CN115204689A - An intelligent agricultural management system based on image processing - Google Patents

Abstract

The invention discloses a smart agricultural management system based on image processing, comprising: an image acquisition module for collecting crop growth images, a real-time monitoring module for real-time monitoring of changes in the growth environment of crops, and a real-time monitoring module for analyzing crop growth images An image analysis module, a storage database for image storage, an environmental prediction module for predicting changes in the growth environment of crops, an early warning module for early warning of plant withering, pest occurrence, and extreme changes in the growth environment, and corresponding responses according to different situations. Integrated processing module for processing. The invention realizes the effect of intelligently identifying the growth stage of crops, intelligently judging the growth state of crops and the occurrence of insect pests in the farmland by comparing the collected images with identification marks and feature extraction, and can carry out early warning prompts with different frequencies. Accurate prediction of changes in the growing environment of crops.

Description

An intelligent agricultural management system based on image processing

technical field

The invention belongs to the field of smart agriculture, and in particular relates to a smart agricultural management system based on image processing.

Background technique

Smart agriculture refers to the combination of modern science and technology with agricultural planting to achieve unmanned, automated 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 the combination of expert wisdom and crop planting can realize the means of intelligent management such as agricultural visualization remote diagnosis, remote control, and disaster warning.

With the development of smart agriculture, it is possible to monitor the growth factors required by crops in real time, such as temperature, light, air composition, soil conditions, and pests and diseases, and these data can be obtained without human intervention. For the greenhouse environment, these data are more sensitive, directly related to the growth of crops and the final benefits. Moreover, the previous greenhouse crop yields were obtained based on the planting experience of previous seasons, which generally varies greatly with changes in weather conditions or field management, and it is difficult to accurately predict the final crop yield.

SUMMARY OF THE INVENTION

The purpose of the present invention is to provide a smart agricultural management system based on image processing, so as to solve the above problems existing in the prior art.

In order to achieve the above purpose, the present invention provides a smart agricultural management system based on image processing, including: an image acquisition module, a real-time monitoring module, an image analysis module, a storage database, an environmental prediction module, an early warning prompt module, a comprehensive processing module;

The image acquisition module is used to collect crop growth images, preprocess the crop growth images, and transmit them to the image analysis module;

The real-time monitoring module is used to monitor changes in the growth environment of crops in real time, obtain images of the growth environment, and transmit the images of the growth environment to the image analysis module;

The image analysis module is used to analyze the crop growth image, output the analysis result to the early warning prompt module, and transmit the crop growth image to the storage database at the same time;

The storage database is used for storing the crop growth image and the crop growth environment image;

The environment prediction module is used to extract the crop growth environment image in the storage database, and perform environment prediction based on the growth environment image;

The early warning prompt module is configured to generate an early warning prompt signal based on the analysis result, and transmit the early warning prompt signal to the comprehensive processing module;

The integrated processing module is used for processing based on the early warning signal and the environmental prediction result, respectively.

Optionally, the preprocessing includes: identifying and marking the developmental features of the crops, the withered parts of the crops, and the parts where pests occur in the crop growth image.

Optionally, the storage data module is also used to store conventional development data of crops based on Internet big data technology;

The conventional development data of crops includes plant characteristics of different growth stages of crops and cycles of each growth stage.

Optionally, the image analysis module extracts the conventional crop development data in the storage database, and performs feature extraction on the crop growth image, and compares the extracted features with the conventional crop development data in the storage data module. , to determine the growth stage of crops;

The withered area is calculated based on the withered part of the crop, and a threshold is set according to the withered area. When the withered area reaches 30% of the crop plant area, a withered early warning signal is generated, and the withered early warning signal is transmitted to the early warning prompt module. The wilting treatment plan is obtained at the growth stage of the crop, and the wilting treatment plan is transmitted to the comprehensive treatment module.

Optionally, the image analysis module calculates the insect pest area based on the part of the crop where the insect damage occurs, and when the insect pest area reaches 10% of the crop plant area, 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, constructs a convolutional neural network feature comparison model, and inputs the extracted features into the convolutional neural network. The feature comparison model is compared, the best feature is output, the best feature is input into the convolutional neural network feature comparison model, the crop growth environment change curve within 30 days is obtained, and the crop growth environment change curve is based on the growth environment change curve. Prediction of changes in the growth environment.

Optionally, the environment prediction module calculates the average fluctuation interval of environmental changes based on the growth environment change curve, and when the average fluctuation interval is less than or equal to 2 days, generates an environmental change warning signal to the early warning prompt module, and sends the corresponding warning signal to the warning prompt module. The growth environment change curve is transmitted to the integrated processing module.

