Summary Site‐specific weed control technologies are defined as machinery or equipment embedded with technologies that detect weeds growing in a crop and, taking into account predefined factors such as economics, take action to maximise the chances of successfully controlling them. In this study, we describe the basic parts of site‐specific weed control technologies, comprising weed sensing systems, weed management models and precision weed control implements. A review of state‐of‐the‐art technologies shows that several weed sensing systems and precision implements have been developed over the last two decades, although barriers prevent their breakthrough. Most important among these is the lack of a truly robust weed recognition method, owing to mutual shading among plants and limitations in the capacity of highly accurate spraying and weeding apparatus. Another barrier is the lack of knowledge about the economic and environmental potential for increasing the resolution of weed control. The integration of site‐specific information on weed distribution, weed species composition and density and the effect on crop yield, is decisive for successful site‐specific weed management.

SHORT TITLE: Assessment of crop soil cover SUMMARYObjective assessment of crop soil cover, defined as the percentage of leaf cover that has been buried in soil due to weed harrowing, is crucial to further progress in post-emergence weed harrowing research. Up to now, crop soil cover has been assessed by visual scores, which are biased and context dependent. The aim of this study was to investigate whether digital image analysis is a feasible method to estimate crop soil cover in the early growth stages of cereals. Two main questions were examined: (1) how to capture suitable digital images under field conditions with a standard high-resolution digital camera and (2) how to analyse the images with an automated digital image analysis procedure. The importance of light conditions, camera angle, size of recorded area, growth stage and direction of harrowing were investigated in order to establish a standard for image capture and an automated image analysis procedure based on the excess green colour index was developed. The study shows that the automated digital image analysis procedure provided reliable estimations of leaf cover, defined as the as the proportion of pixels in digital images determined to be green, which were used to estimate crop soil cover. A standard for image capture is suggested and it is recommended to use digital image analysis to estimated crop soil cover in future research.The prospects of using digital image analysis in future weed harrowing research are discussed.Archived at http://orgprints.org/11007 2

No abstract

No abstract

An automatic tillage system for inter-and intra-row weed control based on real-time kinematic GPS navigation and control has been used to address the problem of mechanically removing weeds within rows of precision seeded crops. The system comprised a side-shifting frame with an attached tine-rotor (cycloid hoe) with eight sigmoidshaped, vertically directed tines. The individual tines can be released for individual rotation in order to avoid collision with geo-referenced crop plants. The system navigated with reference to pre-defined waypoints for tillage parallel to crop rows and around individual crop plants. The system evaluation was based on quantification of treated areas for uprooting and burial and the corresponding prediction of weed control efficiencies. A single pass of an 80 mm wide row band provided tillage of 30-49% of the intra-row area, with highest coverage at a speed of 0.32 m s -1 and at even plant spacing. A double pass, once on each side of the row in opposite directions, provided higher soil disturbance intensity and resulted in tillage of 31-58% of the intra-row area with highest coverage at a speed of 0.32 m s -1 . The intra-row weed control effect was predicted to be up to 20% for a single pass and up to 29% for a 2-way pass treatment both at the white thread to the twoleaf stage of weeds. The result of the prediction is of crucial importance for the considerations of tool designs at the current conceptual stage of the system.