融合SVR和K-means聚类算法的智慧农业大棚智能灌溉研究

Research on intelligent irrigation of intelligent agricultural greenhouse based on SVR and K-means clustering algorithm

当前的智能灌溉技术对灌溉情况的预测精度较低,导致灌溉效率低下,造成水资源浪费。为此,研究提出一种融合支持向量回归(Support Vector Regression,SVR)和K-means的智能灌溉方法。首先,针对SVR存在的缺陷,提出一种改进乌鸦搜索算法对其进行优化;其次,针对K-means算法存在的缺陷,提出了优化策略,确定算法的最佳K值。结合上述内容,构建了智能灌溉模型。结果显示,该模型的准确率超过98%,AUC值达到0.992。上述结果表明研究提出的模型具有较高的精度,能够有效实现智能灌溉,从而提高灌溉效率,降低水资源浪费,对我国农业的发展有促进作用。

The current intelligent irrigation technology has low prediction accuracy for irrigation conditions,resulting in low irrigation efficiency and waste of water resources.Therefore,an intelligent irrigation method combining support vector regression(SVR) and K-means is proposed.First,aiming at the defects of SVR,an improved crow search algorithm is proposed to optimize it;Secondly,aiming at the defects of K-means algorithm,an optimization strategy is proposed to determine the optimal K value of the algorithm.Combining the above contents,an intelligent irrigation model is constructed.The results show that the accuracy of the model is more than 98%,and the AUC value reaches 0.992.The above results show that the model proposed in the study has high accuracy and can effectively realize intelligent irrigation,thus improving irrigation efficiency,reducing water resource waste,and promoting the development of agriculture in China.