Optionally, the pre-warning and prompting module performs audio pre-warning of different frequencies according to different pre-warning signals, and performs pre-warning at a frequency of 5 times per second when acquiring an early-warning signal of withering; When the early warning signal of environmental change is obtained, the early warning of the frequency of 2 per second is carried out.

Optionally, the comprehensive treatment module performs watering, fertilizing, pruning, and lighting treatments on crops based on the withering treatment plan; spraying pesticides and natural enemy control treatments based on pest warning signals; , the total area of farmland to deal with.

The technical effect of the present invention is:

The invention realizes the effect of intelligently identifying the growth stage of crops, intelligently judging the growth state of crops and the occurrence of insect pests in the farmland by comparing the collected images with identification marks and feature extraction, and can carry out early warning prompts with different frequencies. The accurate prediction of changes in the growing environment of crops provides a guarantee for the green and safe production of crops.

Description of drawings

The accompanying drawings constituting a part of the present application are used to provide further understanding of the present application, and the schematic embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation of the present application. In the attached image:

FIG. 1 is a structural diagram of a smart agriculture management system based on image processing in an embodiment of the present invention.

Detailed ways

It should be noted that the embodiments in the present application and the features of the embodiments may be combined with each other in the case of no conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

It should be noted that the steps shown in the flowcharts of the accompanying drawings may be executed in a computer system, such as a set of computer-executable instructions, and, although a logical sequence is shown in the flowcharts, in some cases, Steps shown or described may be performed in an order different from that herein.

Example 1

As shown in FIG. 1 , this embodiment provides an image processing-based smart agriculture management system, including:

Image acquisition module, real-time monitoring module, image analysis module, storage database, environmental prediction module, early warning prompt module, and comprehensive processing module, including:

The image acquisition module is used to collect crop growth images, preprocess the crop growth images, and transmit them to the image analysis module; specifically, the preprocessing process includes: the developmental characteristics of the crops, the withered parts of the crops, the occurrence of the crops in the crop growth images Identify and mark the infested parts.

The real-time monitoring module is used to monitor the changes of the growth environment of crops in real time, obtain images of the growth environment, and transmit the images of the growth environment to the image analysis module;

The storage database is used to store crop growth images and crop growth environment images; the storage data module is also used to store conventional crop development data based on Internet big data technology.

The image analysis module is used to analyze the crop growth image, output the analysis result to the warning prompt module, and transmit the crop growth image to the storage database at the same time; specifically, the analysis process of the image analysis module includes:

Extract the routine development data of crops in the storage database (the routine development data of crops includes the plant characteristics of different growth stages of crops and the cycle of each growth stage), and perform feature extraction on the crop growth images, and compare the extracted features with the stored data module. The conventional development data of crops are compared to determine the growth stage of crops;

The withered area is calculated based on the withered part of the crop, and the threshold is set according to the withered area. When the withered area reaches 30% of the crop plant area, a withered warning signal is generated, and the withered warning signal is transmitted to the warning prompt module. At the same time, the withered area is obtained based on the growth stage of the crop Treatment plan, and transmit the wither treatment plan to the comprehensive treatment 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 crop plant area, a pest warning signal is generated, and the pest warning signal is transmitted to the warning prompt module.

The environment prediction module is used to extract the crop growth environment images in the storage database, and perform environment prediction based on the growth environment images; the specific prediction process includes:

The environment prediction module extracts the features of all crop growth environment images stored in the storage database within 30 days, constructs a convolutional neural network feature comparison model, and inputs the extracted features into the convolutional neural network feature comparison model for comparison, and the output is the highest. The best features are input into the convolutional neural network feature comparison model to obtain the crop growth environment change curve within 30 days, and predict the growth environment change of crops based on the growth environment change curve.

Calculate the average fluctuation interval of environmental changes based on the growth environment change curve. When the average fluctuation interval is less than or equal to 2 days, generate an environmental change early warning signal to the early warning prompt module, and transmit the corresponding growth environment change curve to the comprehensive processing module.

The early warning prompt module is used to generate the early warning prompt signal based on the analysis result, and transmit the early warning prompt signal to the comprehensive processing module;

In some embodiments, the early warning prompting module performs audio warnings of different frequencies according to different early warning signals. When acquiring the withering early warning signal, it performs an early warning of a frequency of 5 times per second; when acquiring an early warning signal of insect pests, it performs a frequency of 3 times per second. When the early warning signal of environmental change is obtained, the early warning of the frequency of 2 per second is carried out.

