廖敏, 粟超, 张宇, 杨亚军, 张强. 大棚种植川贝母分区变量灌溉系统研制[J]. 农业工程学报, 2021, 37(16): 108-116. DOI: 10.11975/j.issn.1002-6819.2021.16.014
    引用本文: 廖敏, 粟超, 张宇, 杨亚军, 张强. 大棚种植川贝母分区变量灌溉系统研制[J]. 农业工程学报, 2021, 37(16): 108-116. DOI: 10.11975/j.issn.1002-6819.2021.16.014
    Liao Min, Su Chao, Zhang Yu, Yang Yajun, Zhang Qiang. Development of the partition variable irrigation system for greenhouse planting Fritillaria cirrhosa[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(16): 108-116. DOI: 10.11975/j.issn.1002-6819.2021.16.014
    Citation: Liao Min, Su Chao, Zhang Yu, Yang Yajun, Zhang Qiang. Development of the partition variable irrigation system for greenhouse planting Fritillaria cirrhosa[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(16): 108-116. DOI: 10.11975/j.issn.1002-6819.2021.16.014

    大棚种植川贝母分区变量灌溉系统研制

    Development of the partition variable irrigation system for greenhouse planting Fritillaria cirrhosa

    • 摘要: 名贵中药材川贝母喜湿、怕高湿特性成为人工灌溉的难点,智能化精准灌溉系统可实现川贝母按需节水灌溉。该研究开发了基于无线传感器网络的川贝母分区变量灌溉系统。在人工种植试验过程中,采用电容法和土壤水分测定仪获得了川贝母生长需水及灌溉用水数据,建立了川贝母生长含水率模型和灌溉含水率模型。为了实现川贝母分区变量灌溉,建立了灌溉模糊控制决策模型,该模糊控制器为双输入单输出结构,利用遗传算法优化模糊控制量化因子、比例因子、模糊控制规则和隶属函数,实现遗传算法优化的模糊控制对川贝母灌用水进行精确决策和川贝母分区变量灌溉。在川贝母种植大棚内应用了该分区变量灌溉技术和系统,结果表明,模糊控制决策的灌溉有一定节水效果,遗传算法优化后的模糊控制每次灌溉土壤含水率增量主要分布在5%~7%,灌溉土壤含水率增量有明显下降。特定种植密度下灌溉试验结果表明,优化后川贝母变量灌溉误差能控制在±5%附近,满足川贝母按需灌溉需求,分区变量灌溉效果明显;随川贝母种植密度增加,所需灌溉用水也增大,二者基本呈线性关系(R2=0.975);川贝母分区变量灌溉节水率与种植密度比之间呈抛物线关系,优化后标准种植密度的年节水率为27.6%。该研究可为川贝母种植密度和灌溉节水提供参考和技术支持。

       

      Abstract: Abstract: Fritillaria cirrhosa is one of the most precious traditional Chinese medicines. Some characteristics, like being fond of humidity and fearing of high humidity, have posed a great challenge to the artificial irrigation of Fritillaria cirrhosa. The intelligent precision irrigation system can be expected to realize the water-saving irrigation on demand in recent years. In this study, a partition system of variable rate Fritillaria Cirrhosa irrigation was developed using the wireless sensor network. An investigation was made on the impact of growth age, planting season, soil, and planting density on the required irrigation water of Fritillaria cirrhosa in the process of artificial cultivation between April 2018 and December 2020. A capacitance analyzer was also selected to detect the soil moisture content. The water demand was thus obtained for the Fritillaria cirrhosa growth and irrigation water in planting soil. As such, the required and planting soil moisture content model was established for the growth of Fritillaria cirrhosa. The results showed that the required soil moisture content of Fritillaria Cirrhosa growth increased, with the increase of planting density, in addition to the factor of Fritillaria cirrhosa growth age. A fuzzy control strategy model was established for the application of partition variable irrigation of Fritillaria cirrhosa. A double-input and simple-output structure was adopted for the fuzzy controller. The input variables were the planting density c and the difference value e between the required soil moisture content for Fritillaria cirrhosa growth and the planting one. The output variable was the increment of soil moisture content u during irrigation. Genetic Algorithm (GA) was utilized to optimize the quantification factors of fuzzy control, Ke and Kc, the scale factor Ku, the fuzzy control rules, as well as the membership function, in order to achieve an accurate decision on the irrigation water of Fritillaria cirrhosa. The irrigation water was also applied under the different planting densities, seasons, and growing ages. A field irrigation process was conducted in the greenhouse of Fritillaria cirrhosa using the partition variable irrigation technology, according to the increment of irrigation soil moisture content u. The experimental results showed that a better water-saving effect of irrigation was achieved using fuzzy control optimized by GA. The increment of soil moisture content was distributed between 5% to 7%, and the consumption of irrigation water decreased significantly. The error of optimized variable irrigation was around ±5% under the specific planting density of Fritillaria cirrhosa, where satisfied the irrigation demand as required water amount of Fritillaria cirrhosa growth. The required irrigation water also increased linearly, with the increase of planting density of Fritillaria cirrhosa. Correspondingly, there was a parabolic relationship between the water-saving rate of partition variable irrigation and the planting density ratio of Fritillaria cirrhosa. The optimum water-saving rate was achieved, where the standard planting density and the annual water-saving rate reached more than 27.6%. This finding can provide a sound reference and technical support to the strategy of planting density and water-saving irrigation for Fritillaria Cirrhosa.

       

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