CN118261488A - Intelligent management system based on digital farm - Google Patents
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Abstract
The invention discloses an intelligent management system based on a digital farm, which relates to the technical field of digital farm management and solves the technical problems that in the prior art, only the environment of the farm is monitored and treated in real time, the environment is regulated and controlled without combining the growth condition of crops, the management efficiency of the farm is low and the management quality is low due to lack of pertinence; the information acquisition module acquires an image of a planting area; the evaluation module separates non-crop features from the planting area image to obtain a crop image to be detected; analyzing the pest and disease conditions and spectrum of the crops based on the crop images to be detected, detecting the height of the crops through a laser radar device, and further judging the growth conditions of the crops; the execution module takes corresponding measures aiming at the growth condition of crops; the method realizes the real-time monitoring of the growth condition of crops and discovers potential problems in time; and farm management efficiency and quality are improved.
Description
Technical Field
The invention belongs to the field of digital farm management, and particularly relates to an intelligent management system based on a digital farm.
Background
Agriculture is one of the basic industries of human society, and as population grows and economy develops, demand for agricultural products is increasing, and farm management is facing more and more challenges and opportunities.
The invention patent publication CN114527692a discloses a smart farm control system comprising: a perception layer, a network transmission layer, an application layer, wherein: the sensing layer is used for acquiring different sensing data in the farm; the network transmission layer is used for transmitting the sensing data of the sensing layer to the application layer; and the application layer is used for controlling the element equipment to adjust the environmental factors of the farm according to the data processing result of the sensing data. The invention carries out real-time monitoring based on an accurate agricultural sensor, carries out multi-level recording and display by utilizing modes of cloud computing, visualization and the like, assists analysis, and can complete agricultural production and management by linkage of instructions and various control equipment, thereby reducing manpower and material resource use and optimizing water, electricity and other resource use.
In the prior art, the environment of the farm is monitored and treated in real time, the environment is regulated and controlled without combining the growth condition of crops, and the management efficiency of the farm is low and the management quality is low due to the lack of pertinence; therefore, the invention provides an intelligent management system based on a digital farm, so as to solve the technical problems.
Disclosure of Invention
The present invention aims to solve at least one of the technical problems existing in the prior art; therefore, the invention provides an intelligent management system based on a digital farm, which is used for solving the technical problems that in the prior art, only the environment of the farm is monitored and treated in real time, the environment is regulated and controlled without combining the growth condition of crops, the management efficiency of the farm is low and the management quality is low due to lack of pertinence.
To achieve the above object, a first aspect of the present invention provides an intelligent management system based on a digital farm, which includes an evaluation module, and an information acquisition module and an execution module connected with the evaluation module;
and the information acquisition module is used for: collecting a plurality of planting area images through an image collecting device; the planting area image is an image in the overlooking direction;
And an evaluation module: separating non-crop features from the plant subregion image to obtain a crop image to be detected; inputting the crop image to be detected into a pest identification model, and identifying whether pests exist in crops; if the crop is damaged by diseases and insects; if not, analyzing the growth information of the crops; wherein the plant disease and insect pest identification model comprises a VGG16 model or a AlexNet model; wherein the growth information includes the height and spectrum of the crop;
analyzing the spectrum of the crops based on the images of the crops to be detected, detecting the height of the crops by a laser radar device, and judging whether the crops reach the expected growth standard or not; if yes, the crops grow normally; if not, the crop grows abnormally, and the abnormal grades are divided; wherein the growth information includes the height and spectrum of the crop; growth anomalies include spectral anomalies and height anomalies;
the execution module: based on the abnormal grade, corresponding measures are taken to ensure the growth of crops.
Preferably, the separating the non-crop features from the plant sub-area image to obtain the crop image to be detected includes:
Collecting a plurality of original planting area images in advance; inputting the original planting area image into a CNN neural network model for image feature separation training; the CNN neural network model obtained through training is marked as a CNN neural network separation model; wherein the non-crop signature is 0 and the crop signature is 1;
Inputting the obtained planting area image into a CNN neural network separation model; and separating out the non-crop features through a CNN neural network separation model to obtain a crop image to be detected.
