CN116524279A - Artificial intelligent image recognition crop growth condition analysis method for digital agriculture - Google Patents
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Abstract
The invention discloses an artificial intelligent image recognition crop growth analysis method for digital agriculture, which is characterized by comprising the following steps of: s1: collecting data; the method comprises the steps of obtaining crop photos or videos in different time periods in a photographing or shooting mode, and recording acquisition time, place and meteorological factor data; s2: preprocessing data; and denoising, image enhancement and other treatments are carried out on the acquired images or videos, so that the image quality is improved, meanwhile, the images are marked, and the type, position and growth state information of crops are marked. The invention relates to the field of digital agriculture, in particular to an artificial intelligent image recognition crop growth analysis method for digital agriculture. The invention aims to provide an artificial intelligent image recognition crop growth analysis method for digital agriculture, which is convenient for agricultural digitization.
Description
Technical Field
The invention relates to the field of digital agriculture, in particular to an artificial intelligent image recognition crop growth analysis method for digital agriculture.
Background
The digital agriculture is a novel agricultural mode for realizing intelligent planting management by taking an information technology as a support and taking data such as crop growth, diseases and the like as a basis and applying technologies such as big data analysis, cloud computing, artificial intelligence and the like. The artificial intelligent image recognition technology is one of key technologies in digital agriculture and can be used for crop growth analysis.
If the plant growth condition can be predicted and analyzed by comprehensively considering factors such as plant diameter, weather, environment and the like, the method is favorable for realizing agricultural digital production.
Disclosure of Invention
The invention aims to provide an artificial intelligent image recognition crop growth analysis method for digital agriculture, which is convenient for agricultural digitization.
The invention adopts the following technical scheme to realize the aim of the invention:
the artificial intelligent image recognition crop growth analysis method for the digital agriculture is characterized by comprising the following steps of:
s1: collecting data; the method comprises the steps of obtaining crop photos or videos in different time periods in a photographing or shooting mode, and recording acquisition time, place and meteorological factor data;
s2: preprocessing data; denoising, image enhancement and other treatments are carried out on the acquired images or videos, so that the image quality is improved, meanwhile, the images are marked, and the type, position and growth state information of crops are marked;
s3: extracting features; extracting features of the preprocessed image by using an artificial intelligence algorithm;
s4: training a classifier; using the extracted image features to train a classifier;
s5: analyzing growth vigor; judging the growth state of the crops by using the trained classifier, evaluating the growth condition and health condition of the crops, and simultaneously carrying out growth analysis on the crops by combining weather factor and environmental factor data;
s6: decision support; according to the growth analysis result, corresponding planting management suggestions are provided for different crop varieties and growth conditions, and farmers are helped to improve crop yield and quality.
As a further limitation of the present technical solution, common feature extraction methods include color histogram, texture feature, shape feature.
As further limitation of the technical scheme, the common classifier comprises a neural network, a support vector machine and a decision tree, and the accuracy of the classifier is improved by continuously adjusting the parameters of the classifier.
As a further limitation of the present technical solution, the specific flow of S3 is as follows:
diameter extraction based on deep learning is adopted to realize plant diameter data acquisition;
s31: preparing data; preparing a batch of plant pictures with known diameters, carrying out standardized treatment on the pictures, and marking the diameter of the plants;
s32: building a neural network, namely building a neural network model suitable for diameter extraction, and using a convolutional neural network model;
s33: training a network; inputting the prepared training data into a network for training, and adjusting network parameters through multiple rounds of iteration to enable the network to better extract the plant diameter;
s34: model testing, namely testing a network by using a test data set, and evaluating model performances such as accuracy and recall rate;
s35: and (3) model application, namely extracting the diameter of the new plant image by using the trained model.
