陈士旺,李莉,杨成飞,李文军,孟繁佳.基于基质含水率的作物蒸腾量估算与预测模型研究[J].农业机械学报,2019,50(Supp):187-194.
CHEN Shiwang,LI Li,YANG Chengfei,LI Wenjun,MENG Fanjia.Estimation and Prediction Model of Crop Transpiration Based on Matrix Moisture Content[J].Transactions of the Chinese Society for Agricultural Machinery,2019,50(Supp):187-194.
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基于基质含水率的作物蒸腾量估算与预测模型研究   [下载全文]
Estimation and Prediction Model of Crop Transpiration Based on Matrix Moisture Content   [Download Pdf][in English]
投稿时间:2019-04-13  
DOI:10.6041/j.issn.1000-1298.2019.S0.029
中文关键词:  作物蒸腾量  基质含水率变化量  估算模型  预测模型  线性回归  GABP神经网络
基金项目:国家重点研发计划项目(2016YED0201003)和丽江市科技计划项目(LJGZZ-2018001)
作者单位
陈士旺 中国农业大学 
李莉 中国农业大学 
杨成飞 中国农业大学 
李文军 中国农业大学 
孟繁佳 中国农业大学 
中文摘要:作物蒸腾作用是基质水分传输的主要驱动力,建立了基于基质含水率变化量的温室番茄作物蒸腾量估算模型和预测模型,并进行对比分析。使用校准后的EC5基质含水率传感器,记录第1次灌溉后与第2次灌溉前基质实时含水率变化量,使用称量法测量作物实时蒸腾量。通过基质含水率变化量与基质栽培槽体积的多元线性回归运算,建立番茄单株日蒸腾量估算模型;将基质含水率变化量、空气温度、空气湿度和光照强度作为输入,利用GABP神经网络算法,建立番茄单株日蒸腾量预测模型。将试验所得温室作物日蒸腾量估算模型和预测模型分别与温室作物实际日蒸腾量进行线性回归分析,结果表明,基于基质含水率变化量建立的估算模型在苗期、花期的预测精度分别为0.9729、0.9796,预测模型的预测精度分别为0.9915、0.9890,两者之间差异不大,但估算模型运算速度远高于预测模型的运算速度,估算模型对于温室灌溉管理具有推广应用价值。
CHEN Shiwang  LI Li  YANG Chengfei  LI Wenjun  MENG Fanjia
China Agricultural University,China Agricultural University,China Agricultural University,China Agricultural University and China Agricultural University
Key Words:crop transpiration  variation of matrix moisture content  estimation model  prediction model  linear regression  GABP neural network
Abstract:Crop transpiration was the main driving force of substrate water transfer. Aiming to establish a greenhouse tomato crop transpiration estimation model and prediction model based on the change of substrate water content, and make a comparative analysis.The calibrated EC5 matrix moisture content sensor was used to record the real time change of matrix moisture content after the first irrigation and before the second irrigation. Real time crop transpiration was measured by weighing method. The estimation model of daily transpiration per plant of tomato was established by multiple linear regression calculation of variation of substrate moisture content and volume of substrate cultivation tank. Taking the variation of substrate moisture content, air temperature, air humidity and illuminate intensity as input, the prediction model of daily transpiration per plant of tomato was established by GABP neural network algorithm. The greenhouse crop transpiration estimation model and predictive model were tested respectively with the greenhouse crop’s daily transpiration by linear regression analysis, the results showed that the prediction accuracy of the estimation model based on the variation of water content in the matrix was 0.9729 and 0.9796, respectively, in the seedling stage and florescence, and the prediction accuracy of the prediction model was 0.9915 and 0.9890, respectively. The differences between the two was not big, but the estimate model operation speed was much higher than predictionmodel of operation speed. In practical application, the estimation model had good robustness to environmental changes, and the relative error was less than 5% at seedling stage and flowering stage. The estimation model had the value of popularization and application for greenhouse irrigation management.

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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