The invention separates out crop images to be detected by using a CNN neural network model; the CNN neural network model can effectively capture local features in the image through rolling and pooling operations, so that the CNN neural network model can effectively extract features of the image, and the image content is better understood; and the CNN neural network model has certain robustness to noise, deformation, shielding and other interference in the image, and can better cope with complex image processing in an actual scene.
Preferably, the analyzing the spectrum of the crop based on the image of the crop to be detected includes:
Carrying out pixel gray scale normalization treatment on the crop image to be detected, extracting pixel gray scale values of all pixel points, and carrying out statistical averaging to obtain a gray scale average value of crops in a planting area;
Calculating the difference between the gray average value and the standard gray average value to obtain a gray difference; judging whether the gray level difference is smaller than a gray level difference threshold value or not; if yes, the spectrum of the crop to be detected is normal; if not, detecting that the spectrum of the crop to be detected is abnormal; wherein the growth stage comprises a nutrition stage, a reproduction stage and a maturation stage; the standard gray average value is the standard value which is reached by crops in the corresponding growth stage.
The gray scale normalization process converts the image into the gray scale image, so that the complexity of subsequent image processing and analysis is greatly simplified; by calculating the gray average value, whether the spectrum of the crop to be detected is normal or not can be rapidly judged; if the gray average value is lower than the set threshold value, the spectrum of the crops is normal; otherwise, the spectrum is abnormal; the method is suitable for rapid detection of large-scale crop images, and reduces interference of human factors.
Preferably, the detecting the height of the crop by the laser radar device includes:
step S1: scanning each side surface of the planting area by using laser radar equipment to acquire point cloud data of three-dimensional information of the side surface of the planting area;
step S2: identifying crops through a classification algorithm, and extracting point cloud data related to the crops;
Step S3: carrying out statistical averaging on the crop point cloud data of each side surface to obtain the crop height of each side surface; and carrying out average treatment on the heights of the crops on each side surface to obtain the heights of the crops in the planting area.
According to the invention, three-dimensional information of crops can be obtained from multiple angles by scanning each side surface of a planting area, so that the shape and height distribution of the crops can be more comprehensively known, the calculation deviation of the heights caused by the data error of a single side surface can be reduced by carrying out statistical average on the point cloud data of the multiple side surfaces, and the accuracy of the whole height is improved; even if the data of a certain side face is abnormal or missing, the data can be compensated to a certain extent through the average value processing of the data of multiple side faces, so that the robustness of the whole flow is enhanced.
Preferably, judging whether the height of the crops is smaller than a standard height threshold value; if yes, the height of the crops is normal; if not, the height of the crops is abnormal.
Preferably, the abnormal crop growth and the abnormal classification are performed, including:
When the spectrum and the height of the crops are abnormal, the abnormal grade of the crops is a first grade;
When the crop is abnormal in spectrum or high, the abnormal grade of the crop is second grade.
Preferably, the step of taking corresponding measures to ensure the growth of crops based on the abnormal grade comprises the following steps:
when the crop is damaged by diseases and insects, comprehensive prevention measures are taken, including physical prevention, biological prevention and chemical prevention and control, so that the damage of the diseases and insects to the crop is reduced;
when the abnormal grade is the first grade or the second grade, if the abnormal grade is caused by operational environmental factors, biotechnological means such as genetic engineering and microbial fertilizers are utilized to improve the stress resistance and the adaptability of crops;
When the abnormal grade is the first grade or the second grade, if the abnormal grade is caused by inoperable environmental factors, crop varieties with strong stress tolerance are introduced, so that the abnormal grade is better adapted to severe environments, and the occurrence of abnormal growth is reduced.
It should be noted that the operational environmental factors include humidity and soil factors, and the non-operational environmental factors include illumination intensity and temperature.
