A method for identifying weeds in paddy field based on multi-feature fusion and BP neural network and its application

Abstract

The invention relates to a paddy field weed identification method based on multi-feature fusion and BP neural network. Firstly, multi-species features of paddy field weed are extracted based on imageprocessing technology. Then these features are fused as the input data of BP neural network for training and detection. Finally, the trained BP neural network classifier is used to identify the new weeds. Many kinds of features include color, shape and texture. BP neural network is regarded as a nonlinear mapping from the input weed characteristic parameters to the output corresponding weed class.The invention also relates to an application of a paddy field weed identification method based on multi-feature fusion and BP neural network. The invention can accurately identify the position, the density and the species of the weeds in the paddy field. The invention can accurately and quickly identify weeds in complex paddy field environment, solves the problems of slow speed and low accuracy of weed identification in paddy field at present, and belongs to the field of intelligent identification of agricultural machinery.

Description

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AI Technical Summary

Benefits of technology

This patented technology helps farmers improve their ability to control unwanted plants by identifying them with precision at different locations within an area without being affected negatively on its overall performance. It also uses advanced techniques like image processing algorithms that are faster than traditional methods but still accurate enough to detect these areas effectively. Overall, this technology allows us better ways to manage crops more efficiently while reducing environmental concerns associated therewith.

Problems solved by technology

This patented describes an algorithm called Multi FactorFusion (MFA) which uses multiple techniques like image processing technology, computer vision analysis, artificial intelligence, data mineralization, genetic algorithms, deep learning, etc., to improve our understanding about how plants grow and reproduce within their roots. MFA helps farmers detect unwanted vegetation without damaging them during harvest season.

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