关键词: 杂草识别, 深度学习, 卷积神经网络, 目标检测, 语义分割

Abstract: Associated weeds not only compete with crops for nutrients and water, but also are the intermediate hosts of a variety of diseases and pests, which has become a difficult problem perplexing the efficient production of crops. With the development of deep learning technology, the automatic detection and classification recognition of weeds have been importantly applied in the process of weed removal. Firstly, this paper expounded the hardware requirements and software implementation process of deep learning applied in the process of weed recognition, analyzed the advantages and disadvantages of different hardware used for deep learning, and expounded the establishment, training basic steps such as model evaluation and model deployment. The research progress of deep learning method in weed and crop recognition and weed classification recognition was discussed. Then it was pointed out that there was a large demand for deep learning data and there was no universal data set at present. And low recognition accuracy was caused by weeds and crops blocking each other, complex lighting environment, and poor machine operation conditions. Finally, it was pointed out that the research on image and spectral data fusion, modularization of weed recognition model, weed growth prediction and embedded model deployment would become the future research direction of weed recognition method based on deep learning.

Key words: weed recognition, deep learning, convolutional neural network, target detection, semantic segmentation