Cotton stubble detection based on wavelet decomposition and texture features.

ABSTRACT:

Background

At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system.

Methods

Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and

SUBMITTER: Yang Y 

PROVIDER: S-EPMC8561878 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature