Recommendation method for planting and breeding cycle resource optimization

Abstract

The invention relates to the technical field of agricultural planting and breeding, and discloses a planting and breeding cycle resource optimization recommendation method comprising the following steps: collecting corn growth image data, and extracting growth characteristics of each corn; extracting representation data of all corns in the feature data set, performing Gaussian kernel density data processing, and determining representative data; comparing each representation data with the representative data to establish a first set, a second set and a third set; comparing the representative data with big data to determine a single-plant predicted yield, adjusting the single-plant predicted yield according to the first set, the second set and the third set to obtain a final single-plant predicted yield, and determining a yield sum; and comparing the sum of the yields with an initial value to judge whether the scale of the bred livestock and poultry is adjusted or not, and determining a recommended scale. According to the method, the growth condition of the corn is evaluated, the yield is accurately predicted, and a reasonable breeding scale is recommended based on the method.

Description

Technical Field

[0001] The present invention relates to the field of agricultural breeding technology, and in particular to a recommended method for optimizing breeding cycle resources. Background Art

[0002] Resource recycling and ecological agriculture have become the key directions for sustainable development. The resource utilization methods of traditional agriculture and animal husbandry are usually singular, and they fail to fully consider the synergy and complementarity between various links, resulting in resource waste and increased pressure on the ecological environment.

[0003] At present, CN115281032A proposes a method of combining feeding mulberry with diversified ecological cycle planting and breeding, and proposes a method of breeding livestock and poultry and / or cultivating edible fungi with feeding mulberry, breeding earthworms with livestock and poultry manure and / or edible fungi chaff, and planting feeding mulberry with earthworm breeding waste. According to the above scheme, a scheme of making silage from corn, feeding livestock and poultry with silage, breeding earthworms with livestock and poultry manure, and planting corn with earthworm breeding waste is formulated. However, in this scheme, the breeding scale at each stage needs to be based on the experience of workers, and the growth process of plants is affected by various environmental factors. Once the evaluation is wrong, subsequent breeding will be affected. In practical applications, the low accuracy is not conducive to the implementation of subsequent schemes.

[0004] Therefore, it is necessary to design a recommended method for optimizing crop-livestock cycle resources to solve the problems existing in current technology. Summary of the invention

[0005] In view of this, the present invention proposes a recommendation method for optimizing crop-livestock cycle resources, aiming to solve the current problems of being unable to accurately reflect the growth dynamics of forage plants, low prediction accuracy and poor reliability.

[0006] In one aspect, the present invention proposes a recommended method for optimizing crop-breeding cycle resources, comprising:

[0007] Collect corn growth image data, process all growth image data, extract the growth characteristics of each corn, and establish a feature data set;

[0008] Extracting all corn characterization data from the feature data set, the characterization data including plant height or leaf area, performing Gaussian kernel density data processing on the characterization data to determine representative data;

[0009] Comparing the characteristic data of each corn with the representative data to establish a first set, a second set and a third set;

[0010] Comparing the representative data with the big data to determine the predicted yield of a single plant, and adjusting the predicted yield of a single plant according to the first set, the second set, and the third set to obtain a final predicted yield of a single plant, and determining the total yield according to the final predicted yield of a single plant;

[0011] The total output is compared with the initial value, and based on the comparison result, it is determined whether the scale of livestock and poultry farming should be adjusted to determine the recommended scale.

[0012] Furthermore, performing Gaussian kernel density data processing on the characterization data to determine representative data includes:

[0013] The same characterization data of all corns are integrated to form a first analysis data set, and kernel density estimation is performed on the data in the first analysis data set to determine that the data with the highest probability is the representative data.

[0014] Furthermore, when performing kernel density estimation on the data in the first analysis data set, the Gaussian kernel density function is:

[0015]

[0016] Where n is the number of data samples in the first analysis data set, h represents the smoothing bandwidth, and s i represents the i-th data point in the first analysis data set, and x represents any variable in the overall data.

[0017] Furthermore, the smoothing bandwidth is calculated by the following formula:

[0018]

[0019] Wherein, h represents the smoothing bandwidth, σ represents the standard deviation of the data in the first analysis data set, β1 represents the data skewness, β2 represents the data kurtosis, n represents the number of data samples in the first analysis data set, and k represents the adjustment coefficient;

[0020] The data skewness is calculated by the following formula:

[0021]

[0022] The data kurtosis is calculated by the following formula:

[0023]

[0024] Where n represents the number of data samples in the first analysis data set, s i represents the i-th data point in the first analysis data set, represents the mean of the data in the first analysis data set, and σ represents the standard deviation of the data in the first analysis data set.

