Fenghua Yu, Zhongyu Jin, Sien Guo, Zhonghui Guo, Honggang Zhang, Tongyu Xu, Chunling Chen
Rice is the world's most important food crop and is of great importance to ensure world food security. In the Rice cultivation process, Weeds are a key factor that affects Rice production. Weeds in the field compete with Rice for sunlight, water, nutrients, and other resources, thus affecting the quality and yield of Rice. The chemical treatment of Weeds in Rice fields using herbicides suffers from the problem of sloppy herbicide application methods. In most cases, farmers do not consider the distribution of Weeds in paddy fields, but use uniform doses for uniform spraying of the whole field. Excessive use of herbicides not only pollutes the environment and causes soil and water pollution, but also leaves residues of herbicides on the crop, affecting the quality of Rice. In this study, we created a weed identification index based on UAV multispectral images and constructed the vegetation index from the reflectance of three bands, RE, G, and NIR. was compared with five traditional vegetation indices, NDVI, LCI, NDRE, and OSAVI, and the results showed that was the most effective for weed identification and could clearly distinguish Weeds from Rice, water cotton, and soil. The weed identification method based on was constructed, and the weed index identification results were subjected to small patch removal and clustering processing operations to produce weed identification vector results. The results of the weed identification vector were verified using the confusion matrix accuracy verification method and the results showed that the weed identification accuracy could reach 93.47%, and the Kappa coefficient was 0.859. This study provides a new method for weed identification in Rice fields.
