| 基于知识图谱与案例推理的水稻精准施肥推荐模型 |
| |
| 引用本文： | 戈为溪,周俊,袁立存,郑彭元.基于知识图谱与案例推理的水稻精准施肥推荐模型[J].农业工程学报,2023,39(2):126-133. |
| |
| 作者姓名： | 戈为溪 周俊 袁立存 郑彭元 |
| |
| 作者单位： | 南京农业大学工学院，南京 210031 |
| |
| 基金项目： | 国家重点研发计划项目（2019YFD0900705）；江苏省现代农机装备与技术示范推广项目（NJ2020-61） |
| |
| 摘 要： | 目前水稻种植户的施肥行为存在一定的盲目性，会造成肥料浪费和环境污染等问题，对此该研究提出了一种基于知识图谱和案例推理的水稻精准施肥推荐模型，包括推荐定性的施肥方案和定量的施肥量两个阶段。首先，使用PairRE模型获取图谱中全部实体和关系的低维向量表示，并在此基础上依据待种植的水稻品种进行知识推理以得到定性的施肥方案；然后，结合实体向量检索出k个相似案例，通过k个案例进行组合预测，得出具体施肥量数值。由中国知网获取166个环境指标数值明确、施肥过程记录完整的水稻施肥事件用于模型的验证，结果表明，与测试事件的实际施肥方案相违背的部分仅占比10.76%；对于氮肥施用总量、磷肥施用总量、钾肥施用总量和氮肥基叶肥与穗肥运筹比例的预测精度分别达到了92.85%、82.61%、79.17%和90.92%。该施肥推荐模型能够输出详细的施肥方案和精确的施肥量，算法过程可解释性较强，可为水稻精准施肥推荐系统的设计提供支撑。
|
| 关 键 词： | 水稻 施肥 模型 知识图谱 案例推理 智能推荐 |
| 收稿时间： | 2022/11/17 0:00:00 |
| 修稿时间： | 2023/1/12 0:00:00 |
Recommendation model for rice precision fertilization using knowledge graph and case-based reasoning |
| |
| GE Weixi,ZHOU Jun,YUAN Licun,ZHENG Pengyuan.Recommendation model for rice precision fertilization using knowledge graph and case-based reasoning[J].Transactions of the Chinese Society of Agricultural Engineering,2023,39(2):126-133. |
| |
| Authors: | GE Weixi ZHOU Jun YUAN Licun ZHENG Pengyuan |
| |
| Institution: | College of Engineering, Nanjing Agricultural University, Nanjing 210031, China |
| |
| Abstract: | Abstract: Fertilizer management in rice fields can play a very important role in rice yield and quality. However, blind fertilization has caused a series of problems, such as soil consolidation, fertilizer waste, and environmental pollution. As a result, it is a high demand to automatically make the fertilization plan, according to the rice growth cycle. There are also some defects in the current agricultural planning using mathematical models, domain empirical knowledge, and statistical artificial intelligence (AI) models. In this study, a novel recommendation model of rice precision fertilization was proposed to optimize the formulation of a rice fertilization plan using a knowledge graph and case-based reasoning. A qualitative fertilization scheme was also provided for the specific application of different kinds of fertilizers. In the knowledge graph, the information was first retrieved to obtain the initial fertilization scheme, according to the cultivated rice variety. The types of information were then compared between the initial fertilization scheme and the required recommended one, in order to obtain the missing types in the initial scheme. Secondly, the PairRE model was used to determine the distributed low-dimensional vectors of all entities and relations in the knowledge graph. The missing part of the rice fertilization scheme was then predicted using these entity and relation vectors. Finally, the predicted results were presented as a fertilization scheme, together with the initial one. In case-based reasoning stage, the K historical cases were retrieved from the most similar to the event to be recommended, according to the environmental parameters, the cultivated rice variety, the yield level of the event to be recommended, the parameters of historical cases, and the vectors of entities in the knowledge graph. The K cases were also used for the specific application in the different kinds of fertilizers through combination prediction. In the model verification, the average proportion of fertilization recommendation contrary to the actual was only 10.76% in different test events, particularly in the part of a qualitative acquisition in the fertilization scheme. The prediction accuracy of the nitrogen (N), phosphorus (P2O5), and potassium (K2O) application amount, as well as ratio of basal-tillering nitrogen application amount to panicle nitrogen application amount, reached 92.85%, 82.61%, 79.17%, and 90.92%, respectively. Correspondingly, Spearman''s rank coefficients were 0.69, 0.76, 0.74, and 0.72, respectively, for the correlation between the predicted and the actual values in the four indexes. A comparison experiment was then performed on the commonly-used recommended fertilization using rule-based reasoning. Consequently, the improved model can be expected to ensure the accuracy of the rice fertilization scheme. A more complete fertilization scheme and specific fertilizer application amount were also provided in the higher practical value. In summary, the developed recommendation model of rice precision fertilization using case-based reasoning can be the key supplement in the fertilization recommendation using a knowledge graph. As such, the recommendation system was effectively improved using a knowledge graph, in order to fully use data for the numerical prediction. The improved model has strong interpretability and high accuracy, particularly for the integrity of the fertilization scheme and fertilizer application amount. The finding can provide a strong reference for the formulation of a rice fertilization plan. |
| |
| Keywords: | rice fertilization models knowledge graph case-based reasoning intelligent recommendation |
|
| 点击此处可从《农业工程学报》浏览原始摘要信息 |
| 点击此处可从《农业工程学报》下载免费的PDF全文 |
|
