Grassland degeneration grading method based on bp neural network
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A bp neural network and classification method technology, applied in the field of grassland grassland degradation degree evaluation, can solve the problem of high labor intensity and achieve the effect of automatic classification
Inactive Publication Date: 2019-09-17
QINGHAI UNIVERSITY
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[0008] However, the above studies on grassland degradation still need to use artificial methods to collect data for grassland evaluation,
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Embodiment 1
[0030] see Figure 1~2 , in an embodiment of the present invention, a method for grading grassland degradation based on a bp neural network, comprising the following steps:
[0031] S1, determine the influencing factors of grassland degradation, and obtain the original data related to the influencing factors. Here, the influencing factors are determined as convex spot coverage, proportion of dominant grass species, proportion of degraded indicator species, proportion of edible grass and rodent damage ;
[0032] S2. Process the original data related to the influencing factors to obtain standardized data. In practical applications, there are many wrong data and vacant data in the obtained original data table. Therefore, the original data needs to be cleaned to obtain normalized data. Obtain valuable data that can be used;
[0033] S3, importing the normalized data into the pre-trained bp neural network model, and outputting the evaluation and grading results;
[0034] S4, tra...
Embodiment 2
[0036] see Figure 1~2 , in an embodiment of the present invention, a method for grading grassland degradation based on a bp neural network, comprising the following steps:
[0037] S1, determine the influencing factors of grassland degradation, and obtain the original data related to the influencing factors. Here, the influencing factors are determined as convex spot coverage, proportion of dominant grass species, proportion of degraded indicator species, proportion of edible grass and rodent damage ;
[0038] S2. Process the original data related to the influencing factors to obtain standardized data. In practical applications, there are many wrong data and vacant data in the obtained original data table. Therefore, the original data needs to be cleaned to obtain normalized data. Obtain valuable data that can be used;
[0039] In terms of actual application, the original data are shown in Table 1
[0040] Table 1, raw data table
[0041]
[0042]
[0043] In the ta...
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Abstract
The invention discloses a grassland degeneration grading method based on a bp neural network, and relates to a grassland degeneration degree evaluation method, which comprises the following steps: S1, determining influence factors of grassland degeneration, and obtaining original data related to the influence factors; s2, processing the original data related to the influence factors to obtain standardized data; s3, importing the standardized data into a pre-trained bp neural network model, and outputting an evaluation grading result; and S4, translating and transcoding the evaluation grading result, and outputting the result in a friendly manner. The beneficial effects of the invention are as follows: the grassland degeneration is automatically graded based on the bp neural network according to a plurality of collected data by taking five main factors which affect the grassland, namely the coverage degree of convex spots, the proportion of dominant grass seeds, the proportion of degeneration indicating seeds, the proportion of edible pasture and the rat damage condition as entry points, and the grassland degeneration grading method can be widely applied to the aspects of grassland degeneration research and the like.
Description
technical field [0001] The invention relates to a method for evaluating grassland degradation degree, in particular to a method for grading grassland degradation based on bp neural network. Background technique [0002] Grassland degradation research is a very complicated project, and the evaluation of grassland degradation degree directly affects the method and intensity of follow-up governance. Inaccurate evaluation will cause a lot of resource consumption, so scientific evaluation methods are very important. At present, with regard to grassland evaluation issues, most grassland workers and researchers use manual methods to collect data for grassland evaluation. This method has many disadvantages such as high cost, low efficiency, and low accuracy, and does not make full use of the existing data resources. [0003] So far, many grassland research workers have done a lot of research on grassland evaluation. Yan Yingxie’s paper "Remote Sensing Evaluation of Grassland Degrad...
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