Pest disaster monitoring method based on machine vision

Abstract

The invention relates to a pest disaster monitoring method based on machine vision. The method includes the steps of installing a pest inducing device at a pest gathering place, and arranging an imagecollecting device facing the pest inducing device to collect images; identifying pests in the collected images and obtaining the number of the pests; if the number of the pests is greater than or equal to a preset threshold value for the number of the pests, separately extracting all the areas where the pests are located from the images to serve as suspected images of the pests, and judging the identification accuracy of each suspected image of the pests; according to the number of the pests and the identification accuracy of each suspected image of the pests, working out the pest disaster prediction level. By arranging the image collecting device facing the pest inducing device to automatically collect the images of the pests, large consumption of time and labor in manual visual inspection can be avoided, and the pests can be monitored in real time; by combining the number of the pests and the identification accuracy of each suspected image of the pests, the accuracy of calculating the pest disaster prediction level is higher, the obtained result is more significant, and the instructiveness in prevention and control of the pests is improved.

Description

technical field [0001] The invention relates to the field of pest monitoring, in particular to a machine vision-based pest monitoring method. Background technique [0002] In recent years, the situation of insect pests has been severe in some areas of our country and has caused serious losses. As pests are the main media of insect pests, pest control is considered to be the key to controlling pests and diseases. In particular, for citrus orchards planted in large areas in the south, Huanglongbing caused by citrus psyllids as a vector has seriously endangered the normal operation of citrus orchards, greatly affecting the output and quality of citrus fruits. At present, in the process of implementing the prevention and control of citrus huanglongbing disease in various regions, the regularity of the occurrence of pests caused by citrus psyllids is mainly manually observed, and the degree of occurrence of pests is also predicted manually. Migration affects the accuracy of moni...

AI Technical Summary

Problems solved by technology

In particular, for citrus orchards planted in large areas in the south, Huanglongbing caused by citrus psyllids as a vector has seriously endangered the normal operation of citrus orchards, greatly affecting the output and quality of citrus fruits

At present, in the process of implementing the prevention and control of citrus huanglongbing disease in various regions, the regularity of the occurrence of pests caused by citrus psyllids is mainly manually observed, and the degree of occurrence of pests is also predicted manually. Migration affects the accuracy of monitoring data

Other traditional pest monitoring methods, such as monitoring with traps, are still not ideal in terms of accuracy and timeliness. They have poor guidance for orchard pest control, high cost of control, and poor results.

Images