Abstract
The use of a fallow phase is an important tool for maximizing crop yield potential in moisture limited agricultural environments, with a focus on removing weeds to optimize fallow efficiency. Repeated whole field herbicide treatments to control low-density weed populations is expensive and wasteful. Site-specific herbicide applications to low-density fallow weed populations is currently facilitated by proprietary, sensor-based spray booms. The use of image analysis for fallow weed detection is an opportunity to develop a system with potential for in-crop weed recognition. Here we present OpenWeedLocator (OWL), an open-source, low-cost and image-based device for fallow weed detection that improves accessibility to this technology for the weed control community. A comprehensive GitHub repository was developed, promoting community engagement with site-specific weed control methods. Validation of OWL as a low-cost tool was achieved using four, existing colour-based algorithms over seven fallow fields in New South Wales, Australia. The four algorithms were similarly effective in detecting weeds with average precision of 79% and recall of 52%. In individual transects up to 92% precision and 74% recall indicate the performance potential of OWL in fallow fields. OWL represents an opportunity to redefine the approach to weed detection by enabling community-driven technology development in agriculture.
Highlights
Weeds presentWeed densityCanola stubble, red–orange soil annual sowthistle (Sonchus oleraceus), volunteer canola (Brassica napus), annual3.1 ryegrass (Lolium rigidum), volunteer faba bean (Vicia faba) Volunteer wheatHeavy wheat stubble, red soil (Triticum aestivum), annual sowthistle, annual Volunteer nar-Lupin stubble, red– orange soil rowleaf lupins (Lupinus angustifolius), annualGrazed barley stubbleVolunteer barley (Hordeum vulgare), 3.3 annual sowthistleDark brown soil, freshly tilled, no soil cover
Technological advancements to the Raspberry Pi system in the years since, including more powerful processors, increased memory, and networking capabilities, have allowed for increasingly complex projects to be developed using the system. This has led to a large online community of “makers”, who have found a multitude of uses for the Raspberry Pi, ranging from environmental monitoring[37] to cloud computing infrastructure[38], and who, with the help of platforms such as GitHub and StackOverflow, can provide support for hardware and software related issues and improve the development process[39]
Using excess green (ExG) instead of normalized ExG (NExG) in the composite ExG and HSV (ExHSV) algorithm may be advantageous based on the results presented here, others have found non-normalized RGB chromatic coordinates to be highly variable[18], resulting in poorer performance
Summary
Weeds presentWeed density (plants m−2)Canola stubble, red–orange soil annual sowthistle (Sonchus oleraceus), volunteer canola (Brassica napus), annual3.1 ryegrass (Lolium rigidum), volunteer faba bean (Vicia faba) Volunteer wheatHeavy wheat stubble, red soil (Triticum aestivum), annual sowthistle, annual Volunteer nar-Lupin stubble, red– orange soil rowleaf lupins (Lupinus angustifolius), annualGrazed barley stubbleVolunteer barley (Hordeum vulgare), 3.3 annual sowthistleDark brown soil, freshly tilled, no soil cover. Canola stubble, red–orange soil annual sowthistle (Sonchus oleraceus), volunteer canola (Brassica napus), annual. 3.1 ryegrass (Lolium rigidum), volunteer faba bean (Vicia faba) Volunteer wheat. Heavy wheat stubble, red soil (Triticum aestivum), annual sowthistle, annual Volunteer nar-. Lupin stubble, red– orange soil rowleaf lupins (Lupinus angustifolius), annual. Volunteer barley (Hordeum vulgare), 3.3 annual sowthistle.
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