Diagnosis method and device of illness state severity of wheat scab in field environment

Abstract

The invention particularly relates to a diagnosis method of the illness state severity of wheat scab in a field environment. The method comprises the following steps that (A) 45-degree-angle images ofwheat ears infected with the wheat scab in a wheat field are collected; (B) a field wheat ear dividing model is used for dividing the disease images to obtain wheat ear areas; a wheat scab dividing model is used for dividing the disease images to obtain disease scab areas; (C) a concave point matching method is used for dividing adhesion areas of the wheat ears and disease scabs to obtain the area of each wheat ear and the disease scab area of the corresponding wheat ear in the wheat field; (D) the ratio R of the disease scab area of each wheat ear to the area of the corresponding wheat ear is calculated, it is judged whether or not R is greater than a set threshold, and if yes, it is determined that the wheat ear is a disease ear; (E) the ratio X of the quantity of the disease ears to the total quantity of the ears in the wheat field is calculated, and the disease states are graded according to the ratio X. The invention also discloses a corresponding detection device. The method hasthe advantages that the diagnosis of the disease state of the wheat scab in the field environment can be achieved, the diagnosis speed is quick, and the accuracy is high.

Description

technical field [0001] The invention relates to the technical field of detection of wheat scab, in particular to a method and device for diagnosing the disease severity of wheat scab in a field environment. Background technique [0002] As a traditional agricultural country, wheat occupies a very important position in my country's food security. As one of the main diseases of wheat, head blight is particularly serious in the Jianghuai River Basin in my country. Due to the inability to accurately judge the severity of the disease, it often leads to excessive application of pesticides, which causes great harm to the agricultural ecological environment and seriously affects the national grain. Safety and food safety, therefore, it is particularly important to study wheat scab disease classification methods and diagnostic devices. [0003] At present, the diagnosis of wheat head blight mostly adopts artificial visual judgment, spectral diagnosis, conventional image processing an...

AI Technical Summary

Problems solved by technology

As one of the main diseases of wheat, head blight is particularly serious in the Jianghuai River Basin in my country. Due to the inability to accurately judge the severity of the disease, it often leads to excessive application of pesticides, which causes great harm to the agricultural ecological environment and seriously affects the national grain. Safety and food safety, therefore, it is particularly important to study wheat scab disease classification methods and diagnostic devices

[0003] At present, the diagnosis of wheat head blight mostly adopts artificial visual judgment, spectral diagnosis, conventional image processing and other methods. These methods have some shortcomings: artificial visual judgment is greatly affected by human subjectivity; The requirements are relatively high, which is not suitable for ordinary plant protection personnel or farmers; conventional image processing techniques for diagnosing wheat head blight are few and most of them are traditional methods, and most of them are in the theoretical stage and have not been applied in the field

More importantly, most of the current detection methods for scab are based on destructive collection of wheat ears. This detection method is inefficient, and there is currently no good detection method for field groups of wheat ears.

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