CN117743975A - Hillside cultivated land soil environment improvement method - Google Patents
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Abstract
The invention provides a hillside farmland soil environment improvement method, and relates to the technical field of agricultural management. Comprising the following steps: acquiring a crop growth state image; acquiring a soil temperature value and a soil humidity value; extracting growth state characteristics and locally strengthening the growth state images of the crops to obtain local characteristic strengthening crop growth state characteristic vectors; performing correlation analysis on soil temperature values and soil humidity values at a plurality of preset time points to obtain soil temperature-humidity time sequence feature vectors; and determining whether irrigation is performed or not based on the semantic interaction fusion characteristics between the local characteristic strengthening crop growth state characteristic vector and the soil temperature-humidity time sequence characteristic vector. The invention provides a method for scientifically improving the hillside cultivated land soil environment by analyzing the association change characteristics between the soil temperature and the humidity and judging whether the soil needs to be irrigated or not based on the semantic interaction association relation between the association change characteristics of the soil temperature and the humidity and the growth state of crops.
Description
Technical Field
The invention relates to the technical field of agricultural management, in particular to a method for improving soil environment of hillside farmland.
Background
Hillside cultivated land refers to farmland cultivated on a hillside land with a certain gradient, and is generally divided into terraced fields and hillside cultivated lands. Hillside cultivated land is an important component of agricultural production, but due to the influence of various factors such as topography, climate and the like, the soil environment often faces various problems such as soil erosion, fertility reduction, insufficient water and the like, and the problems directly affect the growth and yield of crops. Therefore, improving the soil environment of hillside cultivated land has important significance for improving the agricultural production benefit.
In the process of improving the soil environment of hillside farmland, reasonable irrigation is a key link. However, the traditional irrigation method often depends on manual experience judgment, lacks scientific basis, and is difficult to realize accurate irrigation. Meanwhile, due to complexity and variability of conditions such as terrain, climate and the like, the accuracy and efficiency of manual judgment are often limited. Therefore, an optimized method for improving the soil environment of hillside cultivated land is desired.
Disclosure of Invention
In order to solve the technical problems, the embodiment of the invention provides a method for improving the soil environment of hillside farmland. The technical scheme adopted by the invention is as follows:
a method for improving soil environment of hillside farmland, comprising:
acquiring a crop growth state image acquired by a camera;
acquiring soil temperature values and soil humidity values at a plurality of preset time points in a preset time period acquired by a temperature sensor and a humidity sensor;
extracting growth state characteristics and strengthening local characteristics of the crop growth state image to obtain a local characteristic strengthening crop growth state characteristic vector;
performing correlation analysis on the soil temperature values and the soil humidity values at a plurality of preset time points to obtain soil temperature-humidity time sequence feature vectors;
and determining whether irrigation is performed or not based on the semantic interaction fusion characteristics between the local characteristic strengthening crop growth state characteristic vector and the soil temperature-humidity time sequence characteristic vector.
In the above method for improving soil environment of hillside farmland, the method for extracting growth state features and strengthening local features of the crop growth state image to obtain a local feature strengthening crop growth state feature vector comprises: the crop growth state image passes through a crop growth state feature extractor based on a convolutional neural network model to obtain a crop growth state feature map; and carrying out local characteristic strengthening on the crop growth state characteristic diagram to obtain the local characteristic strengthening crop growth state characteristic vector.
In the above method for improving soil environment of hillside farmland, performing local feature enhancement on the crop growth state feature map to obtain a local feature enhanced crop growth state feature vector, including: and the crop growth state feature map passes through a local information efficient modeling module to obtain the local feature enhanced crop growth state feature vector.
In the above method for improving soil environment of hillside farmland, the method for obtaining the local feature-enhanced crop growth state feature vector by passing the crop growth state feature map through a local information efficient modeling module comprises: carrying out local information efficient modeling on the crop growth state feature map by using a local feature strengthening formula so as to obtain a local feature strengthening crop growth state feature vector; the local characteristic strengthening formula is as follows:
wherein F is output Enhancing crop growth status feature vectors for the local features, F input GAP represents pooling operation, reLU represents ReLU activation treatment, conv 1×1 (. Cndot.) means performing a convolution operation based on a 1X 1 convolution kernel, conv 3×3 (. Cndot.) means that a convolution operation based on a 3 x 3 convolution kernel is performed.
