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The #OpenSpotSprayer is a low cost, opensource and simple approach to weed detection. It uses off the shelf items and simple colour-based code to pick out green weeds. Here it is mounted on the Digital Farm Hand robot @pbinarrabri. Full code and guide released soon. @SiaSydney
This machine’s algorithm is capable of distinguishing between #BroadleafWeeds, #GrassWeeds, and #ginger with a great level of accuracy and is helping to detect and spray for #weeds in a ginger crop. To read more about this project, visit bit.ly/3o0cmJK
@GeezaColeman & @WeedAI_news are seeking growers to join a beta testing group for feedback user experience. 📷 All you need are the images to upload. DM @GeezaColeman if you're interested in participating …
Great to see them go out to a good home👍🏼 one rig working and another 3 coming
Weed-AI now hosts: 🗄 17 datasets, 20,891 images 🌾 8 crops + fallow 🌱 28 species + poaceae/broadleaf groups 🌏 datasets from: 🇦🇺🇩🇰🇦🇷🇺🇲 If you've got datasets of weeds or need data for a project, check out Weed-AI weed-ai.sydney.edu.au
We’re about to start our OpenWeedLocator live assembly and demonstration with @GeezaColeman at the helm 🎥🎙
weed-ai.sydney.edu.au is an open data platform that the Sydney Informatics Hub is building to enable R&D into computer vision methods to identify and control weeds. It's great to see new data coming into the system!
🚨 New publication 🚨 Michael Walsh & @SBPowles look at the role of harvest weed seed control in Australian grain production systems. What does it mean for weed recognition? Read the full paper here or summary below from @WeedSmartAU @SiaSydney publish.csiro.au/CP/pdf/CP21647
HWSC gurus @SBPowles and Michael Walsh summarised 30 years of harvest weed seed control research and innovation in their latest paper 🚜 Find out more about the impact of HWSC and what improves its efficacy in our science review ➡️ buff.ly/35RG2n3
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