Research on weed identification method in rice fields based on UAV remote sensing

Abstract

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 WDVINIR vegetation index from the reflectance of three bands, RE, G, and NIR. WDVINIR was compared with five traditional vegetation indices, NDVI, LCI, NDRE, and OSAVI, and the results showed that WDVINIR was the most effective for weed identification and could clearly distinguish weeds from rice, water cotton, and soil. The weed identification method based on WDVINIR 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.

Local perceptions of the benefits versus negative impacts of weedy grasses in central Madagascar, with a focus on the genus Digitaria

“…This identification method agrees with Le Bourgeois et al (2008) who described the IDentification Assistée par Ordinateur (IDAO) system of providing information on all vegetative morphological characters farmers can observe. Although farmers know how to distinguish some species from others, this technique is less modern than some using autumated image identification or Unmanned Aerial Vehicle multispectral images (Yu et al, 2022; Zhang et al, 2022).…”

“…In our study, we have argued that plant names provide information about their environment or their relationship with the community Although farmers know how to distinguish some species from others, this technique is less modern than some using autumated image identification or Unmanned Aerial Vehicle multispectral images (Yu et al, 2022;Zhang et al, 2022).…”

YOLOv8-RMDA: Lightweight YOLOv8 Network for Early Detection of Small Target Diseases in Tea

“…In recent years, deep learning has made significant strides in various fields, including agriculture, thanks to advancements in data analysis and image-processing technology. Target detection using deep learning has emerged as a key area of research in computer vision, with applications in crop maturity detection [3,4], pest and disease identification [5][6][7][8][9][10], plant phenotyping [11][12][13], and weed management [14,15]. Through the development of sophisticated parallel models, challenges such as scattered data resources, information integration complexities, and inefficient knowledge utilization in agricultural settings have been effectively addressed.…”

RVDR-YOLOv8: A Weed Target Detection Model Based on Improved YOLOv8

“…In [10], a machine vision approach for weed identification using support vector machines was proposed. However, traditional machine learning algorithms are time-consuming and prone to bias in extracting key features [11].…”