In the above method for improving soil environment of hillside farmland, performing correlation analysis on the soil temperature values and the soil humidity values at the plurality of predetermined time points to obtain a soil temperature-humidity time sequence feature vector, including: respectively arranging the soil temperature values and the soil humidity values of the plurality of preset time points into a soil temperature time sequence input vector and a soil humidity time sequence input vector according to the time dimension; performing time sequence association coding on the soil temperature time sequence input vector and the soil humidity time sequence input vector to obtain a soil temperature-soil humidity time sequence association matrix; and the soil temperature-soil humidity time sequence correlation matrix passes through a soil temperature-humidity time sequence feature extractor based on a convolutional neural network model so as to obtain the soil temperature-humidity time sequence feature vector.
In the above method for improving soil environment of hillside farmland, performing time-series association coding on the soil temperature time-series input vector and the soil humidity time-series input vector to obtain a soil temperature-soil humidity time-series association matrix, including: and calculating a sample covariance correlation matrix between the soil temperature time sequence input vector and the soil humidity time sequence input vector to obtain the soil temperature-soil humidity time sequence correlation matrix.
In the above hillside farmland soil environment improvement method, calculating a sample covariance correlation matrix between the soil temperature time sequence input vector and the soil humidity time sequence input vector to obtain the soil temperature-soil humidity time sequence correlation matrix, comprising: calculating a sample covariance correlation matrix between the soil temperature time sequence input vector and the soil humidity time sequence input vector by using a sample covariance correlation formula to obtain the soil temperature-soil humidity time sequence correlation matrix; the sample covariance correlation formula is as follows:
M cov =W T XX T W
wherein W is the soil temperature time sequence input vector, X is the soil humidity time sequence input vector, M cov For the soil temperature-soil humidity time sequence correlation matrix, T represents the transposition of the vector.
In the above hillside farmland soil environment improvement method, determining whether to irrigate based on the semantic interaction fusion feature between the local feature enhanced crop growth state feature vector and the soil temperature-humidity time sequence feature vector comprises: performing feature interaction on the local feature strengthening crop growth state feature vector and the soil temperature-humidity time sequence feature vector to obtain a crop growth state-crop growth condition semantic interaction fusion feature vector; optimizing the crop growth state-crop growth condition semantic interaction fusion feature vector to obtain an optimized crop growth state-crop growth condition semantic interaction fusion feature vector; and the optimized crop growth state-crop growth condition semantic interaction fusion feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether irrigation is performed or not.
In the above hillside farmland soil environment improvement method, performing feature interaction on the local feature enhanced crop growth state feature vector and the soil temperature-humidity time sequence feature vector to obtain a crop growth state-crop growth condition semantic interaction fusion feature vector, including: performing feature interaction on the local feature enhanced crop growth state feature vector and the soil temperature-humidity time sequence feature vector by using a projection feature interaction formula to obtain a crop growth state-crop growth condition semantic interaction fusion feature vector; the projection characteristic interaction formula is as follows:
wherein V is 1 Is the characteristic vector of the growth state of the local characteristic reinforced crop, V 2 The soil temperature-humidity time sequence characteristic vector, V f Is the crop growth state-crop growth condition semantic interaction fusion feature vector,/for>Representing a projection interaction process.
In the above hillside farmland soil environment improvement method, the method for obtaining classification results by passing the optimized crop growth state-crop growth condition semantic interaction fusion feature vector through a classifier, wherein the classification results are used for indicating whether irrigation is performed or not, and comprises the following steps: performing full-connection coding on the optimized crop growth state-crop growth condition semantic interaction fusion feature vector by using a full-connection layer of the classifier so as to obtain a full-connection coding feature vector; inputting the fully-connected coding feature vector into a Softmax classification function of the classifier to obtain probability values of the optimized crop growth state-crop growth condition semantic interaction fusion feature vector belonging to each classification label, wherein the classification labels comprise irrigation and non-irrigation; and determining the classification label corresponding to the maximum probability value as the classification result.
Compared with the prior art, the hillside cultivated land soil environment improvement method provided by the invention has the advantages that the growth state characteristic extraction and the local characteristic reinforcement are carried out on the crop growth state image, the soil temperature values and the soil humidity values at a plurality of preset time points are obtained, the correlation analysis is carried out, and whether irrigation is carried out or not is determined based on the semantic interaction fusion characteristic between the local characteristic reinforcement crop growth state characteristic vector and the soil temperature-humidity time sequence characteristic vector.
