Abstract
In recent years, Unmanned Aerial Systems (UAS) have emerged as an innovative technology to provide spatio-temporal information about weed species in crop fields. Such information is a critical input for any site-specific weed management program. A multi-rotor UAS (Phantom 4) equipped with an RGB sensor was used to collect imagery in three bands (Red, Green, and Blue; 0.8 cm/pixel resolution) with the objectives of (a) mapping weeds in cotton and (b) determining the relationship between image-based weed coverage and ground-based weed densities. For weed mapping, three different weed density levels (high, medium, and low) were established for a mix of different weed species, with three replications. To determine weed densities through ground truthing, five quadrats (1 m × 1 m) were laid out in each plot. The aerial imageries were preprocessed and subjected to Hough transformation to delineate cotton rows. Following the separation of inter-row vegetation from crop rows, a multi-level classification coupled with machine learning algorithms were used to distinguish intra-row weeds from cotton. Overall, accuracy levels of 89.16%, 85.83%, and 83.33% and kappa values of 0.84, 0.79, and 0.75 were achieved for detecting weed occurrence in high, medium, and low density plots, respectively. Further, ground-truthing based overall weed density values were fairly correlated (r2 = 0.80) with image-based weed coverage assessments. Among the specific weed species evaluated, Palmer amaranth (Amaranthus palmeri S. Watson) showed the highest correlation (r2 = 0.91) followed by red sprangletop (Leptochloa mucronata Michx) (r2 = 0.88). The results highlight the utility of UAS-borne RGB imagery for weed mapping and density estimation in cotton for precision weed management.
Highlights
Weeds are the major pests of agricultural crops and a serious challenge to sustainable crop production [1,2]
This study demonstrated a methodology for mapping weed infestations in cotton utilizing RGB imagery and non-conventional image analysis techniques
The current study has successfully demonstrated that they can be applied across different levels of weed densities
Summary
Weeds are the major pests of agricultural crops and a serious challenge to sustainable crop production [1,2]. A site-specific approach, which takes into account the spatio-temporal variabilities in weed species establishment and growth, can facilitate effective and economical weed management [3,4]. Images acquired with remote sensing platforms can be analyzed to produce pixel-level or individual-plant-level information regarding the location and distribution of weeds throughout the field [7]. Such information can guide farmers in assessing weed infested areas and developing management grids for site-specific treatment. Castaldi et al [8] analyzed digital imageries to produce coverage maps for weeds in maize (Zea mays L.), which were further transformed into 2 m × 2 m grids. Knowledge of weed distributions and densities across the field is beneficial for tailoring herbicide applications to weed spatial dynamics in the field [9]
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