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International audienceAbstractSorghum halepense (johnsongrass) is a perennial weed with a vegetative reproductive system and one of the most competitive weeds in maize showing a spatial distribution in compact patches. When maize is irrigated, successive weed emergences occur in the early phenological phases of the crop, which require several herbicide applications. Our aim was to provide an accurate tool for an early detection and mapping of johnsongrass patches and delineate the actual surface area requiring a site-specific herbicide treatment based on the weed coverage. This early detection represents a major challenge in actual field scenarios because both species are in the Poaceae family, and show analogous spectral patterns, an extraordinarily similar appearance and a parallel phenological evolution. To solve this, an automatic OBIA (object-based-image-analysis) procedure was developed to be applied on orthomosaicked images using visible (red-green-blue bands) and multispectral (red-green-blue and near infrared bands) cameras collected by an unmanned aerial vehicle (UAV) that flew at altitudes of 30, 60 and 100 m on two maize fields. One of our first phases was the generation of accurate orthomosaicked images of an herbaceous crop such as maize, which presented a repetitive pattern and nearly no invariant parameters to conduct the aerotriangulation. Here, we show that high-quality orthomosaicks were produced from both cameras and that they were able to be the first step for mapping the johnsongrass patches. The most accurate weed maps were obtained using the multispectral camera at an altitude of 30 m in both fields. These maps were then used to design a site-specific weed management program, and we demonstrated that potential herbicide savings ranged from 85 to 96 %. Our results showed that accurate and timely maps of johnsongrass patches in maize can be a key element in achieving site-specific and sustainable herbicide applications for reducing spraying herbicides and costs

Object-based early monitoring of a grass weed in a grass crop using high resolution UAV imagery