating an early warning prompting signal based on the analysis result and transmitting the early warning prompting signal to the comprehensive processing module;
and the comprehensive processing module is used for processing based on the early warning prompt signal and the environment prediction result respectively.
2. The intelligent image-processing-based agricultural management system of claim 1, wherein the pre-processing comprises: and 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 as claimed in claim 1, wherein the data storage module is further configured to store the general crop development data based on internet big data technology;
the general development data of the crops comprise plant characteristics of the crops at different growth stages and the period of each growth stage.
4. The intelligent agricultural management system based on image processing as claimed in claim 1, wherein the image analysis module extracts the regular development data of crops in the storage database, performs feature extraction on the growth images of crops, compares the extracted features with the regular development data of crops in the storage data module, and determines the growth stage of crops;
calculating the withered area based on the withered part of crops, setting a threshold value according to the withered area, generating a withered early warning signal when the withered area reaches 30% of the area of a crop plant, transmitting the withered early warning signal to the early warning prompting module, acquiring a withered processing scheme based on the growth stage of the crops, and transmitting the withered processing scheme to the comprehensive processing module.
5. The intelligent agricultural management system based on image processing according to claim 4, wherein the image analysis module calculates pest area based on the pest position of the crop, and when the pest area reaches 10% of the crop plant area, a pest early warning signal is generated and transmitted to the early warning prompt module.
6. The intelligent agricultural management system based on image processing as claimed in claim 1, wherein the environment prediction module performs feature extraction on all the crop growth environment images stored in the storage database for 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 as claimed in claim 6, wherein 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 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.
8. The intelligent agricultural management system based on image processing as claimed in claim 1, wherein the early warning prompting module performs audio early warning with different frequencies according to different early warning signals, and performs early warning with frequency 5 times per second when the wilting early warning signal is obtained; when insect pest early warning signals are obtained, early warning is carried out for 3 times per second; when the environment change early warning signal is obtained, early warning of the frequency is carried out at 2 times per second.
9. The intelligent image processing-based agricultural management system of claim 1, wherein the integrated processing module waters, fertilizes, prunes, and illuminates crops based on a wilting treatment scheme; spraying pesticides and controlling natural enemies based on the insect pest early warning signal; and controlling the crop species, the planting area and the total farmland area based on the growth environment change curve.
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| CN116797601B (en) * | 2023-08-24 | 2023-11-07 | 西南林业大学 | Image recognition-based Huashansong growth dynamic monitoring method and system |
| CN117634738A (en) * | 2023-11-28 | 2024-03-01 | 北京发祥地科技发展有限责任公司 | Internet of things agricultural environment-friendly integrated platform based on artificial intelligence |
| CN117854012A (en) * | 2024-03-07 | 2024-04-09 | 成都智慧城市信息技术有限公司 | Crop environment monitoring method and system based on big data |
| CN117854012B (en) * | 2024-03-07 | 2024-05-14 | 成都智慧城市信息技术有限公司 | Crop environment monitoring method and system based on big data |
| CN118410923A (en) * | 2024-07-01 | 2024-07-30 | 江苏煤炭地质物测队 | Digital agricultural intelligent management platform |
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