A Review on Remote Sensing of Weeds in Agriculture

Abstract

In the effort of developing precision agriculture tools, remote sensing has been commonly considered as an effective technique for weed patch delineation, where weed infestations are detected based on variations in the plant canopy spectral response. Because the canopy spectral response is important for weed detection, discussions on the irradiative interaction of light in plant canopies and the effect of variable soil background on the canopy spectral response are presented in this review. Also, a presentation of the current techniques for removal of soil effects, including vegetation indices and spectral mixture analysis, shows that these techniques have not been adequately developed for use in remote sensing-based weed detection applications. Given the nature of light interaction in a plant canopy, this review proposes that the spectral response of a plant canopy depends on both the species and the biomass density. Remote detection of weeds from ground-, aircraft-, and satellite-based platforms has been accomplished on a wide scale, yet the use of these weed detection methods to make variable-rate herbicide applications has not occurred as often. By judging success based on variable-rate herbicide applications rather than precise weed localization, some of the current problems in weed sensing may be skirted.

Phenotyping Conservation Agriculture Management Effects on Ground and Aerial Remote Sensing Assessments of Maize Hybrids Performance in Zimbabwe

“…Meanwhile, the use of the near-infrared (NIR) region by some spectral indexes, which greatly decreases its reflectance over soil, helps to increase the sensibility to the canopy cover [62]. Despite these appreciations, the RGB-based indexes GA and GGA outperformed NDVI and the rest of spectral indexes at predicting GY under CA conditions.…”

“…The far higher resolution of the RGB compared with the multispectral images may be the critical factor here when working from an aerial platform ( Figure 2) [33,57]. Meanwhile, the use of the near-infrared (NIR) region by some spectral indexes, which greatly decreases its reflectance over soil, helps to increase the sensibility to the canopy cover [62]. Despite these appreciations, the RGB-based indexes GA and GGA outperformed NDVI and the rest of spectral indexes at predicting GY under CA conditions.…”

Nonconventional Weed Management Strategies for Modern Agriculture

“…Photodetectors are actually used along with sensors. These give a very clear picture about weed infestation and density (Thorp and Tian 2004). On the basis of this principle, weed-detection model instruments have been developed.…”

“…Precision weed management is another prospect of modern weed management. The application of remote sensing, modeling, and robotics in a very sophisticated and highly scientific manner (Christensen et al 2009;Freckleton and Stephens 2009;Lamb and Brown 2001;Slaughter et al 2008; Thorp and Tian 2004;Torres-Sanchez et al 2013;Young et al 2014) will enable us to pave/tread excellent paths for a site-specific, efficient, targeted, and economical weed management in the future.…”

Adaptive detection of volunteer potato plants in sugar beet fields

“…Weed detection systems have evolved from large scale remote sensing techniques to high resolution machine vision detection systems (Thorp and Tian 2004). Nevertheless, machine vision based systems for precise weed control at square cm level have hardly been researched besides, for example, a tomato seedling weed detection and removal application by Lee et al (1999) and sugar beet and weed detection by Astrand (2005).…”