As a further limitation of the present technical solution, the step S4 adopts a decision tree CART classification algorithm, where the CART classification algorithm selects a test attribute according to a coefficient of keni, and the smaller the value of the gini coefficient is, the better the dividing effect is, and if the sample set is set to be T, the gini coefficient value of T can be calculated by the following formula:
wherein:refers to the probability of the class j occurring in the sample set T;
dividing T into T 1 、T 2 Two subsets, the value of the gini coefficient for this subdivision can be calculated by:
wherein: s is the number of total samples in the sample set T;
s 1 to belong to subset T 1 The number of total samples in (a);
s 2 to belong to subset T 2 The number of total samples in the sample.
As a further limitation of the present technical solution, the growth analysis expression comprehensively considering plant diameter, weather and environmental factors is:
P＝D*(1+E)*(1+C) (3)
wherein: p represents a plant growth rate coefficient;
d represents the diameter of the plant;
e represents a meteorological factor;
c represents an environmental factor.
As a further limitation of the present technical solution, E and C set different weight values according to actual conditions, namely:
P＝D*(1+αE)*(1+βC) (4)
wherein: alpha and beta are weight coefficients, and the sum of the two is 1.
Compared with the prior art, the invention has the advantages and positive effects that:
the agricultural digitization applies modern technological means to various links of agricultural production, management, sales and the like, and realizes the development mode of deep integration of agricultural production and informatization, intellectualization, networking and digitization. The agricultural production efficiency is improved, the digital technology can provide a more refined, personalized and efficient agricultural production management scheme for agricultural production, and real-time monitoring and data analysis of factors such as farmlands, facilities, weather, soil and the like are realized, so that the agricultural production is more accurate, scientific and efficient. The digitization technology can reduce the waste of natural resources in agricultural production, optimize the agricultural production structure, and promote the agricultural production benefit, so that the agricultural production is more environment-friendly and sustainable. The application of the digitizing technology can bring more intelligent, efficient and sustainable agricultural ecological environment and promote the progress of agricultural modernization.
By comprehensively considering plant diameter, weather and environmental factors. Meteorological factors may include illumination, temperature, humidity, etc., and environmental factors may include soil nutrition, moisture, oxygen, etc. The setting of the weight value needs to be adjusted according to the actual situation, so that the formula can better reflect the actual situation.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
One embodiment of the present invention will be described in detail below with reference to the attached drawings, but it should be understood that the scope of the present invention is not limited by the embodiment.
The invention is characterized by comprising the following steps:
s1: collecting data; and obtaining crop photos or videos in different time periods in a photographing or shooting mode, and recording acquisition time, place and meteorological factor data.
S2: preprocessing data; and denoising, image enhancement and other treatments are carried out on the acquired images or videos, so that the image quality is improved, meanwhile, the images are marked, and the type, position and growth state information of crops are marked.
Data cleaning: and (3) denoising, thinning, filtering and the like are carried out on the collected original data, so that abnormal values and noise interference are eliminated, and effective data are reserved.
Data analysis: and carrying out trend analysis, periodicity analysis, feature extraction, pattern recognition and other analysis on the processed data through a statistical and analysis technology so as to obtain the plant growth state, variation trend and rule.
The processed data and results are visually displayed in a chart or report form mode, so that a decision maker can more intuitively know the plant growth state and the prediction trend, and better decide and manage the plant growth process.
S3: extracting features; and extracting the characteristics of the preprocessed image by using an artificial intelligence algorithm.
Common feature extraction methods include color histograms, texture features, shape features.
The specific flow of the S3 is as follows:
diameter extraction based on deep learning is adopted to realize plant diameter data acquisition;
s31: preparing data; preparing a batch of plant pictures with known diameters, carrying out standardized treatment on the pictures, and marking the diameter of the plants;
s32: building a neural network, namely building a neural network model suitable for diameter extraction, and using a convolutional neural network model;
s33: training a network; inputting the prepared training data into a network for training, and adjusting network parameters through multiple rounds of iteration to enable the network to better extract the plant diameter;
s34: model testing, namely testing a network by using a test data set, and evaluating model performances such as accuracy and recall rate;
s35: and (3) model application, namely extracting the diameter of the new plant image by using the trained model.
