Multi-kernel Non-convex Optimized Weed Identification Method

Abstract

To improve the recognition rate and stability of weed identification for single features, A support vector machine based multiple kernel ensemble method for weed identification is proposed The three weed features are trained separately to derive a pool of kernel functions., multiple kernel classification results of the three single features are used as independent evidence to construct the basic probability assignment, introduce the complexity of the weed recognition algorithm based on multi-kernel non-convex optimization, and give the final recognition results according to the ensemble results and the classification judgment threshold. The experimental results show that the recognition rate of the multi-kernel non-convex optimized weed identification method reaches 97.57%, which has better stability and higher recognition rate compared with single-feature recognition.