Rapid evaluation of poultry manure content using artificial neural networks (ANNs) method

文献类型： 外文期刊

第一作者： Chen, Longjian

作者： Chen, Longjian;Han, Lujia;Xing, Li

作者机构：

关键词： specific gravity;electrical conductivity;pH value;poultry manure

期刊名称：BIOSYSTEMS ENGINEERING （ 影响因子：4.123； 五年影响因子：4.508 ）

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收录情况： SCI

摘要： With increasing concern over the potential pollution from farm wastes, there is a need for rapid and robust methods that can analyse animal manure. In order to evaluate rapid testing methods based on the relationship between layer manure composition (ammonium nitrogen, total potassium, total nitrogen, total phosphorus, iron, copper, zinc, magnesium and sodium) and physicochemical properties (specific gravity, electrical conductivity, pH), diverse layer manure samples (n =105) were used. Relationships were investigated using linear regression and artificial neural networks (ANNs). The performance of a neural network-based model was compared with a linear regression-based model using the same observed data. It was found that ANN model consistently gives better predictions. Based on the results of this study, ANNs appear to be a promising technique for predicting layer manure composition.

分类号： S18

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