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The above and other objects, features and advantages of the present invention will become more apparent by describing embodiments of the present invention in more detail with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, and not constitute a limitation to the invention. In the drawings, like reference numerals generally refer to like parts or steps.
Fig. 1 is a flowchart of a method for improving a hillside farmland soil environment according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a method for improving soil environment of hillside farmland according to an embodiment of the present invention.
Fig. 3 is a flowchart of performing growth state feature extraction and local feature enhancement on the crop growth state image to obtain a local feature enhanced crop growth state feature vector according to an embodiment of the present invention.
Fig. 4 is a flowchart of performing correlation analysis on the soil temperature values and the soil humidity values at a plurality of predetermined time points to obtain a soil temperature-humidity time sequence feature vector according to an embodiment of the present invention.
FIG. 5 is a flow chart of determining whether to irrigate based on semantic interaction fusion features between the local feature enhanced crop growth status feature vector and the soil temperature-humidity time sequence feature vector in an embodiment of the invention.
FIG. 6 is a flow chart of the method for classifying the optimized crop growth state-crop growth condition semantic interaction fusion feature vector by a classifier to obtain a classification result in the embodiment of the invention.
Detailed Description
Hereinafter, exemplary embodiments according to the present invention will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present invention and not all embodiments of the present invention, and it should be understood that the present invention is not limited by the example embodiments described herein.
Fig. 1 is a flowchart of a method for improving a hillside farmland soil environment according to an embodiment of the present invention. Fig. 2 is a schematic diagram of a method for improving soil environment of hillside farmland according to an embodiment of the present invention. As shown in fig. 1 and fig. 2, the method for improving the soil environment of hillside farmland provided by the embodiment of the invention comprises the following steps: s110, acquiring a crop growth state image acquired by a camera; s120, acquiring soil temperature values and soil humidity values at a plurality of preset time points in a preset time period acquired by a temperature sensor and a humidity sensor; s130, carrying out growth state feature extraction and local feature enhancement on the crop growth state image to obtain a local feature enhanced crop growth state feature vector; s140, performing correlation analysis on the soil temperature values and the soil humidity values at the plurality of preset time points to obtain soil temperature-humidity time sequence feature vectors; and S150, determining whether irrigation is performed or not based on the semantic interaction fusion characteristics between the local characteristic strengthening crop growth state characteristic vector and the soil temperature-humidity time sequence characteristic vector.
In the above method for improving soil environment of hillside fields, in step S110, a crop growth state image acquired by a camera is acquired. The visual information of crops in different growth stages can be obtained by collecting the crop growth state images through the camera. By analyzing and processing the crop growth state image, the growth state characteristics of the crop, such as leaf color, morphology, density and the like, can be extracted. These features can reflect the growth status and health status of crops, and are of great significance for judging the growth status and soil demand of crops.
In the above-mentioned hillside farmland soil environment improvement method, the step S120 obtains soil temperature values and soil humidity values at a plurality of predetermined time points within a predetermined period of time acquired by the temperature sensor and the humidity sensor. It will be appreciated that soil temperature and humidity are one of the important environmental factors affecting crop growth, and are closely related to the state of growth and demand of the crop. By collecting the soil temperature and humidity data at a plurality of time points, the change trend and the periodicity rule of the soil temperature and humidity can be obtained, so that the dynamic change of the soil environment can be better known, and a basis is provided for the analysis of the soil environment and the irrigation of crops.
In the above method for improving soil environment of hillside farmland, in step S130, the growth state feature extraction and local feature enhancement are performed on the crop growth state image to obtain a local feature enhanced crop growth state feature vector. Fig. 3 is a flowchart of performing growth state feature extraction and local feature enhancement on the crop growth state image to obtain a local feature enhanced crop growth state feature vector according to an embodiment of the present invention. As shown in fig. 3, the step S130 includes: s131, passing the crop growth state image through a crop growth state feature extractor based on a convolutional neural network model to obtain a crop growth state feature map; and S132, carrying out local feature enhancement on the crop growth state feature map to obtain the local feature enhanced crop growth state feature vector.
