Field rice ear segmentation method based on depth learning

Abstract

The invention discloses a field rice ear segmentation method based on depth learning. In this method, a depth-total convolution neural network model was designed for segmentation of rice panicles. Thefirst half of the network uses ResNet-101 layers is provided with the Squeeze and Excitation Module structure to filter the importance of the feature layer. All conventional convolution layers in theoriginal ResNet-101 network module 4 and module 5 are replaced with void convolution layers, and the step size is changed from 2 to 1. The second half of the network adopts hollow pyramid pooling andpyramid pooling structure. The method can overcome the great differences of rice ear color, shape, size, posture and texture in different varieties and growth stages, and can realize accurate segmentation of rice ear in different varieties and growth stages. The method can also overcome the influence of irregular edge of rice ear, leaf color aliasing, uneven and changing illumination, shielding and wind in the field. Compared with the prior art, the method has the technical advantages of high precision and strong applicability.

Description

technical field [0001] The invention belongs to the field of agricultural automation, and in particular relates to automatic measurement of rice phenotypic parameters, in particular to a deep learning-based method for segmenting rice ears in field. Background technique [0002] The production and distribution of rice is related to the food security of more than half of the world's population. High yield has always been one of the important goals of rice breeding and cultivation. In the field of rice breeding and cultivation, it is necessary to measure the yield of a large number of candidate samples in different environments, so as to provide a scientific basis for cultivating high-yield, high-quality, and stress-resistant rice varieties. Rice panicle is the organ where rice grains grow, and the traits of panicle are directly related to rice yield. Rice ears also play a very important role in the detection of rice diseases and insect pests, nutritional diagnosis and growth...

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the problem of low segmentation accuracy of the field rice ear segmentation method for different varieties and growth periods in the prior art, the present invention provides a fast field rice ear segmentation method based on deep learning to realize different Segmentation of Multi-variety Rice Ears in Growth Period

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