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Dynamic optimizations of cultivation and fruit-storage processes using a speaking plant-based intelligent control technique

机译:使用基于植物的智能控制技术动态优化栽培和水果存储过程

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This paper presents applications of a 'speaking plant'-based intelligent control technique to dynamically optimize hydroponic cultivation and fruit-storage processes. Tomato growth during the seedling stage was optimized by nutrient concentration ofa hydroponic solution in a cultivation process. Similarly, for the storage process, the rate of water loss in tomatoes was minimized by temperature control. The control system comprises a decision system and a feedback control system. The former is usedfor determining the optimal set point profile (l-step set points) of the environment based on plant responses, whereas the latter controls the environment based on the optimal set point profile. The optimal set point profile of the nutrient concentration of the solution during the seedling stage was a slightly high level (1.4mS/cm) during the first few days, a markedly low level (0.3 mS/cm) during the second few days, a slightly high level (1.6 mS/cm) during the third few days, and the maximum level (2.0 mS/cm) during the last stage (flowering stage of the first truss) under the range of 0.3 to 2.0 mS/cm. This operation promoted reproductive growth of the tomato. In the storage process, on the other hand, the optimal temperature operation that first rises to a high level (40 deg C) for a day and then suddenly drops to the minimum level (15 deg C) under the range of 15 to 40 deg C provided lower values of the rate of water loss than when the temperature was maintained constantly at the lowest level throughout the entire control process. These results suggest that dynamic optimization using a speaking plant-based intelligent control technique is useful for qualitative improvement of both the plant during cultivation and the fruit during storage.
机译:本文介绍了基于“说话植物”的智能控制技术在动态优化水培栽培和果实贮藏过程中的应用。通过在栽培过程中水培溶液的养分浓度来优化幼苗期的番茄生长。类似地,对于存储过程,通过温度控制将西红柿中的水分流失速率降至最低。该控制系统包括决策系统和反馈控制系统。前者用于基于植物响应确定环境的最佳设定点分布图(I步设定点),而后者用于基于最佳设定点分布图控制环境。在苗期,溶液养分浓度的最佳设定点曲线在最初几天为较高水平(1.4mS / cm),在随后几天为显着较低水平(0.3mS / cm),第三天的水位略高(1.6 mS / cm),而最后一个阶段(第一桁架的开花期)的最高水位(2.0 mS / cm)在0.3至2.0 mS / cm的范围内。该操作促进了番茄的生殖生长。另一方面,在存储过程中,最佳温度操作会先升高到一天的最高温度(40摄氏度),然后在15至40摄氏度的范围内突然降至最低温度(15摄氏度)。与在整个控制过程中始终将温度恒定保持在最低水平时相比,C提供的失水速率值更低。这些结果表明,使用有说服力的基于植物的智能控制技术进行动态优化,对于植物在栽培期间和果实在贮藏期间的质性改善都是有用的。

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