Abstract
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.
Highlights
Weed map can be used as a powerful tool to help understanding the distribution of weeds and monitoringWeeds often grow in aggregated patches of varying the spread of established weeds and the effectiveness of size or in stripes along the field borders and along the control programs
This study introduces and discusses a proximal weed mapping method based on the machine vision technologies
Most of the current weed mapping systems use discrete sampling and interpolation method to make weed maps and the accuracy of the weed maps are quite different depending on the weed sensors, sampling and data processing methods
Summary
Weeds often grow in aggregated patches of varying the spread of established weeds and the effectiveness of size or in stripes along the field borders and along the control programs It has been recognized widely Slaughter et al, 2008). There are wide image stitching methods developed in the recent years and it is necessary to make an introduction of the image stitching technology. The parameters of the mapping functions are computed by means of the established feature correspondence in step 2. (4) Image transformation; the reference image and target image are transformed into a same coordinate by the means of the mapping functions. (5) Image interpolation and image blending; the values of the pixels in the stitched image are estimated by using the value of the pixels in the reference image and target image
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