The integrated processing module is used for processing based on the acquired different signals;

In some embodiments, the comprehensive treatment module performs watering, fertilizing, pruning, and lighting treatments on crops based on the withering treatment plan; spraying pesticides and natural enemy control treatments based on pest warning signals; , the total area of farmland to deal with.

The above are only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited to this. Substitutions should be covered within the protection scope of this application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims (9)

1. an intelligent agricultural management system based on image processing, it is characterized in that, comprise successively connected: image acquisition module, real-time monitoring module, image analysis module, storage database, environment prediction module, early warning prompt module, comprehensive processing module; The image acquisition module is used to collect crop growth images, preprocess the crop growth images, and transmit them to the image analysis module; The real-time monitoring module is used to monitor changes in the growth environment of crops in real time, obtain images of the growth environment, and transmit the images of the growth environment to the image analysis module; The image analysis module is used to analyze the crop growth image, output the analysis result to the early warning prompt module, and transmit the crop growth image to the storage database at the same time; The storage database is used for storing the crop growth image and the crop growth environment image; The environment prediction module is used to extract the crop growth environment image in the storage database, and perform environment prediction based on the growth environment image; The early warning prompt module is configured to generate an early warning prompt signal based on the analysis result, and transmit the early warning prompt signal to the comprehensive processing module; The integrated processing module is used for processing based on the early warning signal and the environmental prediction result, respectively. 2 . The intelligent agricultural management system based on image processing according to claim 1 , wherein the preprocessing comprises: identifying the developmental characteristics of crops, the withered parts of crops, and the parts where pests occur in the image of crop growth. 2 . mark. 3. The intelligent agricultural management system based on image processing according to claim 1, wherein the storage data module is also used to store the conventional development data of crops based on the Internet big data technology; The conventional development data of crops includes plant characteristics of different growth stages of crops and cycles of each growth stage. 4. The intelligent agricultural management system based on image processing according to claim 1, wherein the image analysis module extracts the conventional development data of crops in the storage database, and performs feature extraction on the growth images of crops, Compare the features obtained by extraction with the conventional development data of crops in the storage data module to determine the growth stage of crops; The withered area is calculated based on the withered part of the crop, and a threshold is set according to the withered area. When the withered area reaches 30% of the crop plant area, a withered early warning signal is generated, and the withered early warning signal is transmitted to the early warning prompt module. The wilting treatment plan is obtained at the growth stage of the crop, and the wilting treatment plan is transmitted to the comprehensive treatment module. 5. The intelligent agricultural management system based on image processing according to claim 4, wherein the image analysis module calculates the pest area based on the position of the crop where the pest occurs, and when the pest area reaches 10% of the crop plant area , generate a pest warning signal, and transmit the pest warning signal to the warning prompt module. 6. The intelligent agriculture management system based on image processing according to claim 1, wherein the environment prediction module performs feature extraction on all crop growth environment images stored in the storage database within 30 days, and simultaneously constructs a convolutional neural network. Network feature comparison model, input the extracted features into the convolutional neural network feature comparison model for comparison, output the best features, and input the best features into the convolutional neural network feature comparison model to obtain The crop growth environment change curve within 30 days, based on the growth environment change curve, the crop growth environment change is predicted. 7. The intelligent agricultural management system based on image processing according to claim 6, wherein the environment prediction module calculates the average fluctuation interval of environmental changes based on the growth environment change curve, when the average fluctuation interval is less than or equal to At 2 days, an environmental change early warning signal is generated to the early warning prompt module, and the corresponding growth environment change curve is transmitted to the comprehensive processing module. 8. The intelligent agricultural management system based on image processing according to claim 1, wherein the early warning prompt module performs audio early warning of different frequencies according to different early warning signals, and when acquiring the withered early warning signal, performs 5 per second. Early warning of the second frequency; when the early warning signal of insect pests is obtained, the early warning of the frequency of 3 times per second is carried out; when the early warning signal of environmental change is obtained, the early warning of the frequency of 2 times per second is carried out. 9 . The intelligent agricultural management system based on image processing according to claim 1 , wherein the comprehensive processing module performs watering, fertilizing, pruning and lighting treatments on crops based on a withering treatment scheme; spraying based on insect pest warning signals. 10 . Pesticide and natural enemy prevention and treatment; control crop species, planting area, and total farmland area based on the growth environment change curve.

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