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- W3008369512 abstract "Robotic weed control for vegetables is necessary to increase crop productivity, avoid intensive hand weeding as labour shortages in developed countries such as United States has led to a surge in food production costs. However, development of a reliable, intelligent robotic system for weed control in real-time for vegetables still remains a challenging task. The main issue arises while distinguishing crops from weeds in real-time. In this paper, a novel technique to crop signalling to distinguish crops from in-row weeds in complex natural scenarios, such as high weed densities commonly found on organic farms, in real-time using machine vision is presented. Crop signalling is a simple and low-cost technique in which a signalling compound is produced by or applied to the crop and where the signalling compound is machine readable and helps to create visual features that uniquely distinguish the crops from weeds. The crop and weed mapping algorithm presented here were specially designed and developed for a vision-based weeding robot equipped with a micro-jet herbicide-spraying system for weed control in a lettuce field. The proposed technique involves weed/crop mapping and decision making. Experimental results show that the crop detection accuracy was 99.75%, and 98.11% of sprayable weeds were detected. The proposed technique is highly accurate, reliable and more robust than other sensor-based techniques presented in the literature." @default.
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- W3008369512 date "2020-04-01" @default.
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- W3008369512 title "Real-time weed-crop classification and localisation technique for robotic weed control in lettuce" @default.
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- W3008369512 doi "https://doi.org/10.1016/j.biosystemseng.2020.02.002" @default.
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