Plant protection fog drop detection method based on HSV color model

Abstract

The invention relates to the field of pesticide spraying effect detection, and particularly discloses a plant protection fog drop detection method based on an HSV color model, which comprises the following steps: firstly, extracting an R channel image of a fog drop picture, processing the R channel image by using an HSV color model masking method, filtering redundant background information, binarizing to obtain a fog drop contour binary image, extracting the fogdrop contour after morphological denoising and opening operation denoising, finally, calculating the fogdrop contour surrounding shapearea, filtering redundant interference information, and acquiring a fogdrop detection result. According to the method, R three channels are combined, the HSV color model is used for generating the mask map, the mask method is used for detecting the fog drops, and important technical indexes such as the fog drop number pitch diameter, the volume pitch diameter, the deposition density and the deposition coverage rate for measuring the pesticide spraying effect can be accurately counted on the water-sensitive paper with dense fog drops. The detection process is simple.

Description

technical field [0001] The invention relates to the field of pesticide spraying effect detection, and specifically discloses a plant protection fog drop detection method based on an HSV color model. Background technique [0002] In the process of plant growth, pesticides need to be sprayed to protect plants from pests. At this time, it is necessary to judge the effect of pesticide spraying. The commonly used judging method is to collect the droplets first, then detect the droplets, calculate the droplet deposition density, droplet deposition coverage and droplet size spectrum, and then make a judgment. The existing droplet detection methods mainly include laser method, mechanical method, and image method. The laser method is more convenient to operate, but the cost of the measuring instrument is expensive, and the detection conditions are relatively strict, which has certain limitations; for the mechanical method, For example, the oil pan method requires a lot of manual ope...

AI Technical Summary

Problems solved by technology

The existing droplet detection methods mainly include laser method, mechanical method, and image method. The laser method is more convenient to operate, but the cost of the measuring instrument is expensive, and the detection conditions are relatively strict, which has certain limitations; for the mechanical method, For example, the oil pan method requires the operator to do a lot of manual operations. After collecting the droplets, it needs to be measured, calculated, and corrected through a microscope. The operation process is complicated and the accuracy is very low; the image method can be divided into traditional image method and based on In the image method of deep learning, the traditional image method cannot remove the background information well when extracting the image of the fog droplets, and it is easy to introduce the background to cause detection errors. The method based on deep learning is cumbersome in implementation and requires pre-training of the network model Then use the model to detect, the detection process is slow and prone to false detection

The above-mentioned laser method, mechanical method and image method cannot accurately and quickly analyze the fog droplets in some special scenes

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