Research on intelligent clearing of weeds in wheat fields using spectral imaging and machine learning.

The intelligent management of weed clearing can improve wheat yield and reduce the use of pesticides. In this paper, our contribution is to propose a method for wheat and weed recognition based on multispectral imaging and Membrane Search Algorithm Support Vector Machine (MSA-SVM), which is the basis for intelligent weed clearing. A spectral data acquisition system is set up in the laboratory. Then, a total of 700 groups of wheat and weed spectral data are collected. The classical SVM based on spectral data is applied to identify weeds to study the classification performance of wheat and weeds in different bands. The Membrane Search Algorithm (MSA) algorithm proposed by us was used for SVM parameters optimization, and the dimension reduction algorithm was adopted to reduce the interference of redundant information. After local linear embedding (LLE) dimensionality reduction of spectral data, MSA-SVM has the highest average recognition accuracy for weeds and wheat, which is 91.00%. The experimental results show that our research can distinguish wheat from weeds, which is of great significance for the development of smart agriculture. [Display omitted] • Construct a multispectral data collection system for wheat and weeds. • A weed recognition method based on spectral imaging and SVM is proposed. • Dimension reduction of spectral data based on LLE was studied. • The MSA algorithm we proposed is used for SVM parameters optimization. • The provided method can distinguish between wheats and weeds. [ABSTRACT FROM AUTHOR]