Breeding monitoring method and device

Abstract

The invention provides a cultivation monitoring method. The cultivation monitoring method comprises the following steps: acquiring image information from two or more image collection devices with adjacent positions at the current moment, wherein the image information can be provided with a monitoring area feature identifier by the image collection device from which the image information is sourced; a network model is established, and the method specifically comprises the following steps: preprocessing image information from each image collection device, and then obtaining splicing feature dataof an area based on the preprocessed image information; performing weighted summation on the splicing feature data from the adjacent regions to obtain spatial fusion feature data; outputting a corresponding detection result based on the spatial fusion feature data; and carrying out detection calculation by using the network model at preset time intervals, and optimizing a detection result. The invention also provides a culture monitoring device for realizing the method, so that on the premise of ensuring the monitoring speed and stability, the quality detection accuracy and robustness are more reliable, and the monitoring effect of the kelp culture monitoring system is further promoted.

Description

technical field [0001] The invention relates to the field of computer software, in particular to a method for monitoring cultured animals based on a multi-scale spatio-temporal feature fusion image classification algorithm. Background technique [0002] China is a country with a large-scale aquatic product industry. Among them, the kelp industry is the largest in the world, and China's kelp production accounts for more than 90% of the world's kelp production. Kelp is a nutritious edible brown algae that contains more than 60 nutrients, making it an ideal natural sea food. The growth of kelp is affected by various environments such as water temperature, water depth, and water quality, so the quality of kelp production cannot be fully guaranteed. Even the quality of kelp cultivated in the same water area is often uneven. In addition, fish infestation in the waters can also have a destructive effect on the growth of kelp. [0003] The quality of kelp is generally judged by it...

AI Technical Summary

Problems solved by technology

After multiple downsampling operations, the general image classification algorithm is often easy to lose some information such as location and details, which is reflected in the kelp quality detection task, which may cause the spot information to be ignored

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