Automatic detection method of wheat seedling emergence

Abstract

The invention provides an automatic detection method of wheat seedling emergence, comprising the following steps: a wheat image is acquired through segmenting a wheat field image acquired by using a camera in real time; connected domain identification is performed on the wheat image, and centers of mass of connected domains are marked; the seeding direction of the wheat is identified according to the wheat image, if the seeding direction is a horizontal direction, the wheat image is divided into a plurality of subsections in a vertical direction, and if being a vertical direction, the wheat image is divided into a plurality of subsections from a horizontal direction; and the number of the center of mass of the connected domains in each subsections is counted. If the number of the center of mass of the connected domains is greater than a predetermined subsection seedling emergence determining threshold value then the corresponding subsection is determined to be a seedling emergence subsection. If the number of the seedling emergence subsections is greater than an image seedling emergence determining threshold value then the wheat in the wheat image are in a period of seedling emergence. The method can determine the growth period of the wheat in real time with high accuracy of detection results, and has important guiding significance for all kinds of farming activities of the wheat.

Description

technical field [0001] The invention belongs to the cross field of computer vision and agricultural meteorological observation, and in particular relates to an automatic detection method for wheat emergence, that is, a method for detecting whether wheat emerges from the image features by taking the photographed field wheat image sequence as the object. Background technique [0002] Wheat is the second largest grain crop in the world, and it is widely grown in Northeast my country, North China, East China and other regions. The entire development period, yield and quality of wheat are vulnerable to climate change. For a long time, the observation of each developmental stage of wheat is mainly through manual observation, which is greatly affected by the subjective factors of the observer; at the same time, due to the wide planting area and long growth cycle of wheat, manual observation is not economical enough, and there is no way to guarantee it. Accuracy. Therefore, it is ...

AI Technical Summary

Problems solved by technology

The above monitoring of wheat yield and growth status was determined using remote sensing data. However, due to the low resolution of remote sensing images and the fact that they are easily affected by clouds, cloud shadows, and aerosols, only a single image can be used in a fixed area every day. have greater limitations

It can be seen that the detection method based on remote sensing images is not a good choice for the automatic detection of wheat seedling emergence. At present, a method with high accuracy, strong practicability and convenient operation is needed to replace the manual detection method at the emergence stage to obtain wheat seedling emergence. Accurate time, easy to guide farming activities in time

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