Unlock instant, AI-driven research and patent intelligence for your innovation.

Farmland crop identification method based on fusion of semantic segmentation and superpixel segmentation

A technology of superpixel segmentation and semantic segmentation, which is applied in the field of crop recognition in farmland, can solve the problems of poor edge segmentation, low accuracy of type recognition, and inapplicable scenes with complex types of crops, so as to improve the overall recognition accuracy, Strengthen interdependence and improve the effect of inaccurate parcel edge segmentation

Pending Publication Date: 2022-02-18
EAST CHINA NORMAL UNIV
View PDF0 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is to provide a farmland crop recognition method based on the fusion of semantic segmentation and superpixel segmentation, aiming to solve the problem of low recognition accuracy and poor edge segmentation effect of existing algorithms for crop type recognition based on deep learning farmland scene images. Good and technical issues that don't apply to scenes with complex crop types

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Farmland crop identification method based on fusion of semantic segmentation and superpixel segmentation
  • Farmland crop identification method based on fusion of semantic segmentation and superpixel segmentation
  • Farmland crop identification method based on fusion of semantic segmentation and superpixel segmentation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] A crop recognition method for RGB farmland images based on the fusion of semantic segmentation and superpixel segmentation, taking the iCrop farmland image dataset as an example, the implementation process is as follows figure 1 As shown, the specific implementation steps are as follows:

[0045] Step 1. Image preprocessing: Screen the iCrop farmland image dataset, remove unusable images such as duplication, blur, and occlusion, resize all images to 512×512, and use the image annotation tool Labelme to filter the filtered farmland images. The identified five farmland crop types (corn, rice, wheat, rapeseed, and bare land) are marked, and the crop types that do not need to be identified are uniformly marked as "others". The ratio is divided into training set, validation set and test set;

[0046] Step 2, semantic segmentation model training: use the training set and verification set obtained in step 1 to train the semantic segmentation model, and obtain the optimal sema...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a farmland crop identification method based on semantic segmentation and superpixel segmentation fusion, and belongs to the technical field of image processing and application. The objective of the invention is to solve the problems of low recognition accuracy, incapability of providing pixel-level crop classification, inaccurate land parcel edge segmentation and the like of an existing algorithm for performing crop type recognition on a complex farmland scene image. According to the method, a semantic segmentation model of texture feature enhancement and multilayer attention fusion is introduced to perform semantic segmentation on main crops in a farmland image, and a Threshold Voting algorithm is used to fuse superpixel segmentation and semantic segmentation to obtain a farmland image crop type identification result. By using the farmland crop identification method based on semantic segmentation and superpixel segmentation fusion provided by the invention, efficient and accurate crop type identification can be carried out on the RGB farmland image.

Description

technical field [0001] The invention belongs to the technical field of image processing and application, and in particular relates to a field crop recognition method based on the fusion of semantic segmentation and superpixel segmentation. Background technique [0002] The current mainstream way to obtain agricultural data is to use remote sensing satellites to monitor agricultural conditions using remote sensing images, which has the characteristics of wide range, high dynamics and fast speed. However, factors such as complex and diverse cultivated land terrain, scattered cultivated land distribution, and complex crop types have resulted in the inability of satellite remote sensing images to provide very refined agricultural images. In order to achieve precision agriculture, more refined agricultural data are needed as a supplement to satellite remote sensing images. To ensure that every inch of arable land can be rationally utilized. With the rapid development of technolo...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06V20/13G06V10/26G06V10/80G06V10/82G06K9/62G06N3/04
CPCG06N3/045G06F18/253
Inventor 杨超华胡星波
Owner EAST CHINA NORMAL UNIV