Early season weed mapping in sunflower using UAV technology: variability of herbicide treatment maps against weed thresholds

• Published in: Precision agriculture 2016-04, Vol.17 (2), p.183-199
• Main Authors: López-Granados, Francisca, Torres-Sánchez, Jorge, Serrano-Pérez, Angélica, de Castro, Ana I., Mesas-Carrascosa, Fco.-Javier, Peña, José-Manuel
• Format: Article
• Language: English
• Subjects: aerial photography; Agriculture; Algorithms; Altitude; Atmospheric Sciences; Biomedical and Life Sciences; Cameras; Chemistry and Earth Sciences; Computer Science; Crops; Flowers & plants; georeferencing; Helianthus annuus; Herbicides; image analysis; Life Sciences; Mapping; pesticide application; Physics; Remote sensing; Remote Sensing/Photogrammetry; Seedlings; Sensors; Soil Science & Conservation; Statistics for Engineering; Studies; Unmanned aerial vehicles; Weed control; Weeds
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-015-9415-8

Site-specific weed management is defined as the application of customised control treatments only where weeds are located within the crop-field by using adequate herbicide according to weed emergence. The aim of the study was to generate georeferenced weed seedling infestation maps in two sunflower fields by analysing overlapping aerial images of the visible and near-infrared spectrum (using visible or multi-spectral cameras) collected by an unmanned aerial vehicle (UAV) flying at 30 and 60 m altitudes. The main tasks focused on the configuration and evaluation of the UAV and its sensors for image acquisition and ortho-mosaicking, as well as the development of an automatic and robust image analysis procedure for weed seedling mapping used to design a site-specific weed management program. The control strategy was based on seven weed thresholds with 2.5 steps of increasing ratio from 0 % (herbicide must be applied just when there is presence or absence of weed) to 15 % (herbicide applied when weed coverage >15 %). As a first step of the imagery analysis, sunflower rows were correctly matched to the ortho-mosaicked imagery, which allowed accurate image analysis using object-based image analysis [object-based-image-analysis (OBIA) methods]. The OBIA algorithm developed for weed seedling mapping with ortho-mosaicked imagery successfully classified the sunflower-rows with 100 % accuracy in both fields for all flight altitudes and camera types, indicating the computational and analytical robustness of OBIA. Regarding weed discrimination, high accuracies were observed using the multi-spectral camera at any flight altitude, with the highest (approximately 100 %) being those recorded for the 15 % weed threshold, although satisfactory results from 2.5 to 5 % thresholds were also observed, with accuracies higher than 85 % for both field 1 and field 2. The lowest accuracies (ranging from 50 to 60 %) were achieved with the visible camera at all flight altitudes and 0 % weed threshold. Herbicide savings were relevant in both fields, although they were higher in field 2 due to less weed infestation. These herbicide savings varied according to the different scenarios studied. For example, in field 2 and at 30 m flight altitude and using the multi-spectral camera, a range of 23–3 % of the field (i.e., 77 and 97 % of area) could be treated for 0–15 % weed thresholds. The OBIA procedure computed multiple data which permitted calculation of herbicide requir