[0025] Furthermore, when the characterization data of each corn is compared with the representative data to establish the first set, the second set and the third set, it includes:

[0026] Classify the corn corresponding to the characterization data equal to the representative data into the first set;

[0027] The corn corresponding to the characterization data being greater than the representative data is classified into the second set;

[0028] The corn corresponding to the characterization data being smaller than the representative data is classified into the third set.

[0029] Further, when the predicted yield per plant is adjusted according to the first set, the second set and the third set to obtain a final predicted yield per plant, the method includes:

[0030]

[0031] Among them, Cz represents the final predicted yield of a single plant, m is the number of characterization data in the second set, Cp is the pth characterization data in the second set, C0 is the predicted yield of a single plant, n is the number of characterization data in the third set, and Cq is the qth characterization data in the third set.

[0032] Furthermore, when judging whether to adjust the scale of livestock and poultry breeding based on the comparison results, it includes:

[0033] When the difference between the total output and the initial value is less than or equal to a% of the initial value, it is determined that the scale of the livestock and poultry breeding is not adjusted, and the initial scale is used as the recommended scale;

[0034] When the difference between the total output and the initial value is greater than a% of the initial value, it is determined that the scale of the livestock and poultry farming should be adjusted to determine the recommended scale.

[0035] Furthermore, when determining to adjust the scale of the livestock, it includes:

[0036] The output difference is obtained according to the output sum and the initial value, and the adjustment coefficient is determined according to the output difference to adjust the scale of the livestock and poultry breeding, and the adjustment coefficient is proportional to the output difference.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows: by processing corn growth image data, a feature data set is established to determine representative data, and accurate growth representation is provided for each corn plant. Dynamic monitoring of corn growth is achieved, and the single-plant yield of each corn plant is predicted by comparison with big data, and the accuracy of the prediction is further improved by comparison and adjustment of multiple data sets. By comparing with the initial yield value, it is timely determined whether the scale of livestock and poultry breeding needs to be adjusted, and accurate resource allocation and optimization are achieved, thereby improving the synergy between breeding and agricultural planting and reducing resource waste.

[0038] On the other hand, the present application also provides a recommendation system for optimizing crop-livestock cycle resources, which is used to apply the recommendation method for optimizing crop-livestock cycle resources, including:

[0039] A collection unit is configured to collect corn growth image data, process all growth image data, extract growth characteristics of each corn, and establish a characteristic data set;

[0040] A processing unit is configured to extract the characterization data of all corns from the feature data set, wherein the characterization data includes plant height or leaf area, and perform Gaussian kernel density data processing on the characterization data to determine representative data;

[0041] The processing unit is further configured to compare the characterization data of each corn with the representative data to establish a first set, a second set and a third set;

[0042] The processing unit is further configured to compare the representative data with the big data to determine the predicted yield of a single plant, and adjust the predicted yield of a single plant according to the first set, the second set, and the third set to obtain a final predicted yield of a single plant, and determine the total yield according to the final predicted yield of a single plant;

[0043] The judgment unit is configured to compare the total output with the initial value, and judge whether to adjust the scale of livestock and poultry breeding according to the comparison result to determine the recommended scale.

[0044] It is understandable that the above-mentioned recommended system and method for optimizing crop-livestock cycle resources have the same beneficial effects and will not be elaborated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0046] Figure 1A flowchart of a recommended method for optimizing crop-breeding cycle resources provided by an embodiment of the present invention;

[0047] Figure 2 A functional block diagram of a recommendation system for optimizing crop-livestock cycle resources provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0049] In some embodiments of the present application, see Figure 1 As shown, a recommended method for optimizing crop-livestock cycle resources includes:

[0050] S100: collecting corn growth image data, processing all growth image data, extracting growth characteristics of each corn, and establishing a feature data set;

[0051] S200: extracting all corn characterization data from the feature data set, the characterization data including plant height or leaf area, performing Gaussian kernel density data processing on the characterization data, and determining representative data;

[0052] S300: Compare the characterization data of each corn with the representative data to establish a first set, a second set and a third set;

[0053] S400: comparing the representative data with the big data to determine the predicted yield of a single plant, adjusting the predicted yield of a single plant according to the first set, the second set, and the third set to obtain a final predicted yield of a single plant, and determining the total yield according to the final predicted yield of a single plant;

[0054] S500: Compare the total output with the initial value, determine whether to adjust the scale of livestock and poultry breeding according to the comparison result, and determine the recommended scale.

