Development of a Proximal Machine Vision System for off-Season Weed Mapping in Broadacre no-tillage Fallows

Weeds are among the most significant and costly environmental threats in Australian agriculture. Weeds compete with crop plants for moisture, nutrients an d sunlight and can have a detrimental impact on cro p yields and quality if uncontrolled. The distributio n, size, density and species of the weeds are often heterogeneous in the cropping land. Instead of unif ormly spray the same type of herbicide to the whole farm land, selective spray can reduce the herbicide usag e therefore can reduce the serious problems of herb icide resistance, soil damage and food safety. This study describes a weed mapping method which could be used for broadacre no-tillage fallow weed management. The weed maps have the potential to be used as powerf ul herbicide prescription maps for spot spray. The wee d mapping is realized by the machine vision technologies which including image acquisition, ima ge stitching and photomosaic processing. The sampli ng points are continuous and the interpolation methods are used at the minimum levels. The experiment res ult shows that this weed mapping method can map weed under limited conditions.

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