Compared with the prior art, the invention has the beneficial effects that:
1. the method collects the image of the planting area during each growth stage, and further obtains the image of the crop to be detected; analyzing the spectrum condition of crops by calculating the average value of gray values of the crop images to be detected; detecting the height of crops by a laser radar device; the invention provides more comprehensive crop growth condition assessment for agricultural production by simultaneously analyzing the spectrum and the height of crops; whether the spectrum is normal or not reflects the health state of crops, and the height directly reflects the growth speed and the intensity of the crops; in addition, image acquisition and analysis are carried out at different growth stages, once the spectrum or height abnormality of crops is detected, farmers or agricultural managers can quickly take measures to prevent the problem from further worsening;
2. The information acquisition module acquires a plurality of planting area images through the image acquisition equipment; the evaluation module separates non-crop features from the planting area image to obtain a crop image to be detected; analyzing whether the crop has a disease and insect hazard; if yes, the execution module performs comprehensive prevention measures; if not, analyzing the growth information of the crops, and judging whether the crops reach the expected growth standard; if yes, the crops grow normally; if not, the crop growth is abnormal; the comprehensive monitoring of the growth condition of crops is realized, and potential problems are found in time; compared with the traditional agricultural management, the method reduces manual intervention, reduces labor cost and improves the efficiency and quality of agricultural production.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a system flow of the present invention;
FIG. 2 is a flow chart of the method of the present invention;
FIG. 3 is a flow chart of a spectroscopic analysis method of each growth stage of the crop of the present invention;
Fig. 4 is a flow chart of a method for highly analyzing various growth stages of crops according to the present invention.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is obvious that the described embodiments are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-2, an embodiment of a first aspect of the present invention provides a digital farm-based intelligent management system, which includes an evaluation module, and an information acquisition module and an execution module connected with the evaluation module;
The information acquisition module acquires a plurality of planting area images through the image acquisition equipment;
The planting area image is an image in the overlooking direction; an image of the planting area in the overlooking direction is acquired by the unmanned aerial vehicle above the planting area;
separating out non-crop features from the planting area image to obtain a crop image to be detected, wherein the specific separation process is as follows:
Collecting a plurality of original planting area images in advance; inputting the original planting area image into a CNN neural network model for image feature separation training; the CNN neural network model obtained through training is marked as a CNN neural network separation model; wherein the non-crop signature is 0 and the crop signature is 1;
Inputting the obtained planting area image into a CNN neural network separation model; and separating out the non-crop features through a CNN neural network separation model to obtain a crop image to be detected.
For example: assuming that various crops are planted in a certain farm, acquiring an image of a planting area of the farm, and separating a corn image from the image of the planting area;
From a plurality of pre-collected corn images, the images comprising corn and non-corn images, are input into a CNN neural network model for training, and the model has the task of identifying the characteristics of the corn (such as spectrum, texture and the like of the corn) and the non-corn characteristics (such as grasslands, roads, buildings and the like) in the images; in the training process, marking the corn characteristic in the planting area image as 1; marked 0 for other non-corn features; after repeated iterative training, a CNN neural network separation model is obtained, and the model can automatically separate out non-corn features in the image, and only corn feature parts are reserved.
Inputting the crop image to be detected into a pest identification model, and identifying whether pests exist in crops; if the crop is damaged by diseases and insects; if not, analyzing the growth information of the crops; wherein the plant disease and insect pest identification model comprises a VGG16 model or a AlexNet model;
The VGG16 model is a deep convolutional neural network model, the VGG16 model explores the relation between the depth and the performance of the convolutional neural network, and a 16-layer deep convolutional neural network is successfully constructed by repeatedly stacking 3×3 small convolutional kernels and 2×2 maximum pooling layers; in terms of pest identification, the VGG16 model can learn features of the pest image through training, and then classify and identify using the features. In the training process, a large number of plant diseases and insect pests images are usually required to be used as a training data set, and parameters of the model are optimized through a back propagation algorithm and a gradient descent method, so that the model can accurately identify and classify plant diseases and insect pests.
