Design of an Automatic Agricultural Machinery Signage Recognition System Based on Computer Vision

【Objective】 This study aims to design an automatic agricultural machinery identification system based on computer vision, to address the issue of disorganized and chaotic labeling of some agricultural machinery products in the current market, thereby enhancing user recognition efficiency and corporate brand image. 【 Methods: 】High-resolution cameras were used to capture images of agricultural machinery under various lighting and weather conditions. Image quality was enhanced through techniques such as histogram equalization and adaptive contrast enhancement. Convolutional Neural Networks (CNN) were utilized for feature extraction and classification recognition. The system architecture integrates an image processing module, feature extraction module, classifier module, and user interface to ensure overall system efficiency and user-friendliness. 【Conclusion and Results】 The results indicate that the system can accurately identify agricultural machinery labels in complex environments, significantly improving recognition accuracy and speed, and meeting the needs for real-time processing. The design method based on visual impact effects has significant application value in the identification of agricultural machinery labels, effectively enhancing the market competitiveness and user satisfaction of agricultural machinery products.