A kind of rice sheath blight damage identification method based on image technology

Abstract

The invention discloses an image technology-based rice sheath blight hazard identification method. According to the method, a ruler rod for calibration is designed and comprises a rod body; red, green and blue standard color cards are arranged on the rod body and used for performing three-channel calibration on a collected image; the method is adopted for collecting a field rice image and used for hazard identification; and the collected image is calibrated by adopting an image enhancement method, then feature extraction of the rice sheath blight image is performed, and hole severity level division of the rice sheath blight image is performed: according to an obtained disease condition index value, the rice sheath blight is identified to be in a disease level. The method is simple and clear; the labor intensity is greatly reduced; the working efficiency is improved; and the disease condition can be reflected timely, accurately, quickly and comprehensively.

Description

technical field [0001] The invention relates to a method for classifying plant diseases, in particular to a method for identifying rice sheath blight damage based on image technology. Background technique [0002] Rice is one of the most important food crops in my country. At present, my country's rice disease control has always adhered to the "Integrated Management (IPM)" plant protection policy, based on monitoring and forecasting, comprehensively applying agricultural, biological, physical control and chemical control and other technical measures to effectively control disease damage. Accurate and timely monitoring of rice field disease information (occurrence type, occurrence time and occurrence quantity) is the prerequisite for the implementation of IPM of rice diseases, the key to accurate disease prediction and forecast, and the necessary condition for the implementation of precision agriculture. [0003] Rice sheath blight is the first of the three major diseases of...

AI Technical Summary

Problems solved by technology

[0004] At present, the prediction of rice sheath blight in my country is mainly based on the method of sampling survey and visual inspection by agricultural technicians, through the continuous investigation of the diseased plant rate and diseased hole rate in the systematic field and field, combined with the rice variety, growth period, weather, etc. Conditions, fertilizer and water management and other factors are used to analyze and predict the occurrence and development trend of diseases to estimate the occurrence of diseases, ignoring the role of disease point severity (spot height) in forecasting; in addition, random The degree of variability and subjectivity of the survey results is determined by the technical level and experience of different investigators, which leads to low prediction accuracy and affects the development of prevention and control work.

[0005] In fact, sampling surveys and visual estimates often cannot give accurate values ​​of the location, area, and grade of sheath blight, and sampling surveys require a lot of manpower and time, and visual estimates will cause large errors, resulting in rice. The accuracy of sheath blight prediction and control guidance is not high

In addition, the grassroots plant protection system is not sound enough, the strength is weak, the means are backward, the technical ability is poor, the investigation tasks are heavy, and the low-efficiency visual estimation method can no longer meet the development needs of modern agriculture.

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