Robust Methods for Measurement of Leaf-Cover Area and Biomass from Image Data

Abstract

Leaf-cover area is a widely required plant development parameter for predictive models of weed growth and competition. Its assessment is performed either manually, which is labor intensive, or via visual inspection, which provides biased results. In contrast, digital image processing enables a high level of automation, thereby offering an attractive means for estimating vegetative leaf-cover area. Nonetheless, image-driven analysis is greatly affected by illumination conditions and camera position at the time of imaging and therefore may introduce bias into the analysis. Addressing both of these factors, this paper proposes an image-based model for leaf-cover area and biomass measurements. The proposed model transforms color images into an illumination-invariant representation, thus facilitating accurate leaf-cover detection under varying light conditions. To eliminate the need for fixed camera position, images are transformed into an object–space reference frame, enabling measurement in absolute metric units. Application of the proposed model shows stability in leaf-cover detection and measurement irrespective of camera position and external illumination conditions. When tested on purple nutsedge, one of the world's most troublesome weeds, a linear relation between measured leaf-cover area and plant biomass was obtained regardless of plant developmental stage. Data on the expansion of purple nutsedge leaf-cover area is essential for modeling its spatial growth. The proposed model offers the possibility of acquiring reliable and accurate biological data from digital images without extensive photogrammetric or image-processing expertise.

Effect of planter and tractor wheels on row and inter-row weed populations

“…Our image-based weed cover evaluations were highly correlated with aboveground weed biomass (Pearson's coefficient ¼ 0.8296), indicating these images were representative of potential weed competition, as suggested by multiple studies (Ngouajio et al, 1999;Lemieux et al, 2003;Lati et al, 2011). This type of unbiased weed cover evaluation (compared to usual visual estimates) also demonstrates that potential herbicide savings are high if weeds could be sprayed at high resolution (average weed cover values never exceeded 25%) (Fig.…”

Hyperspectral Technologies for Assessing Seed Germination and Trifloxysulfuron-methyl Response in Amaranthus palmeri (Palmer Amaranth)

“…In weed science, hyperspectral imaging has previously been used for site-specific weed management, particularly for weed crop classification (López-Granados, 2011; Herrmann et al, 2013; Shapira et al, 2013) or as a part of decision support system for herbicide application (Tellaeche et al, 2008; Lati et al, 2011). We could not find any reference to the use of this technology for early detection of herbicide response in young weeds (phenological stage of 3–4 true leaves).…”

Targeted and Microdose Chemical Applications

“…Precision application of weed control treatments requires sensitive technology that can track, record, and compute information on leaf shape, color, surface, and edge features for separating a weed and a crop plant (Hearn 2009;Meyer et al 1998;Lati et al 2011;Slaughter et al 2008b;Tang et al 2003;Tellaeche et al 2011). The technology is still emerging and has a few challenges, including oc-cluded leaves, misshapen leaves, moving leaves, and dusty leaves (see Chapter 15).…”