| 秒尺度温室番茄作物-环境互作模型构建与验证 |
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| 引用本文： | 徐立鸿,孟凡峥,蔚瑞华.秒尺度温室番茄作物-环境互作模型构建与验证[J].农业工程学报,2021,37(8):212-222. |
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| 作者姓名： | 徐立鸿 孟凡峥 蔚瑞华 |
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| 作者单位： | 同济大学电子与信息工程学院，上海 201800 |
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| 基金项目： | 上海市科技兴农重点攻关项目（沪农科创字（2018）第3-2号）；国家自然科学基金项目（项目号：61973337） |
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| 摘 要： | 为了解决现有温室模型时间尺度不统一的问题,该研究建立了一个时间尺度统一的温室番茄作物-环境互作模型,描述作物与环境之间的相互作用,提高模型的精准性。首先,将番茄作物生长模型拆分成SUPPLY、PARTITION、GROWTH3个子模块,针对3个模块在由天数量级时间尺度到秒数量级时间尺度变换时存在的问题,通过模型替换、结构改造、参数辨识等方法对时间尺度进行了转换,并利用EFAST敏感性分析算法将模型中的不确定参数分为敏感参数和不敏感参数两类。然后,在秒时间尺度番茄作物生长模型的基础上,考虑番茄作物对温室环境的实时反馈,结合小气候模型形成包含未知参数的"通用"的互作模型结构。最后,利用贝叶斯优化方法及番茄生产温室的实际数据,分别对互作模型中生长模型和小气候模型的未知参数进行参数辨识,确定互作模型全部结构与参数,得到可用的互作模型。利用该研究得到的秒时间尺度生长模型对2015—2018年上海崇明A8温室番茄产量进行模拟,其与真实产量值间的均方根误差在7.34～18.85 g/m~2之间,平均相对误差在5.8%～18%之间,均小于TOMGRO模型与Integrated模型,可以更好地预测产量变化。含作物反馈的小气候环境模型经参数辨识后,模拟番茄作物3个不同生长时期(幼苗期、开花坐果期、结果期)的环境因子(温室内温度、湿度、CO_2浓度)变化的平均相对误差均在3%～6%之间,且相较于未考虑作物反馈的一般小气候模型有更好的模拟效果。互作模型的建立将作物与温室小气候环境统一成一个模型,可以为温室环境控制提供模型基础。
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| 关 键 词： | 温室 模型 番茄生长模型 小气候模型 敏感性分析 贝叶斯优化 |
| 收稿时间： | 2020/11/18 0:00:00 |
| 修稿时间： | 2021/1/23 0:00:00 |
Development and verification of tomato crop-environment interaction model in second timescale greenhouse |
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| Xu Lihong,Meng Fanzheng,Wei Ruihua.Development and verification of tomato crop-environment interaction model in second timescale greenhouse[J].Transactions of the Chinese Society of Agricultural Engineering,2021,37(8):212-222. |
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| Authors: | Xu Lihong Meng Fanzheng Wei Ruihua |
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| Institution: | College of Electronics and Information Engineering in Tongji University, Shanghai 201800, China |
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| Abstract: | Abstract: Greenhouse cultivation has a strong impact of crops on the complex process, because various time scales existed in the controlled environment. The greenhouse model can be divided into two types, including the crop growth and microclimate model. The crop growth model was usually used to simulate daily change of crop, where the variables of crop is updated at each time step within a day. The microclimate model has a shorter calculating step, because the climate in a greenhouse changed quickly, due mainly to rapid fluctuation of weather outside. In general, the climate physics is considered as a fast process, while the crop physics is considered as a slow one. The difference of time scales has brought a great challenge at the level of crop state, such as the rapid fluctuations of greenhouse climate or the elusive ambient inputs in the monitoring system of a greenhouse. In this study, taking tomato as a research object, a crop-climate interactive model at small timescale was established to balance the time scales of crop and climate in greenhouse. Firstly, the growth model was divided into three sub-modules, including the SUPPLY, PARTITION, and GROWTH. The replacement, structural transformation were implemented in the model, when three modules were transformed from a long timescale (day level) to a small timescale (second level). Two types of uncertain parameters were divided in the model under a global sensitivity analysis (Extended Fourier Amplitude Sensitivity Test, EFAST), such as sensitive and insensitive parameters. Insensitive parameters were fixed in the model, whereas, the sensitive parameters needed to be identified, according to real production data in specific greenhouses. Secondly, the general interactive model was obtained to combine small time-scale crop growth model and greenhouse microclimate model. Microclimate in the interactive model was different from other microclimate model, because it fully considered the reaction between the microclimate and crop, where the microclimate model was be considered as an input for the crop model. The proposed interactive model was also calibrated and validated in the field test. The real data was collected from A8 Venlo type greenhouse at Chongming Island, Shanghai of China. The 4-year (2015-2018) observed data of tomato yield was used in the model. It was found that the root mean square error (RMSE) between the simulated and real yield value was 7.3-18.85, and the average relative error was between 5.8% and 18%, both less than TOMGRO and Integrated model. The data demonstrated that the interactive model presented a better performance on the yield prediction of tomato. The microclimate simulation result also proved that the interactive model behaved a higher accuracy at different crop growth stages than that without considering the influence of crop growth. The average relative error was less than 10% for the prediction of microclimate environment at three stages of crop growth (growing, blooming and setting fruiting), indicating high efficiency to simulate the real dynamics of greenhouse microclimate. Nevertheless, there were relatively larger deviations in the small part of simulation from actual data, such as simulated yield in 2018 and temperature trajectory when LAI=2. Bayesian optimization was also used to identify the uncertain parameters in both crop growth and microclimate model. Model structure and parameters were totally determined after sensitivity analysis and parameter identification. Consequently, the interactive model can provide a theoretical basis for cultivation and environmental control in a greenhouse. |
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| Keywords: | greenhouse models tomato growth model microclimate model sensitivity analysis Bayesian optimization |
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