AlexNet is an image recognition model based on Convolutional Neural Network (CNN) for recognizing crop diseases and insect pests; the AlexNet model consists of an 8-layer convolutional neural network, which includes 5 convolutional layers and 3 fully-connected layers. The model adopts the techniques of ReLU activation function, dropout regularization technology, local Response Normalization (LRN), data enhancement and the like, so that the generalization capability and the robustness of the model are effectively improved;
In the aspect of pest identification, alexNet models can be used for classifying and identifying new pest images by training and learning the characteristics of the pest images. Specifically, a large number of pest images can be collected as a training data set, training is performed by using AlexNet models, features of the pest images are extracted, and then classification and identification are performed by using the features.
Referring to fig. 3, based on the image of the crop to be detected, analyzing the spectrum of the crop, and detecting the height of the crop by the laser radar device, determining whether the crop meets the expected growth standard; if yes, the crops grow normally; if not, the crop grows abnormally, and the abnormal grades are divided; wherein the growth information includes the height and spectrum of the crop; growth anomalies include spectral anomalies and height anomalies;
based on the overlooking direction to-be-detected crop image, analyzing the spectrum of the crop, comprising:
Carrying out pixel gray scale normalization treatment on the crop image to be detected, extracting pixel gray scale values of all pixel points, and carrying out statistical averaging to obtain a gray scale average value of crops in a planting area;
Calculating the difference between the gray average value and the standard gray average value to obtain a gray difference; judging whether the gray level difference is smaller than a gray level difference threshold value or not; if yes, the spectrum of the crop to be detected is normal; if not, detecting that the spectrum of the crop to be detected is abnormal; wherein the growth stage comprises a nutrition stage, a reproduction stage and a maturation stage; the standard gray average value is the standard value which is reached by crops in the corresponding growth stage.
Referring to fig. 4, the detection of the height of crops by the laser radar device comprises the following processing steps:
step S1: scanning each side surface of the planting area by using laser radar equipment to acquire point cloud data of three-dimensional information of the side surface of the planting area;
step S2: identifying crops through a classification algorithm, and extracting point cloud data related to the crops;
Step S3: carrying out statistical averaging on the crop point cloud data of each side surface to obtain the crop height of each side surface; and carrying out average treatment on the heights of the crops on each side surface to obtain the heights of the crops in the planting area.
Judging whether the height of the crops is smaller than a standard height threshold value or not; if yes, the height of the crops is normal; if not, the height of the crops is abnormal.
When the crop has spectrum abnormality and height abnormality at the same time, the abnormal grade of the crop is first grade; when the crop is abnormal in spectrum or high, the abnormal grade of the crop is second grade.
The execution module takes corresponding measures to ensure the growth of crops based on the abnormal grade, and the method comprises the following steps:
when the crop is damaged by diseases and insects, comprehensive prevention measures are taken, including physical prevention, biological prevention and chemical prevention and control, so that the damage of the diseases and insects to the crop is reduced;
When the abnormal grade is primary or secondary and is caused by operational environmental factors, biotechnological means such as genetic engineering and microbial fertilizers are utilized to improve the stress resistance and adaptability of crops;
when the abnormal grade is primary or secondary and is caused by inoperable environmental factors, crop varieties with strong stress resistance are introduced, so that the crop varieties are better adapted to severe environments and the occurrence of abnormal growth is reduced.
It should be noted that the operational environmental factors include humidity and soil factors, and the non-operational environmental factors include illumination intensity and temperature.