[0055] Specifically, in S100, image acquisition technology (such as high-definition sensors) is used to regularly obtain growth images of corn. Information about the growth height, leaf area, plant morphology, etc. of corn is provided. Computer vision and image processing technology (such as image segmentation, edge detection, etc.) are used to extract the growth characteristics of corn from the image, such as leaf area, plant height, etc. A feature data set containing multiple corns is established. In S200, the characterization data is used to measure the growth status of each corn plant. Through Gaussian kernel density processing, the extracted characterization data is smoothed to eliminate noise and irregular fluctuations in the data, thereby more accurately reflecting the overall growth trend of corn. Data processed by Gaussian kernel density can more effectively capture the main patterns of data distribution. In S300, the growth status of each corn plant is classified by comparison with representative data. In S400, the representative data is compared with a large data set of historical data, and the known growth and yield relationship in the historical data is used to determine the predicted yield of each corn plant, and the total yield is obtained in combination with the planting scale. In S500, the total yield is compared with the initially set target yield. Analyze and recommend adjustments to the breeding scale. For example, if corn does not grow as expected, the scale of livestock and poultry farming will need to be reduced.

[0056] It is understandable that through efficient data collection, image analysis and Gaussian kernel density processing, the growth of corn is monitored, and accurate yield prediction is achieved in combination with big data analysis. The accuracy and reliability of the prediction are improved. By comparing the data of each set, the prediction results can be self-corrected. It is possible to timely discover potential resource imbalance problems and automatically recommend adjustments to the breeding scale of livestock and poultry. Improve resource utilization efficiency.

[0057] In some embodiments of the present application, Gaussian kernel density data processing is performed on the characterization data to determine the representative data, including:

[0058] The same characterization data of all corns are integrated to form a first analysis data set, and kernel density estimation is performed on the data in the first analysis data set to determine the data with the highest probability as the representative data.

[0059] In some embodiments of the present application, when performing kernel density estimation on the data in the first analysis data set, the Gaussian kernel density function is:

[0060]

[0061] Where n is the number of data samples in the first analysis data set, h represents the smoothing bandwidth, and s i represents the i-th data point in the first analysis data set, and x represents any variable in the overall data.

[0062] In some embodiments of the present application, the smoothing bandwidth is calculated by the following formula:

[0063]

[0064] Wherein, h represents the smoothing bandwidth, σ represents the standard deviation of the data in the first analysis data set, β1 represents the data skewness, β2 represents the data kurtosis, n represents the number of data samples in the first analysis data set, and k represents the adjustment coefficient;

[0065] The data skewness is calculated by the following formula:

[0066]

[0067] The kurtosis of the data is calculated by the following formula:

[0068]

[0069] Where n represents the number of data samples in the first analysis data set, s i represents the i-th data point in the first analysis data set, represents the mean of the data in the first analysis data set, and σ represents the standard deviation of the data in the first analysis data set.

[0070] It is understandable that kernel density estimation can deeply explore the inherent laws of data without relying on explicit assumptions, providing more accurate and robust analysis results. By calculating the skewness and kurtosis of the data, the adjustment of the smoothing bandwidth is more adapted to the distribution characteristics of the data, improving the accuracy and stability of the overall prediction model. The accurate extraction of representative data helps to further adjust the scale of breeding and optimize resource allocation.

[0071] In some embodiments of the present application, when the characterization data of each corn is compared with the representative data to establish the first set, the second set and the third set, it includes:

[0072] The corn corresponding to the characterization data equal to the representative data is classified into the first set;

[0073] The corns with characterization data greater than the corresponding representative data are classified into the second set;

[0074] Corn with characterization data smaller than the corresponding representative data is classified into the third set.

[0075] In some embodiments of the present application, when the predicted yield per plant is adjusted according to the first set, the second set, and the third set to obtain the final predicted yield per plant, the method includes:

[0076]

[0077] Among them, Cz represents the final predicted yield of a single plant, m is the number of characterization data in the second set, Cp is the pth characterization data in the second set, C0 is the predicted yield of a single plant, n is the number of characterization data in the third set, and Cq is the qth characterization data in the third set.

[0078] It is understandable that the comparison of corn characterization data with representative data, the construction of data sets and the careful adjustment of the predicted yield of individual plants based on these sets ensure that the yield prediction is more in line with the actual growth conditions. It improves the accuracy of the prediction and avoids the accumulation of errors caused by a single standard.

[0079] In some embodiments of the present application, whether to adjust the scale of livestock and poultry farming is determined based on the comparison results, including: when the difference between the total output and the initial value is less than or equal to a% of the initial value, it is determined that the scale of livestock and poultry farming does not need to be adjusted, and the initial scale is used as the recommended scale; when the difference between the total output and the initial value is greater than a% of the initial value, it is determined that the scale of livestock and poultry farming is adjusted, and the recommended scale is determined.

