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Smart Agriculture ›› 2021, Vol. 3 ›› Issue (3): 116-128.doi: 10.12133/j.smartag.2021.3.3.202104-SA001

• 智能管理与控制 • 上一篇    下一篇

基于Penman-Monteith模型和路径排序算法相结合的草莓灌溉方法与验证

张宇1,2(), 赵春江1,2, 林森2(), 郭文忠2, 文朝武2, 龙洁花2   

  1. 1.吉林农业大学 信息技术学院,吉林 长春 130118
    2.北京农业智能装备技术研究中心,北京 100097
  • 收稿日期:2021-04-07 修回日期:2021-10-04 出版日期:2021-06-30 发布日期:2021-12-06
  • 基金资助:
    北京市科技计划(Z211100004621006);北京市农林科学院青年基金(QNJJ202027);宁夏回族自治区重点研发计划项目(2018BBF02024)
  • 作者简介:张 宇(1997-),男,硕士研究生,研究方向为知识图谱研究。E-mail: 435542515@qq.com
  • 通讯作者: 林森 E-mail:435542515@qq.com;linseng@nercita.org.cn

Irrigation Method and Verification of Strawberry Based on Penman-Monteith Model and Path Ranking Algorith

ZHANG Yu1,2(), ZHAO Chunjiang1,2, LIN Sen2(), GUO Wenzhong2, WEN Chaowu2, LONG Jiehua2   

  1. 1.College of Information Technology, JiLin Agriculture University, Changchun 130118, China
    2.Beijing Agricultural Intelligent Equipment Technology Research Center, Beijing 100097, China
  • Received:2021-04-07 Revised:2021-10-04 Online:2021-06-30 Published:2021-12-06
  • corresponding author: LIN Sen E-mail:435542515@qq.com;linseng@nercita.org.cn

摘要:

灌溉是影响作物产量的重要因素。为更加有效、精确地控制设施作物的灌溉,本研究以“章姬”草莓为例,将作物实时生长特征引入灌溉决策模型中,将Penman-Monteith(P-M)模型和知识推理相结合对草莓的灌溉展开研究。首先明确影响草莓灌溉的因子和影响系数,然后建立“章姬”草莓灌溉知识结构和草莓灌溉知识图谱,接着应用路径排序算法(Path Ranking Algorithm,PRA)对P-M模型计算的灌溉值进行调整,实现草莓的精准灌溉。知识推理中每个专家的灌溉调整策略都不相同,本试验以草莓产量最大为目标,选择概率值最高的一组灌溉推理值对灌溉进行调整。试验结果表明,在规定时间采收的情况下,本研究提出的基于Penman-Monteith模型和路径排序算法相结合的方法比传统P-M模型方法的果实总产量、单株果实均产量和果实均重百分比分别提高2478.5g、20.65g和12.15%(单个果实均重提高1.65g),硬度提升了0.1 kg/cm2。表明该方法根据作物生长状态对作物灌溉进行调整合理,为精确灌溉提供了新的思路。

关键词: 人工智能, 知识图谱, 知识推理, 精准灌溉, 路径排序算法, 草莓, Penman-Monteith

Abstract:

Irrigation is an important factor that affects crop yield. In order to control irrigation of facility crops more effectively and accurately, this study took "Zhangji" strawberry as an example, introduced crop real-time growth characteristics into irrigation decision-making, and combined Penman-Monteith (P-M) model and knowledge reasoning to study the irrigation of strawberry. In the first step, the influencing factors and expert experience in identifying strawberry growth period of "Zhangji" strawberry irrigation were standardized, and the strawberry irrigation data structure based on Resource Description Framework (RDF) was established. The second step was to collect expert experience of strawberry irrigation according to the standardized knowledge structure model. Firstly, all data were unified into structured data, and then were stored in *.csv format together with expert experience, and strawberry irrigation knowledge map based on Neo4j was constructed. The third step was to collect the environmental data and plant data of strawberry in each growth period. The fourth step was using P-M model to calculate the initial irrigation value of strawberry, and then adjusted the initial irrigation value by knowledge reasoning.The fifth step was to conduct experimental planting and evaluate the sampled fruits. In knowledge reasoning, irrigation adjustment strategies of each expert was different. In strawberry irrigation experiment based on P-M model and path sorting algorithm, a group of irrigation reasoning values with the highest probability value were selected to adjust irrigation with the goal of maximizing strawberry yield. The experimental results showed that under the condition of harvesting at a specified time, The total fruit yield, average fruit yield per plant and average fruit weight percentage increased by 2478.5 g, 20.65 g and 12.15% (average fruit weight increased by 1.65 g per fruit) based on P-M model and path sorting algorithm compared with traditional P-M model, respectively. First, on the basis of P-M model, the yield-first irrigation adjustment strategy was adopted. Based on knowledge reasoning, the irrigation frequency and amount were adjusted timely according to the crop growth situation, which improved the yield. Second, under the condition of harvesting and recording yield at a specified time, the experiment accurately controlled the growth period to ensure early fruit ripening, while the other three groups of fruits were not fully mature and the yield of immature fruits were not calculated. Under the method of strawberry irrigation based on Penman-Monteith model and path sorting algorithm, the fruit was picked within a fixed time and reached 0.39 kg/cm2, which increased by 0.1 kg/cm2. Because the planting goal of this study was yield first, only the influence of irrigation on yield was considered. The experimental resulted show that the irrigation method based on model and knowledge reasoning could improve the yield of strawberry, and can provide a new idea for precise irrigation.

Key words: artificial intelligence knowledge graph, knowledge reasoning, precise irrigation, path ranking algorithm, strawberry, Penman-Monteith

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