In particular, convolutional neural networks (Convolutional Neural Network, CNN) are a deep learning model specifically for processing image data that is capable of automatically learning a feature representation in an image through a multi-layer convolution and pooling operation. In the process of extracting the growth state characteristics of crops, the convolutional neural network can effectively acquire local information in the image through the combination of the convolutional layer and the pooling layer, and can capture detail characteristics in the growth state image of the crops, such as important characteristics of the textures, the shapes and the like of the blades, so that the growth states of the crops in different growth stages are reflected. In contrast, conventional fully connected neural networks require flattening of an image into one-dimensional vectors when processing the image, and spatial structure information of the image may be lost. Moreover, the convolution layers in the convolution neural network have the characteristic of parameter sharing, namely the same convolution kernel shares weight at different positions of the image. Therefore, the parameter quantity of the model can be greatly reduced, the training efficiency of the model is improved, and the model has certain invariance to image transformation such as translation, rotation and the like. For the crop growth status image, the characteristic of such parameter sharing can better utilize the repeated pattern and local features in the image, so that the crop growth status feature extractor based on the convolutional neural network model can extract advanced feature representations useful for crop growth status judgment from the crop growth status image.
In the technical scheme of the invention, in order to further improve the expression capability of the characteristics, the understanding and description of the growth state of the crops are enhanced, and then the local characteristic strengthening treatment is carried out on the characteristic diagram of the growth state of the crops. In a specific example of the present invention, the method for obtaining the local feature-enhanced crop growth state feature vector by performing local feature enhancement on the crop growth state feature map is: and the crop growth state feature map passes through a local information efficient modeling module to obtain the local feature enhanced crop growth state feature vector. It will be appreciated that in the crop growth status feature map, the features of each location contain information relating to the status of the crop growth. However, the extent to which local features at different locations contribute to the crop growth status may be different. Some local regions may contain more important or representative features, while other regions may contain noise or irrelevant information. The analysis and modeling of the local area are carried out on the crop growth state characteristic diagram through the local information efficient modeling module, so that important local characteristics can be emphasized and focused, representative local characteristics are extracted, details and differences of the crop growth state are better represented, the discrimination capability and the expression capability of the crop growth state characteristics are improved, and more comprehensive information is provided for subsequent analysis and decision-making.
In a specific example, the step S132 includes: carrying out local information efficient modeling on the crop growth state feature map by using a local feature strengthening formula so as to obtain a local feature strengthening crop growth state feature vector; the local characteristic strengthening formula is as follows:
wherein F is output Enhancing crop growth status feature vectors for the local features, F input GAP represents pooling operation, reLU represents ReLU activation treatment, conv 1×1 (. Cndot.) means performing a convolution operation based on a 1X 1 convolution kernel, conv 3×3 (. Cndot.) representation of performing 3X 3 based convolutionConvolution operation of the kernel.
In the above method for improving soil environment of hillside farmland, in step S140, the soil temperature values and the soil humidity values at the predetermined time points are subjected to correlation analysis to obtain a soil temperature-humidity time sequence feature vector. It should be appreciated that soil temperature and humidity are important environmental factors for crop growth, with a direct impact on the growth and development of the crop. Through carrying out association analysis on the soil temperature and the humidity at a plurality of time points, the correlation between the soil temperature and the humidity can be excavated, namely whether the changes in time have certain synchronism or similarity, and then the potential association relation between the soil temperature and the humidity is found, for example, whether the humidity can be reduced when the temperature is increased or whether the temperature can be reduced when the humidity is increased or the like, so that more comprehensive soil condition evaluation information is provided, and the irrigation operation is more accurately judged.
Fig. 4 is a flowchart of performing correlation analysis on the soil temperature values and the soil humidity values at a plurality of predetermined time points to obtain a soil temperature-humidity time sequence feature vector according to an embodiment of the present invention. As shown in fig. 4, the step S140 includes: s141, arranging the soil temperature values and the soil humidity values of the plurality of preset time points into a soil temperature time sequence input vector and a soil humidity time sequence input vector according to a time dimension respectively; s142, performing time sequence association coding on the soil temperature time sequence input vector and the soil humidity time sequence input vector to obtain a soil temperature-soil humidity time sequence association matrix; and S143, the soil temperature-soil humidity time sequence correlation matrix passes through a soil temperature-humidity time sequence feature extractor based on a convolutional neural network model so as to obtain the soil temperature-humidity time sequence feature vector.
It should be appreciated that soil temperature and humidity vary over time. By arranging the soil temperature values and the soil humidity values at a plurality of preset time points according to the time dimension, a series of time sequence data can be formed and used for providing a history record of the soil temperature and humidity, reflecting the change trend and the periodicity rule of the soil temperature and humidity, and further capturing the dynamic characteristics of the soil temperature and humidity along with the time change.