Diameter extraction based on watershed algorithms or diameter extraction based on edge detection may also be employed.
The watershed algorithm is a mathematical morphology-based algorithm that can divide an image into several sub-regions for detecting the contour and diameter of a plant. When the watershed algorithm is applied, plant images need to be preprocessed, such as binarization, denoising and the like, and then the diameter of the plant can be extracted by calculating the outline and the area of the plant.
Edge detection is an algorithm widely used in image processing and can extract contours in images. When the diameter extraction is realized by applying the edge detection algorithm, the plant image needs to be subjected to image enhancement and other treatments, and then the edge detection is performed by using the Canny and other algorithms. Finally, the diameter of the plant is obtained by calculating the width and position of the outline of the plant.
S4: training a classifier; the extracted image features are used to train a classifier.
The common classifier comprises a neural network, a support vector machine and a decision tree, and the accuracy of the classifier is improved by continuously adjusting the parameters of the classifier.
A batch of data sets already labeled with categories needs to be prepared for training. The size and quality of the data set will directly affect the effectiveness of the training.
And S4, a decision tree CART classification algorithm is adopted, wherein the CART classification algorithm selects test attributes according to the coefficient of the radix, the smaller the value of the gini coefficient is, the better the dividing effect is, and the sample set is set as T, and the gini coefficient value of the T can be calculated by the following formula:
wherein:refers to the probability of the class j occurring in the sample set T;
dividing T into T 1 、T 2 Two subsets, the value of the gini coefficient for this subdivision can be calculated by:
wherein: s is the number of total samples in the sample set T;
s 1 to belong to subset T 1 The number of total samples in (a);
s 2 is of the genusIn subset T 2 The number of total samples in the sample.
The CART algorithm has the advantages that: besides the characteristics of high accuracy, high efficiency, simple mode and the like of a general decision tree, the method has the characteristics of the method. For example, the CART algorithm has no requirement on probability distribution of the target variable and the predicted variable, so that results caused by different probability distribution of the target variable and the predicted variable are avoided; the CART algorithm can process the vacancy values, so that deviation caused by the vacancy values is avoided; the CART algorithm can process isolated leaf nodes, so that the influence of data with different attributes in the data set on further branches can be avoided; the CART algorithm uses binary branches, and can fully use all data in the data set so as to find out the structure of all trees; the rules derived from the model can be interpreted very intuitively, as is easier to understand than other models.
S5: analyzing growth vigor; judging the growth state of the crops by using the trained classifier, evaluating the growth condition and health condition of the crops, and simultaneously analyzing the growth vigor of the crops by combining weather factor and environmental factor data.
The growth analysis expression comprehensively considering plant diameter, weather and environmental factors is as follows:
P＝D*(1+E)*(1+C) (3)
wherein: p represents a plant growth rate coefficient;
d represents the diameter of the plant;
e represents a meteorological factor;
c represents an environmental factor.
E and C set different weight values according to actual conditions, namely:
P＝D*(1+αE)*(1+βC) (4)
wherein: alpha and beta are weight coefficients, and the sum of the two is 1.
Meteorological factors may include illumination, temperature, humidity, etc., and environmental factors may include soil nutrition, moisture, oxygen, etc. The setting of the weight value needs to be adjusted according to the actual situation, so that the formula can better reflect the actual situation.
S6: decision support; according to the growth analysis result, corresponding planting management suggestions are provided for different crop varieties and growth conditions, and farmers are helped to improve crop yield and quality.
The above disclosure is merely illustrative of specific embodiments of the present invention, but the present invention is not limited thereto, and any variations that can be considered by those skilled in the art should fall within the scope of the present invention.
