关键词: 最小二乘支持向量机, 评价, 粒子群算法, 土壤肥力

Abstract: Associated with Particle Swarm Optimization (PSO) and Least Squares Support Vector Machine (LSSVR), this study proposes a soil fertility evaluation model based on PSO-LSSVR. Ten soil indicators were selected as elevation indexes, which included organic matters, total nitrogen, available phosphorus, available potassium, cation exchange, capacity, acidity-alkalinity, volume weight, clay dispersion, water stable aggregated and dispersion ratio. The black soil in Jilin province was used example to construct soil fertility evaluation model. The result was compared with two other methods (extension-element method and ordinary SVM method), the soil fertility levels of most samples were consistent, sample 2 and sample 13 were classified as grade Ⅳ and grade Ⅲ by PSO-LSSVR model respectively, which were in line with the actual situation, which showed that PSO-LSSVR model was a suitable soil fertility evaluation model and the result accurately reflected soil properties.

Key words: least square support vector machine regression, evaluation, particle swarm optimization, soil fertility