Research on artificial neural networks to accurately predict element concentrations in nutrient solutions
文献类型: 外文期刊
作者: Zhai, Jiawei 1 ; Dong, Hongtu 1 ; Liu, Tianyang 1 ; Jin, Xiaotong 1 ; Luo, Bin 1 ; Li, Aixue 1 ; Wang, Cheng 1 ; Wang, Xiaodong 1 ;
作者机构: 1.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China
2.Fujian Agr & Forestry Univ, Coll Mech & Elect Engn, Fuzhou 350100, Peoples R China
3.Minist Agr & Rural Affairs, Key Lab Agr Sensors, Beijing 100097, Peoples R China
4.11 Shuguang Huayuan Zhong Lu, Beijing, Peoples R China
关键词: nutrient solution measurement; multi-ion sensing; back propagation (BP); radial basis function (RBF); particle swarm optimization (PSO); genetic algorithm (GA); gravitational search algorithm (GSA)
期刊名称:MEASUREMENT SCIENCE AND TECHNOLOGY ( 影响因子:2.4; 五年影响因子:2.3 )
ISSN: 0957-0233
年卷期: 2023 年 34 卷 11 期
页码:
收录情况: SCI
摘要: Calcium, potassium, nitrogen, magnesium, and phosphorus, the main elements of the nutrient solution, are absorbed by plants and play an important role in plants. By measuring Ca2+, K+, Mg2+, NH4 (+), NO3 (-), HPO4 (2-), the artificial neural networks (ANNs) were used in this study to accurately calculate the concentrations of these elements. Firstly, the error sources of the calculating element concentration were analyzed based on the data of six-ion measurement experiments. Subsequently, various optimization algorithms were compared to optimize back propagation and radial basis function ANNs. Finally, the results of mean relative errors (MREs) and recovery values show that ANNs can effectively reduce the measurement error of ion sensors. From the perspective of recovery values, the prediction error of all elements can be controlled within 15%. From the perspective of MRE, except for magnesium and phosphorus elements, the improved model prediction errors of other elements were also less than 10%.
- 相关文献
作者其他论文 更多>>
-
Ultrasensitive molecular imprinted electrochemical sensor for in vivo determination of glycine betaine in plants
作者:Ai, Geng;Zhou, Yanan;Zhang, Heng;Wei, Qian;Luo, Bin;Wang, Cheng;Xue, Xuzhang;Li, Aixue;Ai, Geng;Xie, Yingge
关键词:Molecularly imprinted polymer; Glycine betaine; In vivo measurement; Electrochemical sensor
-
An ultrasensitive probe-free electrochemical immunosensor for gibberellins employing polydopamine-antibody nanoparticles modified electrode
作者:You, Yang;Luo, Bin;Wang, Cheng;Dong, Hongtu;Wang, Xiaodong;Hou, Peichen;Li, Aixue;You, Yang;Sun, Lijun
关键词:Gibberellins; Immunosensor; Carboxylated graphene oxide; Carboxylated multi -walled carbon nanotubes; Polydopamine nanoparticles
-
Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform
作者:Li, Yinglun;Wen, Weiliang;Fan, Jiangchuan;Gou, Wenbo;Gu, Shenghao;Lu, Xianju;Guo, Xinyu;Li, Yinglun;Wen, Weiliang;Fan, Jiangchuan;Gou, Wenbo;Gu, Shenghao;Lu, Xianju;Yu, Zetao;Wang, Xiaodong;Guo, Xinyu
关键词:
-
Study on the spatio-temporal evolution and influencing factors of farmland abandonment on a county scale
作者:Wang, Cheng;Su, Yue;Xie, Yan;Xia, Panpan;He, Shan;Cui, Yanglin;Cui, Yanglin
关键词:Farmland abandonment; Spatiotemporal evolution; Influencing factors; Yangtze River Delta; Pingyang County
-
Recognition of maize seed varieties based on hyperspectral imaging technology and integrated learning algorithms
作者:Yang, Huan;Wang, Cheng;Zhang, Han;Zhou, Ya'nan;Luo, Bin;Yang, Huan;Wang, Cheng;Zhang, Han;Zhou, Ya'nan;Luo, Bin;Yang, Huan;Wang, Cheng
关键词:Hyperspectral; Random subspace ensemble learning; Maize seed; Variety recognition
-
Identification of wheat seed endosperm texture using hyperspectral imaging combined with an ensemble learning model
作者:Zhao, Wei;Luo, Bin;Bai, Weiwei;Kang, Kai;Zhang, Han;Zhao, Wei;Zhao, Xueni;Bai, Weiwei;Hou, Peichen;Zhang, Han
关键词:Wheat classification; Feature fusion; Endosperm texture; Vitreosity; Hyperspectral imaging; Ensemble learning
-
Detection of seed purity of hybrid wheat using reflectance and transmittance hyperspectral imaging technology
作者:Zhang, Han;Tu, Keling;Sun, Qun;Zhang, Han;Luo, Bin;Hou, Qiling;Zhao, Changping
关键词:hybrid wheat; seed purity; hyperspectral imaging; reflectance spectrum; transmittance spectrum; machine learning