Claims (7)
1. The artificial intelligent image recognition crop growth analysis method for the digital agriculture is characterized by comprising the following steps of:
s1: collecting data; the method comprises the steps of obtaining crop photos or videos in different time periods in a photographing or shooting mode, and recording acquisition time, place and meteorological factor data;
s2: preprocessing data; denoising, image enhancement and other treatments are carried out on the acquired images or videos, so that the image quality is improved, meanwhile, the images are marked, and the type, position and growth state information of crops are marked;
s3: extracting features; extracting features of the preprocessed image by using an artificial intelligence algorithm;
s4: training a classifier; using the extracted image features to train a classifier;
s5: analyzing growth vigor; judging the growth state of the crops by using the trained classifier, evaluating the growth condition and health condition of the crops, and simultaneously carrying out growth analysis on the crops by combining weather factor and environmental factor data;
s6: decision support; according to the growth analysis result, corresponding planting management suggestions are provided for different crop varieties and growth conditions, and farmers are helped to improve crop yield and quality.
2. The method for analyzing the growth condition of the crop identified by the artificial intelligent image of the digital agriculture according to claim 1, wherein the method comprises the following steps: common feature extraction methods include color histograms, texture features, shape features.
3. The method for analyzing the growth condition of the crop identified by the artificial intelligent image of the digital agriculture according to claim 1, wherein the method comprises the following steps: the common classifier comprises a neural network, a support vector machine and a decision tree, and the accuracy of the classifier is improved by continuously adjusting the parameters of the classifier.
4. The method for analyzing the growth condition of the crop identified by the artificial intelligent image of the digital agriculture according to claim 3, wherein the method comprises the following steps: the specific flow of the S3 is as follows:
diameter extraction based on deep learning is adopted to realize plant diameter data acquisition;
s31: preparing data; preparing a batch of plant pictures with known diameters, carrying out standardized treatment on the pictures, and marking the diameter of the plants;
s32: building a neural network, namely building a neural network model suitable for diameter extraction, and using a convolutional neural network model;
s33: training a network; inputting the prepared training data into a network for training, and adjusting network parameters through multiple rounds of iteration to enable the network to better extract the plant diameter;
s34: model testing, namely testing a network by using a test data set, and evaluating model performances such as accuracy and recall rate;
s35: and (3) model application, namely extracting the diameter of the new plant image by using the trained model.
5. The method for analyzing the growth condition of the crop identified by the artificial intelligent image of the digital agriculture according to claim 4, wherein the method comprises the following steps: and S4, a decision tree CART classification algorithm is adopted, wherein the CART classification algorithm selects test attributes according to the coefficient of the radix, the smaller the value of the gini coefficient is, the better the dividing effect is, and the sample set is set as T, and the gini coefficient value of the T can be calculated by the following formula:
wherein:refers to the probability of the class j occurring in the sample set T;
dividing T into T 1 、T 2 Two subsets, the value of the gini coefficient for this subdivision can be calculated by:
wherein: s is the number of total samples in the sample set T;
s 1 to belong to subset T 1 The number of total samples in (a);
s 2 to belong to subset T 2 The number of total samples in the sample.
6. The method for analyzing the growth condition of the crop identified by the artificial intelligent image of the digital agriculture according to claim 4, wherein the method comprises the following steps: the growth analysis expression comprehensively considering plant diameter, weather and environmental factors is as follows:
P＝D*(1+E)*(1+C) (3)
wherein: p represents a plant growth rate coefficient;
d represents the diameter of the plant;
e represents a meteorological factor;
c represents an environmental factor.
7. The method for analyzing the growth condition of the crop identified by the artificial intelligent image of the digital agriculture according to claim 6, wherein the method comprises the following steps: e and C set different weight values according to actual conditions, namely:
P＝D*(1+αE)*(1+βC) (4)
wherein: alpha and beta are weight coefficients, and the sum of the two is 1.
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