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With the rise and development of precision agriculture and smart agriculture concepts, traditional agricultural pest detection and identification methods have become increasingly unable to meet current agricultural production requirements due to their slow recognition speed, low recognition accuracy, and strong subjectivity need. This article aims to combine multifeature fusion technology with sensors to apply to crop pest detection and build crop pest detection services based on image recognition. In terms of image recognition, the use of image denoising methods based on median filtering, image preprocessing methods based on the maximum between-class error method (Otsu), image segmentation methods based on super green features, and feature extraction methods based on multiparameter features and based on the one-to-one elimination strategy and the M-SVM multiclass recognition algorithm fused with the kernel function, it realizes the identification and detection of six soybean leaf borers. The system uses the ARM920T series S3C2440 chip as the central processing unit. Through the temperature and humidity sensor and infrared, the multisensor module composed of sensors collects real-time information on the agricultural greenhouse. After normalizing the information, the central processing unit performs judgment processing and information fusion. And through experimental data, it is finally verified that the image recognition method used in this paper improves the recognition rate and effectiveness by nearly 65% in the detection of soybean leaf moth pests. |
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