Abstract
The optimal amount of fertilizer application which was needed by the trees and the factors that influence the fertilization have an intricated nonlinear relationship. According to the problems that the traditional fertilization prediction model has, such as lacking of the scalability and practicality, this paper initiates an accurate fertilization prediction model that was based on the GRA-PSO-BP neural network which can make the accurate fertilization come true and improve the economic benefits of forest industry. This paper uses the GRA method to determine the input of the neural network as the site index and make the forest age, nutrient content of the advantage trees, biomass of the advantage trees, biomass of average trees, and target yield as the output numbers of the Actual amount of fertilizer applied. During the calculation process, the global particle swarm optimization algorithm is used to optimize the initial numbers and threshold numbers of BP neural network which build a phased GRA-PSO-BP accurate fertilization model. Compared with the prediction algorithm of full input variate that is based on the single BP neural network and the prediction algorithm of full input variate that is based on PSO-BP Neural Network, the GRA method can determine the key factors that influence the amount of fertilizer applied in different forest areas and modify the prediction model to improve the scalability and accuracy of the prediction and finally achieve the precision fertilization as the data of different forests updated, so we can see that the prediction result of this paper is more accurate. The result demonstrates that the GRA-PSO-BP neural network Segment fertilization model is more accurate than the traditional BP neural network and BP Neural Network that was optimized by the PSO algorithm, and specifically, the error of the predicted amount of fertilizer application and the actual amount of fertilizer application is less than 5%, which can effectively guide the fertilization in stages.
Highlights
In the process of forestry production, it is fundamentally required to calculate the amount of fertilizer, such as nitrogen, phosphorus, and potassium fertilizer, for different forest species and different soil conditions [1]
It is inefficient to determine the amount of fertilization by traditional experience or basic theoretical model, which may cause environmental pollution, so it is difficult to adapt to the new situation of precision forestry advocated by the contemporary world
In order to solve the problem of nonlinear precision fertilization, this paper introduces the neural network modeling method and uses BPNN, particle swarm optimization (PSO)-BPNN, and GRA-PSO-BPNN to construct three kinds of forest precise fertilization models for comparative verification and result analysis. rough comparison, the accuracy of the BPNN model is low and the error range is about 20%, and it is unstable
Summary
In the process of forestry production, it is fundamentally required to calculate the amount of fertilizer, such as nitrogen, phosphorus, and potassium fertilizer, for different forest species and different soil conditions [1]. In the condition of 25°C–35°C, Cunninghamia lanceolata can grow rapidly. In this regard, fertilization is the key link for high yield of Chinese fir forest. Forest fertilization functions as a critical technique to enhance soil fertility, improve tree nutrition, and facilitate rapid growth and high yield [3, 4]. E premise of precision production of Chinese fir forest is precise fertilization since such an agrotechnique can help balance the nutrients in soil and increase the nutrients required by trees, so as to achieve the target yield. The implementation of precision fertilization can save the fertilizer and phase down environmental pollution
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