Method for identifying lodging regions of wheat in multiple growth periods based on transfer learning

Abstract

The invention relates to the technical field of image recognition, in particular to a method for identifying lodging regions of wheat in multiple growth periods based on transfer learning, and the method comprises the following steps: A, shooting an RGB image and / or a multispectral image of a to-be-recognized wheat field; b, splicing and cutting the images to obtain a complete image of the wheat field to be identified; and C, importing the complete image of the wheat field to be identified into the trained DeepLabv 3 + model to identify the lodging area. The method is based on a DeepLabv 3 + network model. two methods are constructed by adopting a transfer learning mode to realize extraction of lodging regions of wheat in multiple growth periods; based on unmanned aerial vehicle images anda transfer learning method, lodging wheat characteristics in multiple periods can be effectively obtained, high-precision wheat area automatic extraction is achieved, it is possible to accurately detect a wheat lodging area, and powerful data support is provided for researching wheat lodging influence factors. The method is little affected by the environment and convenient to implement, and afterthe DeepLabv 3 + model is trained, the lodging area can be automatically recognized only by shooting the image of the wheat field to be recognized and importing the image into the model.

Description

technical field [0001] The invention relates to the technical field of image recognition, in particular to a method for identifying lodging areas of wheat in multiple growth stages based on transfer learning. Background technique [0002] As one of the most important food crops in the world, wheat is often prone to large-scale lodging due to natural disasters such as pests and diseases, floods, excessive planting density, and excessive nitrogen fertilizer during cultivation and management. When lodging occurs at any stage of wheat growth, it will significantly reduce the yield and quality of wheat, which poses a huge potential hidden danger to the healthy production of wheat worldwide. Therefore, it is of great value and significance to study the non-destructive monitoring method or technology of wheat lodging for the stable production of global grain. [0003] In the nondestructive remote sensing monitoring of crop lodging, there are mainly three methods: near-ground, airb...

AI Technical Summary

Problems solved by technology

Bouman et al. studied the backscatter characteristics of lodging wheat using DUTSCAT airborne scatterometer data and found that for all wheat lodging angles lodging resulted in an increase in average radar backscatter

However, the cost of using the man-machine remote sensing platform is too high to be widely used

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