In the technical scheme of the invention, considering that the soil temperature and the humidity are two interrelated variables, the two variables can influence each other to a certain extent. Therefore, the soil temperature time sequence input vector and the soil humidity time sequence input vector are subjected to time sequence association coding so as to extract the time sequence association characteristics of the soil temperature and the humidity. In a specific example of the present invention, the soil temperature time sequence input vector and the soil humidity time sequence input vector are subjected to time sequence association coding, so as to obtain a coding mode of a soil temperature-soil humidity time sequence association matrix, wherein the coding mode is as follows: and calculating a sample covariance correlation matrix between the soil temperature time sequence input vector and the soil humidity time sequence input vector to obtain the soil temperature-soil humidity time sequence correlation matrix. That is, the degree of correlation between the soil temperature data and the humidity data is measured by calculating a sample covariance correlation matrix between the soil temperature time series input vector and the soil humidity time series input vector. It should be appreciated that covariance is a statistic that measures the correlation between two variables and may reflect the linear relationship and trend of variation between variables. By calculating the sample covariance correlation matrix between the soil temperature time sequence input vector and the soil humidity time sequence input vector, the correlation information of the soil temperature and the humidity at different time points can be obtained, so that whether the variation trend of the soil temperature and the humidity is consistent, whether hysteresis effect or periodic variation exists or not and other information can be known, and a data basis is provided for further analysis and modeling.
In a specific example, the step S142 includes: calculating a sample covariance correlation matrix between the soil temperature time sequence input vector and the soil humidity time sequence input vector by using a sample covariance correlation formula; the sample covariance correlation formula is as follows:
M cov =W T XX T W
wherein W is the soil temperature time sequence input vector, X is the soil humidity time sequence input vector, M cov For the soil temperature-soil humidity time sequence correlation matrix, T represents the transposition of the vector.
Specifically, the S143, when determining the soil temperature-humidity timing characteristic vector, is obtained by passing the soil temperature-soil humidity timing correlation matrix through a soil temperature-humidity timing characteristic extractor based on a convolutional neural network model. It should be understood that the convolutional neural network can abstract the soil temperature-soil humidity time sequence correlation matrix through the combination of the convolutional layer and the pooling layer, automatically learn the correlation mode in the soil temperature-soil humidity time sequence correlation matrix, effectively extract the local correlation characteristics in the soil temperature-soil humidity time sequence correlation matrix, better capture the information such as dynamic change, trend, periodicity and the like between the soil temperature and the humidity, and obtain the soil temperature-humidity time sequence characteristic vector with more discrimination, thereby improving the accuracy of soil condition assessment.
In the above hillside farmland soil environment improvement method, step S150 is configured to determine whether to irrigate based on the semantic interaction fusion feature between the local feature enhanced crop growth state feature vector and the soil temperature-humidity time sequence feature vector. It should be appreciated that the local feature enhanced crop growth state feature vector reflects the crop's demand for moisture through the characteristics of the crop's growth rate, leaf status, etc. And the soil temperature-humidity time sequence feature vector reflects the temperature and humidity change trend and the association relation of the soil. Through carrying out semantic interaction fusion on the two, the water demand condition of crops and the actual condition of soil can be comprehensively considered, the response of the crops to the soil condition can be better understood, the connection between the water demand condition and the soil condition of the crops can be established, and whether irrigation is needed can be accurately and comprehensively estimated.
FIG. 5 is a flow chart of determining whether to irrigate based on semantic interaction fusion features between the local feature enhanced crop growth status feature vector and the soil temperature-humidity time sequence feature vector in an embodiment of the invention. As shown in fig. 5, the step S150 includes: s151, performing feature interaction on the local feature enhanced crop growth state feature vector and the soil temperature-humidity time sequence feature vector to obtain a crop growth state-crop growth condition semantic interaction fusion feature vector; s152, optimizing the crop growth state-crop growth condition semantic interaction fusion feature vector to obtain an optimized crop growth state-crop growth condition semantic interaction fusion feature vector; and S153, the optimized crop growth state-crop growth condition semantic interaction fusion feature vector is passed through a classifier to obtain a classification result, and the classification result is used for indicating whether irrigation is performed or not.
