| 温室封闭式栽培自适应灌溉方法 |
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| 引用本文： | 袁洪波,李 莉,王海华,N.A. Sigrimis.温室封闭式栽培自适应灌溉方法[J].农业工程学报,2015,31(22):222-228. |
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| 作者姓名： | 袁洪波 李 莉 王海华 N.A. Sigrimis |
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| 作者单位： | 1.河北农业大学机电工程学院，保定 071001,2.中国农业大学现代精细农业系统集成教育部重点实验室，北京 100083,2.中国农业大学现代精细农业系统集成教育部重点实验室，北京 100083,3. 希腊雅典农业大学农业工程系，雅典11855，希腊 |
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| 基金项目： | 国家自然科学基金（青年）项目（31301240）；土壤植物机器系统技术国家重点实验室开放课题基金（2014-SKL-03）；河北省高等学校科学研究计划青年基金项目（QN20131083）；河北省自然科学基金项目（C2014204025）保定市科学研究与发展计划项目（15ZN005、15ZG011） |
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| 摘 要： | 为了改进和提高温室封闭式栽培精细灌溉控制方法,针对利用Penman-Monteith(P-M)公式和传感器数据信息相结合进行灌溉控制中因为涉及参数较多而使用不便、需要近似计算导致建立的作物蒸腾模型精度不够等问题,该文根据封闭式栽培可以回收并循环利用多余灌溉水的特点,利用灌溉量与排出量的差值和温室小气候环境数据建立相对精确的作物蒸腾量计算模型,并在此基础上利用人工神经网络算法实现了温室封闭式栽培自适应灌溉控制,结果表明,在10 d内灌溉用水量为实际蒸腾量的97.8%,基本实现了按照作物需水量进行灌溉。研究对于实现按照作物蒸腾量进行准确的水分供给、节约灌溉用水量、提高水分利用效率具有一定的现实意义。
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| 关 键 词： | 灌溉 温室 模型 蒸腾 自适应控制 封闭式栽培 |
| 收稿时间： | 2015/7/16 0:00:00 |
| 修稿时间： | 2015/10/10 0:00:00 |
Adaptive irrigation method for closed cultivation in greenhouse |
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| Yuan Hongbo,Li Li,Wang Haihua and N.A. Sigrimis.Adaptive irrigation method for closed cultivation in greenhouse[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(22):222-228. |
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| Authors: | Yuan Hongbo Li Li Wang Haihua and NA Sigrimis |
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| Institution: | 1. College of Mechanical and Electrical Engineering, Agricultural University of Hebei, Baoding 071001, China;,2. Key Laboratory on Modern Precision Agriculture System Integration Research of Ministry of Education, China Agricultural University, Beijing 100083, China;,2. Key Laboratory on Modern Precision Agriculture System Integration Research of Ministry of Education, China Agricultural University, Beijing 100083, China; and 3. Department of Agricultural Engineering, Agricultural University of Athens, Athens 11855, Greece; |
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| Abstract: | Abstract: At present, the research of precision irrigation are mostly concentrated on combining the Penman-Monteith(P-M) formulate and sensor data information to calculate the crop evapotranspiration, and then the irrigation operations are performed in accordance with the calculation results. But the calculation of crop evapotranspiration based on the P-M formula not only requires a lot of environmental parameters, but also is an approximated calculation for the greenhouse environment, and the results of calculation cannot accurately reflect the crop evapotranspiration. So a more practical and simple method is required for the closed cultivation in greenhouses. Based on the characteristics of closed cultivation in greenhouse, the excess irrigation water can be collected and recycled easily by drainage system, we established a relatively accurate crop evapotranspiration computational model using artificial neural network according to the difference between the amount of irrigation and drainage based on the microclimate data in greenhouse, and tested for its feasibility in the adaptive control for irrigation in greenhouse.The difference between irrigation and drainage was calculated, and then an artificial neural network model was trained based on that difference and environmental information (temperature, relative humidity and solar radiation) in greenhouse so as to establish the crop transpiration model. The crop transpiration was calculated using this model according to the current environmental information until the set of value was reached, and then, the irrigation program was executed. Then, the difference between the amount of irrigation and drainage was calculated again, and the crop transpiration model was modified according to the value. After that, the amount of crop transpiration was calculated again by the modified model and the irrigation operation was executed when the set of value was reached again, so the crop transpiration calculation model was adjusted again and again, finally the crop transpiration model would be infinitely close to the actual evapotranspiration process, and the crop irrigation amount and frequency of days were adaptively adjusted in accordance with the method described above. The research data used in this work had been obtained from greenhouses located in the Agricultural Unisversity of Athens, Greece, at November 3-13, 2014. In the greenhouses, tomato was planted in the bags of substrate, and the bags were placed on the brackets that height of 0.5 m, and the drainage system were designed in the brackets. The drainage was collected into the recycle tank through the pipe, and a water level sensor was placed in the recycle tank. The drainage was measured after each irrigation and then the actual crop water requirements could be obtained by the combination of irrigation and drainage, which was the actual crop transpiration between the two irrigation processes. In addition, greenhouses temperature, relative humidity and solar radiation were also collected. The results showed that the calculated value of the model was more accurate under the condition that the information on greenhouse environment parameter was sufficient, such as the use of environment data within 216 hours. In the experiment, the 10 days irrigation control was implemented using the calculated the value of crop transpiration model, the total amount of irrigation water was 76517.59 L, and the actual amount of transpiration was 78238.85 L, which was obtained using the difference between irrigation and drainage. The error between the theoretical value and the actual value was 2.2%, and the daily average difference between the irrigation and actual transpiration was 172.126 L, or 0.043 L/(m2·d), suggesting that the irrigation amount was consistent with actual crop evapotranspiration. So the adaptive irrigation method is reliable to meet actual crop water demand for irrigation control purpose. |
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| Keywords: | irrigation greenhouses models evapotranspiration adaptive control closed cultivation |
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