Study on Prediction Model of Soil Nutrient Content Based on Optimized BP Neural Network Model. Issue 4 (21st February 2023)

• Record Type: Journal Article
• Authors: Wang, Guowei; Wang, Jiaxin; Wang, Jiawei; Yu, Haiye; Sui, Yuanyuan
• Is Part Of: Communications in soil science and plant analysis. Volume 54:Issue 4(2023)
• Page Start: 463
• Page End: 471
• Publication Date: 2023-02-21
• Subjects: BP neural network -- Grey Wolf algorithm -- precise fertilization -- prediction of soil nutrient content; Soil science -- Periodicals; Plants -- Chemical analysis -- Periodicals; Agricultural chemistry -- Periodicals; 631.405
• DOI: 10.1080/00103624.2022.2118291
• Languages: English
• ISSNs: 0010-3624

Abstract

ABSTRACT: Soil nutrient content is an important index to determine the amount of fertilizer. Traditional soil nutrient content is obtained by manual sampling, which greatly increases the cost of agricultural production input. In order to solve this problem, this paper studies the relationship between soil nutrients, fertilizer amount, yield and the next year's soil nutrients, and establishes an optimized BP neural network model to realize the prediction of soil nutrient content. Aiming at the problem that the traditional BP neural network model will affect the prediction accuracy due to the different weights and thresholds, the Grey Wolf algorithm with reverse learning mechanism is introduced to obtain the optimal solution of BP neural network weights and thresholds. The soil nutrient content data collected in chenjiadian village, Nongan County, Jilin Province for five consecutive years were used to test the model. The data of the first four years were used as the training set and the data of the fifth year as the test set. The prediction results of BP neural network model, Grey Wolf algorithm optimized BP neural network model and Grey Wolf algorithm optimized BP neural network model with reverse learning mechanism were compared The prediction accuracy of BP neural network model optimized by Grey Wolf algorithm with reverse learning mechanism reached 88.7%, which was better than the first two models. It can provide basis for the fertilization decision of variable rate fertilization in the next year, so as to reduce the input of labor cost.