Specifically, in S151, feature interaction is a process of fusing and exchanging information between different features. In the technical scheme of the invention, the time sequence feature vector of the soil temperature and the humidity reflects the dynamic change condition of the soil, and the crop growth state feature vector describes the growth state of crops, such as plant height, leaf area, growth rate and the like. By combining the crop growth state feature vector and the soil temperature-humidity time sequence feature vector by using a projection layer, complex relations and interdependencies between the crop growth state and the crop growth condition can be captured, so that more comprehensive feature representation of the crop growth state-the crop growth condition is obtained, and the feature expression capability is further improved. That is, the crop growth state-crop growth condition semantic interaction fusion feature vector comprehensively considers the information of the crop growth state features and the soil temperature-humidity time sequence features, and performs feature interaction through the projection layer so as to fuse semantic interaction information between the crop growth state and the crop growth condition, so that the relationship between the crop growth state and the crop growth condition can be more accurately described, and more valuable feature representation is provided for subsequent analysis and modeling.
In a specific example, the step S151 is specifically implemented as follows: performing feature interaction on the local feature enhanced crop growth state feature vector and the soil temperature-humidity time sequence feature vector by using a projection feature interaction formula to obtain a crop growth state-crop growth condition semantic interaction fusion feature vector; the projection characteristic interaction formula is as follows:
wherein V is 1 Is the characteristic vector of the growth state of the local characteristic reinforced crop, V 2 The soil temperature-humidity time sequence characteristic vector, V f Is the crop growth state-crop growth condition semantic interaction fusion feature vector,/for>Representing a projection interaction process.
Specifically, the S152 may include, in specific implementation: performing fusion optimization on the local characteristic strengthening crop growth state characteristic vector and the soil temperature-humidity time sequence characteristic vector to obtain a correction characteristic vector; and fusing the correction feature vector with the crop growth state-crop growth condition semantic interaction fusion feature vector to obtain the optimized crop growth state-crop growth condition semantic interaction fusion feature vector. It should be understood that in the above technical solution, the crop growth state feature map represents image semantic features of a crop growth state image based on a convolutional neural network model. After the local information efficient modeling module is used, the local information efficient modeling module can focus local information in the crop growth state feature map through convolution kernels of different receptive fields, so that the local feature enhanced crop growth state feature vector can have higher feature expression resolution. The soil temperature-humidity time sequence characteristic vector expresses the local correlation mode characteristic of the temperature-humidity under the local time domain based on the convolution kernel of the time domain correlation of the soil temperature value and the soil humidity value. Considering the difference of the data source domain distribution of the soil temperature value, the soil humidity value and the crop growth state image and the influence of noise, when a projection layer is used for carrying out characteristic interaction on the local characteristic strengthening crop growth state characteristic vector and the soil temperature-humidity time sequence characteristic vector, the local characteristic strengthening crop growth state characteristic vector and the soil temperature-humidity time sequence characteristic vector have the amplified higher-order associated characteristic distribution difference due to the difference of the source domain distribution, and obvious interaction correspondence sparsity exists between the local characteristic strengthening crop growth state characteristic vector and the soil temperature-humidity time sequence characteristic vector so as to influence the expression effect of the crop growth state-crop growth condition semantic interaction fusion characteristic vector.
Based on the above, in the technical scheme of the invention, the local characteristic enhanced crop growth state characteristic vector and the soil temperature-humidity time sequence characteristic vector are fused and optimized by the following fusion optimization formula to obtain the correction characteristic vector; the fusion optimization formula is as follows:
wherein V is 1 Is the characteristic vector of the growth state of the local characteristic reinforced crop, V 2 Is the time sequence characteristic vector of the soil temperature and humidity, v 1i 、v 2i And v ci The local characteristic strengthening crop growth state characteristic vector V 1 The soil temperature-humidity time sequence characteristic vector V 2 And the eigenvalues of the correction eigenvectors,and->The squares of 1 norm and 2 norm of the feature vector are respectively expressed, and the local feature enhanced crop growth state feature vector V 1 And a soil temperature-humidity time sequence characteristic vector V 2 Have the same eigenvector length L and ε is a weight hyperparameter, log represents the logarithmic function value based on 2.
