Research on Plant Feature Extraction and Intelligent Recognition System Based on Computer Vision

This paper proposes a plant feature extraction and intelligent recognition system based on computer vision. By collecting and analyzing plant images, the system can efficiently extract multidimensional features of plants and perform intelligent recognition. The core of the system includes the design and model simulation of image processing algorithms, combined with deep learning technology, to automatically extract plant leaf morphology, color, texture and other features, so as to establish a plant recognition model. To increase the precision and robustness of identification, CNN is adopted in this paper, and SVM is used to classify and recognize the feature. Simulation results indicate that the recognition precision of this system is more than 90% on the standard dataset. Moreover, it can keep its high recognition capability under complicated circumstances (for example, illumination, background disturbance and noise). For further verification, a lot of experiments are carried out, and the optimization algorithm is compared with the conventional one. The results show that the optimized algorithm has significantly improved the speed and accuracy of feature extraction, especially on large-scale data sets.

I. Introduction

With the continuous development of computer vision technology, the application of intelligent recognition systems in various fields has received widespread attention. Especially in botanical research and agricultural production, the demand for plant feature extraction and automatic identification is increasing. Plant feature extraction and recognition technology based on computer vision can extract valuable features from plant images through digital image processing, and then perform intelligent classification and recognition. This provides strong technical support for plant species detection, pest and disease identification, agricultural monitoring and other fields.