Litcius/Paper detail

High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding

Lydia Elstone, Kin Yau How, Samuel Brodie, Muhammad Zulfahmi Ghazali, William P. Heath, Bruce Grieve

2020Sensors55 citationsDOIOpen Access PDF

Abstract

Precision weeding can significantly reduce or even eliminate the use of herbicides in farming. To achieve high-precision, individual targeting of weeds, high-speed, low-cost plant identification is essential. Our system using the red, green, and near-infrared reflectance, combined with a size differentiation method, is used to identify crops and weeds in lettuce fields. Illumination is provided by LED arrays at 525, 650, and 850 nm, and images are captured in a single-shot using a modified RGB camera. A kinematic stereo method is utilised to compensate for parallax error in images and provide accurate location data of plants. The system was verified in field trials across three lettuce fields at varying growth stages from 0.5 to 10 km/h. In-field results showed weed and crop identification rates of 56% and 69%, respectively. Post-trial processing resulted in average weed and crop identifications of 81% and 88%, respectively.

Topics & Concepts

WeedPrecision agricultureCropParallaxIdentification (biology)Weed controlRGB color modelAgricultural engineeringAgronomyMathematicsArtificial intelligenceComputer visionComputer scienceAgricultureBiologyEngineeringBotanyEcologySmart Agriculture and AIGreenhouse Technology and Climate ControlElectrowetting and Microfluidic Technologies
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