[0080] In some embodiments of the present application, when determining to adjust the scale of livestock and poultry farming, it includes: obtaining the output difference based on the total output and the initial value, the output difference is the difference between the total output and the initial value, and determining the adjustment coefficient based on the output difference to adjust the scale of livestock and poultry farming, and the adjustment coefficient is proportional to the output difference.

[0081] It is understandable that by setting a reasonable tolerance threshold, frequent adjustments due to small fluctuations are avoided, ensuring the stability and efficiency of operations. The adjustment process responds flexibly according to the actual resource utilization situation. It can adapt to different environmental changes and resource demand changes more accurately.

[0082] In the above embodiment, by processing the corn growth image data, a feature data set is established to determine representative data, and accurate growth representation is provided for each corn plant. Dynamic monitoring of corn growth is achieved, and the single plant yield of each corn plant is predicted by comparison with big data, and the accuracy of the prediction is further improved by comparison and adjustment of multiple data sets. By comparing with the initial yield value, it is timely determined whether the scale of livestock and poultry breeding needs to be adjusted, and accurate resource allocation and optimization are achieved, which improves the synergy between breeding and agricultural planting and reduces resource waste.

[0083] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a recommendation system for optimizing crop-breeding cycle resources, which is used to apply the recommendation method for optimizing crop-breeding cycle resources, including:

[0084] A collection unit is configured to collect corn growth image data, process all growth image data, extract growth characteristics of each corn, and establish a characteristic data set;

[0085] A processing unit is configured to extract the characterization data of all corns from the feature data set, the characterization data including plant height or leaf area, perform Gaussian kernel density data processing on the characterization data, and determine representative data;

[0086] The processing unit is further configured to compare the characterization data of each corn with the representative data to establish a first set, a second set, and a third set;

[0087] The processing unit is further configured to compare the representative data with the big data to determine the predicted yield of a single plant, and adjust the predicted yield of a single plant according to the first set, the second set, and the third set to obtain a final predicted yield of a single plant, and determine the total yield according to the final predicted yield of a single plant;

[0088] The judgment unit is configured to compare the total output with the initial value, and determine whether to adjust the scale of livestock and poultry breeding according to the comparison result to determine the recommended scale.

[0089] It is understandable that the above-mentioned recommended system and method for optimizing crop-livestock cycle resources have the same beneficial effects and will not be elaborated here.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A recommended method for optimizing crop-livestock cycle resources, characterized in that: include: Collect corn growth image data, process all growth image data, extract the growth characteristics of each corn, and establish a feature data set; Extracting all corn characterization data from the feature data set, the characterization data including plant height or leaf area, performing Gaussian kernel density data processing on the characterization data to determine representative data; Comparing the characteristic data of each corn with the representative data to establish a first set, a second set and a third set; Comparing the representative data with the big data to determine the predicted yield of a single plant, and adjusting the predicted yield of a single plant according to the first set, the second set, and the third set to obtain a final predicted yield of a single plant, and determining the total yield according to the final predicted yield of a single plant; The total output is compared with the initial value, and based on the comparison result, it is determined whether the scale of livestock and poultry farming should be adjusted to determine the recommended scale.

2. The recommendation method according to claim 1, characterized in that: Performing Gaussian kernel density data processing on the characterization data to determine representative data includes: The same characterization data of all corns are integrated to form a first analysis data set, and kernel density estimation is performed on the data in the first analysis data set to determine that the data with the highest probability is the representative data.

3. The recommendation method according to claim 2, characterized in that: When performing kernel density estimation on the data in the first analysis data set, the Gaussian kernel density function is: Where n is the number of data samples in the first analysis data set, h is the smoothing bandwidth, and s is i represents the i-th data point in the first analysis data set, and x represents any variable in the overall data.

4. The recommendation method according to claim 3, characterized in that: The smoothing bandwidth is calculated by the following formula: Wherein, h represents the smoothing bandwidth, σ represents the standard deviation of the data in the first analysis data set, β1 represents the data skewness, β2 represents the data kurtosis, n represents the number of data samples in the first analysis data set, and k represents the adjustment coefficient; The data skewness is calculated by the following formula: The data kurtosis is calculated by the following formula: Where n represents the number of data samples in the first analysis data set, s i represents the i-th data point in the first analysis data set, represents the mean of the data in the first analysis data set, and σ represents the standard deviation of the data in the first analysis data set.

5. The recommendation method according to claim 1, characterized in that: When the characterization data of each corn is compared with the representative data to establish the first set, the second set and the third set, it includes: Classify the corn corresponding to the characterization data equal to the representative data into the first set; The corn corresponding to the characterization data being greater than the representative data is classified into the second set; The corn corresponding to the characterization data being smaller than the representative data is classified into the third set.

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