张淼,孔盼,李雁华,任海燕,蒲攀,张丽楠.基于极限学习机的土壤硝态氮预测模型研究[J].农业机械学报,2016,47(6):93-99.
Zhang Miao,Kong Pan,Li Yanhua,Ren Haiyan,Pu Pan,Zhang Li’nan.Prediction Model of Soil NO-3-N Concentration Based on Extreme Learning Machine[J].Transactions of the Chinese Society for Agricultural Machinery,2016,47(6):93-99.
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基于极限学习机的土壤硝态氮预测模型研究   [下载全文]
Prediction Model of Soil NO-3-N Concentration Based on Extreme Learning Machine   [Download Pdf][in English]
投稿时间:2015-12-09  
DOI:10.6041/j.issn.1000-1298.2016.06.012
中文关键词:  土壤硝态氮  离子选择电极  能斯特模型  极限学习机
基金项目:国家自然科学基金项目(31201136、61134011)
作者单位
张淼 中国农业大学 
孔盼 中国农业大学 
李雁华 中国农业大学 
任海燕 中国农业大学 
蒲攀 中国农业大学 
张丽楠 中国农业大学 
中文摘要:利用极限学习机模型解译高氯离子干扰下盐碱土中硝酸根离子选择电极响应信号,系统分析了漂移校正算法、能斯特及极限学习机模型对电极法硝态氮(NO-3-N)预测结果准确性的影响差异。结果表明,漂移校正算法可明显提高传感器标定方程的重复性和一致性,响应斜率及截距电位的波动范围分别缩小了3.67%和7.25%;极限学习机模型的最优隐含层节点数为14;基于极限学习机的电极法NO-3-N质量浓度预测模型可较好抑制盐碱土中氯离子干扰,与标准检测结果之间的最大绝对误差和均方根误差分别为6.36mg/L和4.02mg/L。相关研究结论可为电极法测土过程中的信号校正、数据处理模型和模型参数选取提供参考。
Zhang Miao  Kong Pan  Li Yanhua  Ren Haiyan  Pu Pan  Zhang Li’nan
China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:soil nitrate-nitrogen  ion-selective electrodes  Nernst model  extreme learning machine
Abstract:The soil nitrate-nitrogen (NO-3-N) is essential element for crop growth. Because of the obvious advantages on cost, applicability and easy-implementation, the nitrate ion-selective electrode (ISE) was demonstrated potentials in both laboratory and in-field researches on soil available nitrogen detections. However, problems of unidealistic selectivity and potential drift usually limited the application of ISE. The extreme learning machine algorithm was used to decouple the signals of nitrate ion-selective electrode from the interference of chloride. Three data processing algorithms, including drift correction, Nernstian model and extreme learning machine were systemically analyzed. Experiments were carried out on the self-designed multi-channel nutrient detection platform. Totally 150 soil samples were selected for the system validation. The experimental results indicated that the accuracy and consistency of sensor’s scaling equations were effectively improved by drift correction algorithm. The variations of response slope and intercept potential were reduced by 3.67% and 7.25%, respectively. The neuron number in hidden layer of the extreme learning machine was 14,which were tested as optimized parameter. The extreme learning machine could effectively decouple the interference of chloride from nitrate ion-selective electrode in saline alkali soil. The maximum absolute error and root mean square error were 6.36mg/L and 4.02mg/L, respectively. In conclusion, the research results can provide references in the related studies for soil detection by ion-selective electrode.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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