The partial data in the formula is obtained by removing dimension and taking the numerical value for calculation, and the formula is obtained by simulating a large amount of acquired data through software and is closest to the real situation; the preset parameters and the preset threshold values in the formula are set by those skilled in the art according to actual conditions or are obtained through mass data simulation.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (7)
1. The intelligent management system based on the digital farm is characterized by comprising an evaluation module, an information acquisition module and an execution module, wherein the information acquisition module and the execution module are connected with the evaluation module;
and the information acquisition module is used for: collecting a plurality of planting area images through an image collecting device; the planting area image is an image in the overlooking direction;
And an evaluation module: separating non-crop features from the plant subregion image to obtain a crop image to be detected; inputting the crop image to be detected into a pest identification model, and identifying whether pests exist in crops; if the crop is damaged by diseases and insects; if not, analyzing the growth information of the crops; wherein the plant disease and insect pest identification model comprises a VGG16 model or a AlexNet model; wherein the growth information includes the height and spectrum of the crop;
analyzing the spectrum of the crops based on the images of the crops to be detected, detecting the height of the crops by a laser radar device, and judging whether the crops reach the expected growth standard or not; if yes, the crops grow normally; if not, the crop grows abnormally, and the abnormal grades are divided; wherein the growth information includes the height and spectrum of the crop; growth anomalies include spectral anomalies and height anomalies;
the execution module: based on the abnormal grade, corresponding measures are taken to ensure the growth of crops.
2. The intelligent management system based on a digital farm according to claim 1, wherein the separating the non-crop features from the plant sub-area image to obtain the crop image to be detected comprises:
Collecting a plurality of original planting area images in advance; inputting the original planting area image into a CNN neural network model for image feature separation training; the CNN neural network model obtained through training is marked as a CNN neural network separation model; wherein the non-crop signature is 0 and the crop signature is 1;
Inputting the obtained planting area image into a CNN neural network separation model; and separating out the non-crop features through a CNN neural network separation model to obtain a crop image to be detected.
3. The intelligent management system based on a digital farm according to claim 1, wherein the analyzing the spectrum of the crop based on the image of the crop to be detected comprises:
Carrying out pixel gray scale normalization treatment on the crop image to be detected, extracting pixel gray scale values of all pixel points, and carrying out statistical averaging to obtain a gray scale average value of crops in a planting area;
Calculating the difference between the gray average value and the standard gray average value to obtain a gray difference; judging whether the gray level difference is smaller than a gray level difference threshold value or not; if yes, the spectrum of the crop to be detected is normal; if not, detecting that the spectrum of the crop to be detected is abnormal; wherein the growth stage comprises a nutrition stage, a reproduction stage and a maturation stage; the standard gray average value is the standard value which is reached by crops in the corresponding growth stage.
4. The intelligent management system based on a digital farm according to claim 1, wherein the detection of the height of crops by the lidar device comprises:
step S1: scanning each side surface of the planting area by using laser radar equipment to acquire point cloud data of three-dimensional information of the side surface of the planting area;
step S2: identifying crops through a classification algorithm, and extracting point cloud data related to the crops;
Step S3: carrying out statistical averaging on the crop point cloud data of each side surface to obtain the crop height of each side surface; and carrying out average treatment on the heights of the crops on each side surface to obtain the heights of the crops in the planting area.
5. The intelligent management system based on a digital farm according to claim 4, wherein it is determined whether the height of the crop is less than a standard height threshold; if yes, the height of the crops is normal; if not, the height of the crops is abnormal.
6. The intelligent management system based on a digital farm according to claim 1, wherein the abnormal crop growth and the abnormal classification comprises:
When the spectrum and the height of the crops are abnormal, the abnormal grade of the crops is a first grade;
When the crop is abnormal in spectrum or high, the abnormal grade of the crop is second grade.
7. The intelligent management system based on a digital farm according to claim 1, wherein the taking of corresponding measures to guarantee the growth of crops based on the anomaly level comprises:
when the crop is damaged by diseases and insects, comprehensive prevention measures are taken, including physical prevention, biological prevention and chemical prevention and control, so that the damage of the diseases and insects to the crop is reduced;
when the abnormal grade is the first grade or the second grade, if the abnormal grade is caused by operational environmental factors, biotechnological means such as genetic engineering and microbial fertilizers are utilized to improve the stress resistance and the adaptability of crops;
When the abnormal grade is the first grade or the second grade, if the abnormal grade is caused by inoperable environmental factors, crop varieties with strong stress tolerance are introduced, so that the abnormal grade is better adapted to severe environments, and the occurrence of abnormal growth is reduced.
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