Plant pest and disease detection method based on multiple features and support vector machine

Abstract

The invention discloses a plant pest and disease detection method based on multiple features and a support vector machine, including the specific steps of preprocessing a plant image collected by an unmanned aerial vehicle, extracting the color feature, HSV feature, texture feature and shape feature of the image, and inputting the features to a support vector machine to detect possible pests and diseases in the process of growth. The plant pest and disease image is detected by combining multi-feature fusion and the support vector machine. For different plants and different pests and diseases, plant pest and disease detection can be carried out using the method. Moreover, the method is real-time, accurate and practical.

Description

technical field [0001] The invention relates to the technical fields of image processing and machine learning, in particular to a method for detecting plant diseases and insect pests based on multi-features and support vector machines. Background technique [0002] my country is a large agricultural country where natural disasters, especially crop diseases and insect pests, occur frequently. There are many types, high frequency and great harm of crop diseases and insect pests, which are important restrictive factors for increasing agricultural production and improving the quality of agricultural products. The diagnosis and treatment of plant diseases and insect pests is related to the safety of agricultural production, the quality and safety of agricultural products, and the safety of the ecological environment, so the research on the detection methods of plant diseases and insect pests has very important theoretical value and practical significance. [0003] As an interdisc...

AI Technical Summary

Problems solved by technology

The traditional detection of plant diseases and insect pests is done by farmers or inspectors based on actual planting experience or looking at photos. Although it has a high recognition rate and recognition speed, it is costly, and the manual workload is large and the coverage is insufficient. Therefore, it is automated. Detection is of great significance to standardization and standardized management

The second method is chemical detection. Chemical detection is mainly used in laboratories. It uses the chemical composition analysis of plant leaves to identify pests and diseases. It has the advantages of accuracy and reliability. However, due to expensive detection equipment, complicated operation, difficult installation and debugging, and long detection time, it is difficult It is not suitable for ordinary farmers and has not been widely used

Spectrum can directly reflect the internal structure and motion state of molecules, with strong characteristics and high sensitivity, and is widely used in industry, agriculture and scientific research, but this technology is easily affected by environmental moisture

The fourth method is image processing technology detection. Image processing technology detects whether there are diseases and insect pests by analyzing the color and texture of plant leaf images. Satisfactory detection accuracy

[0007] The above detection methods of plant diseases and insect pests are relatively common and effective methods. The above methods have solved the problem of diagnosis and treatment of plant diseases and insect pests to a certain extent. Efficient implementation poses great difficulties, and its usefulness and accuracy remain unsatisfactory

For different environments, light, and types of pests and diseases, the detection results vary widely

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