Field wheat aphid counting method

Abstract

The invention discloses a field wheat aphid counting method. The method comprises the following steps of image acquisition: when wheat is grown into a booting stage from a jointing stage, acquiring a wheat-plant original image through a digital camera, acquiring X small pixel subimages in the original image and taking as a training sample; training sample collection: randomly intercepting N aphid images from the original image and taking as a positive sample, and randomly intercepting M images and taking as a negative sample; identification model training: extracting HOG characteristics of the positive sample and the negative sample, inputting the HOG of the positive sample and the negative sample into a SVM classifier to carry out training so as to obtain an insect pest classification model, using the samples to rectify the model and verifying an insect pest identification model; aphid detection: searching a MSER in the original image and finally using the SVM classifier to search aphids in a MSER area and record an aphid quantity. Operation is simple and aphid quantity calculating is high-efficient and accurate.

Description

technical field [0001] The invention relates to a counting method, in particular to a field wheat aphid counting method. Background technique [0002] Insect pests are a major factor affecting the growth of crops. Aphids are frequent and reoccurring pests in wheat production. When they harm wheat, they not only suck plant juice, affect the development of wheat, but also spread a variety of viral diseases. Accurately estimating pest density is the basis for pest prediction and forecasting. Timely acquisition of pest density is the basis for analyzing pest population dynamics and pest damage, and is also a necessary condition for obtaining the economic threshold of pest control. At present, the more common method of counting aphids is manual counting. Due to the small size of aphids, long-term visual observation is not only labor-intensive and inefficient, but also the counting accuracy is affected by subjective factors. At the same time, there is also a semi-automatic method...

AI Technical Summary

Problems solved by technology

[0003] Due to the small size of aphids in manual counting, long-term visual observation is not only labor intensive and inefficient, but also the counting accuracy is affected by subjective factors; while the specific board counting operation is cumbersome and it is difficult to induce all aphids to the board, There is a large error

Images