A crop real-time water consumption monitoring method and device based on machine vision

Abstract

The embodiment of the invention provides a crop real-time water consumption monitoring method and device based on machine vision. The coverage is obtained in a lossless image mode, the relation between the real-time crop coefficient and the coverage is obtained by means of professional lysimeter equipment, the method can be expanded to other planting fields to measure the real-time water storage capacity and crop coefficient of crops, and supports are provided for scientific research and production. Image recognition and artificial intelligence technologies such as an information image preprocessing technology, a segmentation technology, an image feature extraction and selection algorithm and the like are applied to monitor and obtain crop growth and physiological state information such ascrop leaf area, coverage degree and canopy temperature which are closely related to yield and quality, so that the water utilization efficiency is improved.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of ecological monitoring, and more specifically, to a method and device for monitoring real-time water consumption of crops based on machine vision. Background technique [0002] my country is a country identified by the United Nations as "water-scarce". Drought and water shortage have become the most important factor restricting China's economic and social development. In agricultural water, there is a serious waste phenomenon. In order to better promote the development of our country's economy, it is imperative to save water and rationally irrigate. [0003] The existing monitoring of crop water consumption information is mainly based on machine vision methods. For example, the Chinese invention patent application with the publication number CN 1895025A proposes "a new method for detecting crop water demand information based on machine vision". The images are captured by image acq...

AI Technical Summary

Problems solved by technology

[0004] The above image-based methods rely on the size of crop stalks or fruits and the individual information of crop growth for monitoring and judgment. Related monitoring has high requirements for measurement accuracy and high equipment requirements, making it difficult to popularize and apply; at the same time, individual information Whether it can represent group information will have an impact on the accuracy of irrigation guidance. The crop evapotranspiration is used to calculate the water demand of the crop. The crop coefficient is a fixed analog value. In fact, the crop coefficient is always changing in the crop growth environment. Use a fixed suggestion value, it will lead to a decrease in the accuracy of crop water demand; and in terms of coverage, in algorithms that need to use thresholds to segment crops and backgrounds, the selection of thresholds is extremely important. When the selection is inappropriate, the segmentation error will increase. When using an image, the background segmentation will have different segmentation thresholds due to changes in the environment and the crop itself. The selection of the threshold is extremely complicated, and the selection of the threshold will also affect the segmentation effect, which in turn affects the judgment of the actual water demand of the crop.

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