Grassland degeneration grading method based on bp neural network

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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Problems solved by technology

[0008] However, the above studies on grassland degradation still need to use artificial methods to collect data for grassland evaluation, which is labor-intensive. Based on this, this application proposes a grassland degradation classification method based on bp neural network

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