Here, in order to promote the consistency of the local feature-enhanced crop growth state feature vector and the distribution information representation of the soil temperature-humidity time sequence feature vector in the feature fusion scene, the absolute coordinates of the distribution regression are predefined by the feature scale and the structural representation of the feature vector to be fused as the special coordinatesThe standard of geometric registration is crossed by the sign values, so that the consistency of rigid grids of information distribution can be maintained, and the distance-based misalignment and incomplete overlapping between characteristic distribution information representations are punished by utilizing the thought of probability chamfering loss, so that the characteristic fusion of the consistency of the local characteristic strengthening crop growth state characteristic vector and the distribution information representation of the soil temperature-humidity time sequence characteristic vector is realized. Thus, the ratio of v ci The formed correction feature vector is fused with the crop growth state-crop growth condition semantic interactive fusion feature vector, so that the interactive fusion expression effect of the crop growth state-crop growth condition semantic interactive fusion feature vector on the local feature strengthening crop growth state feature vector and the soil temperature-humidity time sequence feature vector can be improved, and the accuracy of a classification result obtained by the classifier is improved.
Specifically, in S153, a classifier is used to learn the relationship between the crop growth status and the crop growth condition and irrigation demand, and classify the new sample according to the relationship. That is, the input optimized crop growth status-crop growth condition semantic interaction fusion feature vector is mapped to one of two categories, namely, irrigation is required and irrigation is not required by a classifier. Therefore, whether irrigation is carried out or not is decided based on the classification result, so that reasonable control of irrigation is realized, the soil environment of cultivated lands is improved, and the growth and the yield of crops are improved.
FIG. 6 is a flow chart of the method for classifying the optimized crop growth state-crop growth condition semantic interaction fusion feature vector by a classifier to obtain a classification result according to the embodiment of the invention. As shown in fig. 6, S153 includes: s1531, performing full connection coding on the optimized crop growth state-crop growth condition semantic interaction fusion feature vector by using a full connection layer of the classifier to obtain a full connection coding feature vector; s1532, inputting the fully-connected coding feature vector into a Softmax classification function of the classifier to obtain probability values of the optimized crop growth state-crop growth condition semantic interaction fusion feature vector belonging to various classification labels, wherein the classification labels comprise irrigation and no irrigation; and S1533, determining the classification label corresponding to the maximum probability value as the classification result.
In summary, the hillside farmland soil environment improvement method provided by the embodiment of the invention is explained, which utilizes an artificial intelligence technology based on deep learning to analyze the association change characteristics between soil temperature and humidity, and further judges whether the soil needs to be irrigated or not based on the semantic interaction association relation between the association change characteristics of soil temperature and humidity and the growth state of crops. Therefore, the method can realize scientific improvement of the hillside cultivated land soil environment, thereby improving the growth and yield of crops and promoting the sustainable development of agriculture.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method for improving the soil environment of hillside cultivated land, which is characterized by comprising the following steps:
acquiring a crop growth state image acquired by a camera;
acquiring soil temperature values and soil humidity values at a plurality of preset time points in a preset time period acquired by a temperature sensor and a humidity sensor;
extracting growth state characteristics and strengthening local characteristics of the crop growth state image to obtain a local characteristic strengthening crop growth state characteristic vector;
performing correlation analysis on the soil temperature values and the soil humidity values at a plurality of preset time points to obtain soil temperature-humidity time sequence feature vectors;
and determining whether irrigation is performed or not based on the semantic interaction fusion characteristics between the local characteristic strengthening crop growth state characteristic vector and the soil temperature-humidity time sequence characteristic vector.
2. The method for improving the soil environment of hillside fields according to claim 1, wherein the step of performing growth state feature extraction and local feature enhancement on the crop growth state image to obtain a local feature enhanced crop growth state feature vector comprises:
the crop growth state image passes through a crop growth state feature extractor based on a convolutional neural network model to obtain a crop growth state feature map;
and carrying out local characteristic strengthening on the crop growth state characteristic diagram to obtain the local characteristic strengthening crop growth state characteristic vector.
3. The method for improving the soil environment of hillside fields according to claim 2, wherein the step of performing local feature enhancement on the crop growth state feature map to obtain a local feature enhanced crop growth state feature vector comprises:
and the crop growth state feature map passes through a local information efficient modeling module to obtain the local feature enhanced crop growth state feature vector.
4. The method for improving the soil environment of hillside fields according to claim 3, wherein the step of passing the crop growth state feature map through a local information efficient modeling module to obtain the local feature-enhanced crop growth state feature vector comprises the steps of:
carrying out local information efficient modeling on the crop growth state feature map by using a local feature strengthening formula so as to obtain a local feature strengthening crop growth state feature vector; the local characteristic strengthening formula is as follows:
wherein F is output Enhancing crop growth status feature vectors for the local features, F input GAP represents pooling operation, reLU represents ReLU activation treatment, conv 1×1 (. Cndot.) representation of progressConv based on convolution operation of 1×1 convolution kernel 3×3 (. Cndot.) means that a convolution operation based on a 3 x 3 convolution kernel is performed.
