Exploration of data mining technology for remote control and integration of mechanical equipment in smart agriculture

Regarding the issues of insufficient control accuracy and slow response speed of remote control methods for mechanical equipment in traditional smart agriculture, a remote control system was built using depth cameras and network cameras. K-nearest neighbor algorithm and fuzzy density optimization density peak clustering were used to extract crop information, and particle swarm optimization algorithm was applied to improve support vector machine detection of crop anomalies. A remote control system based on data mining technology was designed. The results showed that the accuracy of the proposed FD-KNN-DPC clustering algorithm for crop information extraction reached 94.8%, which was superior to the compared K-means, DPC, and KNN algorithms. For crop anomaly detection, the proposed PSO-SVM classification algorithm achieved accuracy, recall, and F1 score of 94.9%, 95.4%, and 94.8%, respectively, with significantly better overall performance than other compared algorithms. The highest ground recognition accuracy of the design system could reach 98.3%, and the lowest average detection time was 13.8 seconds, which could improve work efficiency. The remote control system for agricultural machinery and equipment proposed by the research has good control effects and can accelerate the modernization of agriculture.