5. The method for improving the soil environment of hillside fields according to claim 4, wherein the correlation analysis of the soil temperature values and the soil humidity values at the plurality of predetermined time points is performed to obtain a soil temperature-humidity time sequence feature vector, comprising:
respectively arranging the soil temperature values and the soil humidity values of the plurality of preset time points into a soil temperature time sequence input vector and a soil humidity time sequence input vector according to the time dimension;
performing time sequence association coding on the soil temperature time sequence input vector and the soil humidity time sequence input vector to obtain a soil temperature-soil humidity time sequence association matrix;
and the soil temperature-soil humidity time sequence correlation matrix passes through a soil temperature-humidity time sequence feature extractor based on a convolutional neural network model so as to obtain the soil temperature-humidity time sequence feature vector.
6. The method for improving the soil environment of hillside fields according to claim 5, wherein the step of performing time-series correlation encoding on the soil temperature time-series input vector and the soil humidity time-series input vector to obtain a soil temperature-soil humidity time-series correlation matrix comprises:
and calculating a sample covariance correlation matrix between the soil temperature time sequence input vector and the soil humidity time sequence input vector to obtain the soil temperature-soil humidity time sequence correlation matrix.
7. The method of improving a hillside farmland soil environment according to claim 6, wherein calculating a sample covariance correlation matrix between the soil temperature timing input vector and the soil humidity timing input vector to obtain the soil temperature-soil humidity timing correlation matrix comprises:
calculating a sample covariance correlation matrix between the soil temperature time sequence input vector and the soil humidity time sequence input vector by using a sample covariance correlation formula to obtain the soil temperature-soil humidity time sequence correlation matrix; the sample covariance correlation formula is as follows:
M cov =W T XX T W
wherein W is the soil temperature time sequence input vector, X is the soil humidity time sequence input vector, M cov For the soil temperature-soil humidity time sequence correlation matrix, T represents the transposition of the vector.
8. The method of improving a hillside field soil environment of claim 7, wherein determining whether to irrigate based on the semantic interaction fusion feature between the local feature enhanced crop growth status feature vector and the soil temperature-humidity time series feature vector comprises:
performing feature interaction on the local feature strengthening crop growth state feature vector and the soil temperature-humidity time sequence feature vector to obtain a crop growth state-crop growth condition semantic interaction fusion feature vector;
optimizing the crop growth state-crop growth condition semantic interaction fusion feature vector to obtain an optimized crop growth state-crop growth condition semantic interaction fusion feature vector;
and the optimized crop growth state-crop growth condition semantic interaction fusion feature vector is passed through a classifier to obtain a classification result, wherein the classification result is used for indicating whether irrigation is performed or not.
9. The method for improving the soil environment of hillside fields according to claim 8, wherein performing feature interaction on the local feature-enhanced crop growth state feature vector and the soil temperature-humidity time sequence feature vector to obtain a crop growth state-crop growth condition semantic interaction fusion feature vector comprises:
performing feature interaction on the local feature enhanced crop growth state feature vector and the soil temperature-humidity time sequence feature vector by using a projection feature interaction formula to obtain a crop growth state-crop growth condition semantic interaction fusion feature vector; the projection characteristic interaction formula is as follows:
wherein V is 1 Is the characteristic vector of the growth state of the local characteristic reinforced crop, V 2 The soil temperature-humidity time sequence characteristic vector, V f Is the crop growth state-crop growth condition semantic interaction fusion feature vector,/for>Representing a projection interaction process.
10. The method for improving the soil environment of hillside fields according to claim 9, wherein the step of passing the optimized crop growth state-crop growth condition semantic interaction fusion feature vector through a classifier to obtain a classification result, wherein the classification result is used for indicating whether irrigation is performed or not, comprises:
performing full-connection coding on the optimized crop growth state-crop growth condition semantic interaction fusion feature vector by using a full-connection layer of the classifier so as to obtain a full-connection coding feature vector;
inputting the fully-connected coding feature vector into a Softmax classification function of the classifier to obtain probability values of the optimized crop growth state-crop growth condition semantic interaction fusion feature vector belonging to each classification label, wherein the classification labels comprise irrigation and non-irrigation;
and determining the classification label corresponding to the maximum probability value as the classification result.
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